Consolidated AI training catalogue

We routinely attribute mentality to different types of cognitive systems, including humans, animals, and even robots. However, it is not obvious, to say the least, how a biological or computational system might be producing the phenomena we describe using mental vocabulary. This gives rise to a whole plethora of philosophical questions, including the following ones: What do we mean by a 'mind'? How (if at all) do minds interact with physical substrates? How does the mind relate to the body? What are the conditions under which a mind can be attributed to a creature? Are biological organisms the only ones that can produce mental states? How is the mind related to behavior? In this course, we will explore these and related questions drawing on classical and contemporary texts from the analytical tradition. We plan to discuss classical and modern views about the mind-body relation, folk psychology, and the possibility of artificial minds. A selection of scholarly articles and book chapters which will be made available on Moodle.

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The topics of the course are as follows: - Semantics vs. proofs- Propositional logic: Syntax and semantics- First-order logic: Syntax and semantics- Proof calculi, soundness and completeness- Modelling natural language in formal logicThe exact lecture plan is as provided on the Moodle system and updated continuously.

The course covers the following topics: 1. Fundamental Issues 1.1. Overview of AI problems, examples of successful recent AI applications. 1.2. What is intelligent behavior? The Turing test, Rational versus non-rational reasoning. 1.3. Problem characteristics: Fully versus partially observable, Single versus multi-agent, Deterministic versus stochastic, Static versus dynamic, Discrete versus continuous. 1.4. Nature of agents: Autonomous versus semi-autonomous, Reflexive, goal-based, and utility-based, the importance of perception and environmental interactions. 1.5. Philosophical and ethical issues. 2. Basic Search Strategies 2.1. Problem spaces (states, goals and operators), problem solving by search. 2.2. Factored representation (factoring state into variables). 2.3. Uninformed search (breadth-first, depth-first, depth-first with iterative deepening). 2.4. Heuristics and informed search (hill-climbing, generic best-first, A*). 2.5. Space and time efficiency of search. 2.6. Two-player games (introduction to minimax search). 2.7. Constraint satisfaction (backtracking and local search methods. 3. Basic Machine Learning 3.1. Definition and examples of broad variety of machine learning tasks, including classification. 3.2. Inductive learning. 3.3. Simple statistical-based learning, such as Naive Bayesian Classifier, decision trees. 3.4. The over-fitting problem. 3.5. Measuring classifier accuracy.

Part I: Logical Foundations. Propositional Logic, First-Order Logic, Modal Logic Part II: Modelling Agents. Belief and Preference States, Belief Dynamics, Action Logics, Multi-Agent Systems Part III: Non-Monotonic Reasoning. Classical Approaches, Valuation-Based Approaches, Formal Argumentation Part IV: Probabilistic Reasoning. Probabilistic Logics, Inductive Probabilistic Inference, Causal Reasoning Part V: Practical Knowledge Representation. Applications to Knowledge Representation

Lectures: - rules and regulations - standard deontic logic - input/output logic - project

The course has 4 parts: 1. Modal logics for agent reasoning, 2. Conditional logic, 3. Natural language semantics & non-monotonic logic, 4. Formal argumentation

This course aims to provide students with guidelines and methodologies to identify epistemic and ethical issues present in data science. We expect students to develop a critical eye that helps them spot and mitigate such problems in their daily work as data scientists.As much as data science involves automating tasks, we should avoid falling for the automatization of mental processes we undergo to solve new problems. Data scientists are used to dealing with data as much as they do with methods used to process such data. Still, rarely, if ever, do we stop to question methodologies that justify employing one method or another.Surprisingly for many, data science is as much about science as it is about data. We have refined the data acquisition tools and technology stacks used for accomplishing all tasks covered under the data science umbrella. However, below the layers of tools and frameworks that facilitate our daily work, there lies a foundational ground of assumptions, principles, and constraints that shape the way we do data science.During this course, students will learn by example different layers of the scientific method and how they relate to data science and data ethics. Namely, they will learn how mechanisms behind the data affect data analysis, and how different types of scientific inference condition the application of data science solutions and conclusions to other contexts.In this sense, examples of statistical abuse, misconduct, and bad visualization will be shown together with their, sometimes catastrophic, collateral consequences.

Part A: Eigenvectors and eigenvalues, adjacency matrix, hypergraphs and bipartite networks, trees and planar networks, degrees, paths and components, graph Laplacian. Part B: Introduction to quantum networking, scalar product, unitary matrices, Hadamard transformation, 2-qubits, entanglements, manipulation of qubits, introduction to gates, example 2-qubit gates (CNOT, Pauly, swap), no-cloning theorem, Bell states and Bell measurements, quantum repeater and entanglement swapping, fundamental experiments like Mach-Zehnder interferometer.

Part A: Eigenvectors and eigenvalues, adjacency matrix, hypergraphs and bipartite networks, trees and planar networks, degrees, paths and components, graph Laplacian. Part B: Introduction to quantum networking, scalar product, unitary matrices, Hadamard transformation, 2-qubits, entanglements, manipulation of qubits, introduction to gates, example 2-qubit gates (CNOT, Pauly, swap), no-cloning theorem, Bell states and Bell measurements, quantum repeater and entanglement swapping, fundamental experiments like Mach-Zehnder interferometer.

Introduction to Aspects of Graph Theory (Networks and Graphs, Adjacency Matrix, Bipartite Networks, Weighted Networks, Paths and Distances, Connectedness, Cluster Coefficients) Introduction to Quantum Networks (Dirac Notation for Qubits, Definition of Entanglement, Bell States, Quantum Repeaters, Entanglement Swapping, Experimental View using the Mach-Zehnder Interferometer) Introduction to End-to-End Communication (synchronous, asynchronous, multiplex in Space, Time, Frequency, and Code) Error Detection and Correction, Probability Representation of Errors Automatic Repeat Request (ARQ) (Stop-and-Wait, Go-Back-N, Selective Repeat) Multiple Access Protocols Internet (TCP, IPv6, Routing, Distributed Architectures and Protocols, Internet-of-Things)

The course aims to provide knowledge of two complex statistical models: cluster analysis (cluster analysis, typological analysis) and multidimensional scaling (EMD). Psychological data-based examples serve as the basis for studying these models. By the end of the course, students should be able to explain, at least from a conceptual perspective, the different steps involved in these statistical analyses. Selected Bibliography Everitt, B., Landau, S., Leese, M., & Stahl, D. (2011). Cluster Analysis. Chichester: Wiley. Coxon, A.P.M. (1982). The user's guide to multidimensional scaling. London: Heinemann. Tournois, J. & Dickes, P. (1993). La pratique de l'échelonnement multidimensionnel. Bruxelles: De Boeck.

First, we will introduce mathematical problems occurring in finance, mainly in option pricing and portfolio optimization in financial markets following stochastic models. These formulations call upon partial differential equations, conditional expectation, optimal time, dynamic programming and control. Next, we shall describe methods to approach numerical simulations of these objects. Finally, in practical sessions, we will use recent software and Python programming to illustrate efficiency of these methods.The course will be organized in three parts: PDE methods for option pricing and numerical methods in stochastic control: 15h (A. Sulem)Monte Carlo methods (tree method, regression, machine learning): 8h (Ludovic Goudenège)Applied sessions with computer using the computational finance software “Premia” (www.premia.fr) and Python programming: 7h (Ludovic Goudenège)

The course provides an introduction into analytical methods for supply chain management, such as statistical data analysis, time series analysis, supervised machine learning, as well as numerical methods for constrained and unconstrained optimization. Students will get a hands-on experience with widely used Python libraries, such as Pandas, NumPy, SciPy, Scikit-Learn, as well as solver libraries for optimization.

Module 1: Fundamentals and main negotiation concepts, analytics (game theory); BATNA, walkaway, ZOPA, negotiation management Module 2: (1st Simulated negotiation) Module 3: Factors and context in negotiation, the content of negotiation, barriers to value creation, bargaining styles Module 4: Advanced negotiation analysis, weighting trade-offs, tactics and strategies, (2nd simulated negotiation) Module 5: (2nd Simulated negotiation) Module 6: Emotional self-assessment, the negotiator mindset, Bias and irrational negotiations, cultural issues, ethics in negotiations Module 7: Negotiating with Artificial Intelligence and Online Negotiations. Negotiation Capability at organisational level. How to deal with negotiation conducted via emails, Web conferencing platform, AI chatbot. (3rd simulated negotiation with an AI agent)

The course aims to cover the following topics: pricing strategies, markdown pricing and dynamic pricing, single resource and network revenue management, overbooking, bid pricing, choice modeling, assortment optimization, as well as other aspects of pricing (legal, behavioral) and new approaches to pricing (such as machine learning).

Statistical methods for integrative omics data analysis Data processing, filtering and quality control approaches Retrieving data from public biomedical databases Cellular pathway analysis of omics data Molecular network analysis of omics data Machine learning analysis of omics data Biomedical literature / text mining analysis Visualization of high-dimensional biological data Preparing a short research proposal in a team

Introduction to Basic Learning and Research Tools: a. Moodle b. Complete Anatomy c. ClinicalKey Student, UpToDate & A-Z d. Mahara 2. Introduction to Health Data and Computing: a. Basic Concepts of Computerized Data b. DIKW Pyramid c. Data Life Cycle d. Importance of Computing in Healthcare 3. Healthcare Information Systems (HIS): a. Functionalities and Use of HIS b. Electronic Medical Records (EMRs) c. Electronic Prescription Systems d. Different Systems: DSP, Hospital Record, City Doctor, Health Services... 4. Analysis and Interpretation of Health Data: a. Data Analysis Techniques b. Using Data for Research and Improving Patient Care 5. Computer Tools for Clinical Decision Making: a. Clinical Decision Support Software b. Mobile Applications for Nursing Care 6. Rules of Confidentiality and Data Protection (GDPR) 7. Informatique Security Practices: a. CI Triad b. Common Threats: Risk and Impact c. Encryption d. Authentication e. VPN & Firewall 8. Integrating Computing Skills into Clinical Practice: a. Using Technology in Patient Care b. Managing Records and Health Information c. Accompanying Reflective Nursing with EVB 9. Discussions and Debates on Ethical Issues Related to Healthcare Computing 10. Miscellaneous Topics: a. AI Elements (Types of AI, Black Box) b. IoT and DMD c. Digital Health d. Surgical Robotics

Artificial Intelligence (AI) Landscape and the Position of Deep Learning Within Ito Introduction to AI Historical Evolution of AI Types of AI: Narrow, General, Superintelligent Overview of Machine Learning Deep Learning: Definition and Importance Key Deep Learning Architectures Applied Overview of Some Accessible Artificial Intelligences Introduction to AI Applications Text and Language Processing AI Image and Video Processing AI Speech and Audio Processing AI Practical Experimentation with AI Tools Key Concepts in AI Algorithms and Models Training, Validation and Testing Data Supervised, Unsupervised, and Reinforcement Learning Neural Networks Basics AI in Language and Communication Natural Language Processing (NLP) Fundamentals Machine Translation Techniques Chatbots and Conversational AI Prompt engineering techniques AI in Multilingual Contexts Challenges in Multilingual NLP using AI Techniques in Machine Translation Cross-Lingual Transfer Learning Tools and Platforms for Multilingual AI Examples of Multilingual AI Applications AI in Multicultural Contexts Understanding Cultural Biases in AI AI Applications for Cross-Cultural Communication Cultural Nuances in AI Models Developing Culturally-Aware AI Ethical and Legal Dimensions of AI Ethical Considerations in AI Development Addressing Bias and Fairness in AI Legal Implications of AI Usage Data Privacy and Security Regulatory Frameworks and Guidelines Practice in Groups Through Mini-Project of a Persona-Based Chatbot Introduction to the Mini-Project Defining the Persona and Scope Selecting Appropriate Tools and Technologies Development Phases: Design, Build, Test Evaluation and Presentation of the Chatbot Project

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Terminology - Digital Health Literacy Clinical Decision Support Data - Information Electronic Health records - Health Information Systems Clinical Research requirements in Digital Medicine Ethics (CNER) Medical Device Regulation (technology/hardware/software) Usability and Social Acceptance (Patient-centered view) Digital Medical Devices Telemedicine Regulation and Quality Digital supported Care (Integrated Care Management) PROMS/PREMS/Digital Biomarker Precision Health Big Data - Machine Learning + AI

Throughout the course, we will explore the key components of autonomous vehicles, including connectivity, localization, perception, planning, and control. More details on the specific topics covered in class are provided in the syllabus on Moodle.

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Ideas, innovation, and technical inventions have become the most important resource in today's developed economies. Innovation is an important driver of growth and wealth of nations. From the firms' perspective, innovation has become widely recognized as a key source of competitive advantage for businesses of all sizes. Innovation is, however, uncertain, costly, and the returns are difficult to appropriate. This course provides students with a comprehensive understanding of the main theoretical and empirical concepts of the economic analysis of innovation.

Understanding risk and analyzing data to drive policy and decision making is the name of the game in institutions like banks, insurance companies, hedge funds, and governments. Financial Engineering is the study of applying math, statistics, computer science, economic theory, and other quantitative methods to analyzing and modeling financial markets. Financial engineers work at the intersection between data science and finance. The first financial engineers were Fischer Black, Robert Merton, and Myron Scholes, infamous for their options pricing model known as the Black-Scholes Model. This model won the Nobel prize in economics and is the foundation for the explosion in derivative markets.

1. Introduction (2 TUs). 2. Fundamentals of decision making and markets (8 TUs): Demand and supply analysis: Markets, Investment, Uncertainty. 3. Blockchain technology and decentralized finance (DeFi) (6 TUs) Introduction to DeFi, Risk and challenges in DeFi, AML/CFT compliance in the crypto-asset sphere. 4. Artificial intelligence and the financial sector (4 TUs): Introduction to the use of AI in the financial sector. 5. Regulatory compliance and system design (6 TUs): Privacy and compliance in Central Bank Digital Currencies, Compliance by design and through IS design. 6. Q&A (2 TUs).

This course aims to conceptualize and demystify the complex theme of financial innovation from multiple perspectives. Firstly, an introduction to financial innovation would be provided to students, leading to an understanding of the motives underlying the increasing adoption of technology-intensive tools and systems by businesses and end-users. Secondly, introductions to three relevant and disruptive technologies revolutionizing traditional business processes in finance would be taught: peer-to-peer networks and distributed ledgers, machine learning (ML) as a subset of artificial intelligence (AI), and cloud computing. Specifically, students would learn about the basics of Blockchain through distributed ledgers, gain an overview of ML modeling tools to develop critical-thinking skills and prepare for innovative idea design relevant to their future careers. Additionally, they would understand the basics of cloud computing and how different models help solve real business problems. Finally, socio-economic analyses of financial innovations would be discussed to raise awareness among students about the mutation of the financial ecosystem (including the overall economy) and its implications at human levels.

"AI for Finance and Insurance" est une formation pensée pour les décideurs, data enthusiasts et professionnels du secteur désireux de comprendre comment l’intelligence artificielle transforme en profondeur leurs métiers.La première partie introduit les fondamentaux de l’IA, avec une contextualisation historique, l’explication des concepts clés (machine learning, deep learning, LLMs, etc.) et un lexique accessible Elle s’accompagne d’une prise en main d’outils no-code et low-code (ChatGPT, Notebooks interactifs, plateformes de modélisation sans code…) permettant aux participants de prototyper rapidement des idées sans connaissances techniques avancées La deuxième partie est dédiée à l’exploration de cas d’usage à fort impact dans la finance et l’assurance : automatisation de la gestion des sinistres, scoring intelligent, lutte contre la fraude, assistant virtuel pour les clients ou collaborateurs, analyse sémantique de documents réglementaires, etc Chaque cas est illustré par des démonstrateurs ou des retours d’expérience Enfin, la troisième partie s’intéresse à la mise en œuvre concrète de l’IA dans les organisations Elle aborde les questions de stratégie data, IA, de gouvernance, de pilotage des projets, de constitution d’équipes hybrides, de buy-in interne, ainsi que les enjeux éthiques et réglementaires spécifiques au secteur financier.Cette formation offre une vue d’ensemble stratégique et opérationnelle, permettant aux participants de passer de la compréhension à l’action, et de faire de l’IA un levier réel de création de valeur.

Learn how AI-driven insights can revolutionize the way you lead teams, make decisions, and shape company culture By combining advanced data analytics with tried-and-true management principles, you’ll learn how to create a more agile, innovative, and resilient organization We’ll delve into strategic decision-making by showing you how to harness predictive models to identify trends and opportunities before they fully emerge You’ll see how these insights can optimize resource allocation, streamline operations, and align your teams around common goals We’ll also discuss how AI can help you measure employee performance, boost collaboration, and maintain high levels of engagement—even in rapidly changing work environments Throughout this module, we’ll focus on real-world case studies and hands-on exercises This practical approach ensures you gain not just theoretical knowledge but also the skills and confidence to implement AI-informed management techniques in your day-to-day practice By the end, you’ll have a fresh perspective on leadership—one that embraces data-driven strategies, places people at the center, and empowers your organization to stay one step ahead in a dynamic marketplace.

Throughout this immersive training, you’ll discover how to leverage the full potential of AI to optimize your campaigns, design hyper-personalized offers, and deeply analyze customer behavior Step by step, we’ll explore the tools and techniques that enable you to go beyond traditional marketing approaches For instance, you’ll learn how to clone a voice or how to generate AI-powered podcasts To keep things concrete and engaging, we’ll study various real-life examples from different industries (e-commerce, SaaS, retail, and more), so you can understand how other companies have successfully integrated AI into their marketing strategies We’ll also look at the latest innovations in predictive analytics, natural language processing, and image recognition—cutting-edge fields that are becoming increasingly crucial in building modern customer relationships This workshop isn’t just about theory You’ll get hands-on experience with practical, user-friendly tools—even if you don’t have advanced technical skills The goal is to give you the keys to design and run high-performance campaigns while staying agile in an ever-changing market By adopting a more innovative approach powered by artificial intelligence, you’ll be able to develop more impactful marketing strategies, strengthen customer loyalty, and drive sustainable growth for your business.

The "AI in Healthcare" workshop explores the use of artificial intelligence in healthcare and its associated ethical challenges It begins with a 45-minute introduction, offering an overview of AI applications in healthcare, such as diagnostic tools and virtual health assistants, and includes an interactive discussion about different aspects of AI systems.The next hour focuses on practical insights, presenting case studies of successful AI implementations, a live demonstration or guided exploration of an AI tool, and a group brainstorming session on integrating AI into participants’ healthcare settings This is followed by a one-hour hands-on exercise where participants work in small groups to design a simple AI workflow tailored to a specific medical scenario, with facilitated discussions to refine their ideas and address potential challenges.The workshop concludes with an hour-long session on ethics and responsible AI Key issues, such as data privacy, bias, and accountability, are explored through discussions, debates, and role-playing activities A final wrap-up session focuses on balancing innovation with ethical considerations, equipping participants with insights for using AI responsibly in healthcare.

AIVDA is an adaptative training workshop that enables participants to discover and harness the potential of Artificial Intelligence (AI) in just two days.DataThings’ AI Value Discovery Analysis enables you to identify AI use cases that have the potential to deliver rapid and significant ROI for your company, as well as explore related business opportunities.AI is perceived as a buzzword today, and its purpose and potential capabilities are often misunderstood Every day, at DataThings we experience how AI can spur innovation and deliver exceptional user experiences We are eager to support you and your teams, using relevant design-thinking tools, in implementing AI and transforming your business into a happy and augmented organization, driven by human-centricity.At the end of the course, participants will be able to understand both the benefits and the risks, as well as the aspects of AI that may fit the needs of their organization in terms of potential projects They will be able to apply different methodologies to identify and evaluate the potential ROI for AI solution implementation.

In diesem Kurs erhalten die Teilnehmer eine Einführung in die Grundlagen der Künstlichen Intelligenz (KI) und deren Anwendungsmöglichkeiten in der Verwaltung Der Kurs behandelt eine Vielzahl von Themen, darunter die Geschichte der KI, die Funktionsweise von Large Language Models (LLM) und die vielfältigen Einsatzbereiche von KI.Praktische Übungen mit Microsoft Copilot ermöglichen es den Teilnehmern, die Anwendung von KI im Arbeitsalltag der Verwaltung zu erproben Darüber hinaus wird eine Diskussion über die Chancen und Risiken von KI sowie deren Energieverbrauch und Nachhaltigkeit geführt.

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IMPORTANT INFORMATION: Pre-registration for this course is now available Following your completion of the questionnaire linked below, you will be contacted by Digital Learning Hub to approve your registration Submit a pre-registration request.This module teaches participants how to develop and implement back-end solutions using R Participants will learn to build APIs with Plumber, automate data workflows, and integrate R with databases (e.g., MySQL, PostgreSQL) The module covers advanced data processing techniques, API security, and scaling R solutions for production environments By the end, participants will be able to create back-end systems that connect R to databases and handle large-scale data workflows.

In this module, you’ll explore how cutting-edge AI solutions can revolutionize various HR processes—from recruitment and onboarding to employee engagement and workforce planning We’ll delve into how predictive analytics and machine learning can help you anticipate staffing needs, assess skill gaps, and even forecast turnover risks You’ll also learn about advanced recruitment tools that automatically screen resumes, identify top candidates, and streamline the hiring process to save you time and reduce bias Beyond recruitment, we’ll cover practical ways to apply AI for performance management, employee development, and succession planning You’ll discover how to analyze large volumes of HR data to pinpoint areas of improvement, design personalized learning paths, and boost overall employee satisfaction Throughout this module, the focus is on real-world, hands-on experiences—enabling you to experiment with intuitive software and platforms without needing a deep technical background By the end, you’ll be equipped with actionable strategies to integrate AI effectively in your HR operations, enhance the employee experience, and foster a more agile, data-driven HR function.

This comprehensive course on AI for Marketing equips professionals with the skills to leverage artificial intelligence in transforming marketing strategies Participants will delve into how AI enhances content creation, data analysis and campaign personalization The program blends theoretical insights with hands-on practice Through interactive workshops and real-world case studies, attendees will master AI-driven tools like ChatGPT, MidJourney, Perplexity The course also addresses ethical considerations and strategies to overcome common challenges like algorithmic bias By the end, participants will be able to integrate AI solutions into their workflows.

Ce cours permet aux participants de découvrir des exemples concrets d’utilisation de ChatGPT dans le domaine administratif, d’automatiser des tâches récurrentes, et de créer des lettres types personnalisées Les participants apprendront à optimiser l’usage de ChatGPT pour leurs tâches spécifiques, exploreront de nouvelles applications de l’IA dans leur travail quotidien, et évalueront l’efficacité des solutions automatisées.

Ce cours permet aux participants de découvrir des exemples concrets d’utilisation de ChatGPT dans le domaine administratif, d’automatiser des tâches récurrentes, et de créer des lettres types personnalisées Les participants apprendront à optimiser l’usage de ChatGPT pour leurs tâches spécifiques, exploreront de nouvelles applications de l’IA dans leur travail quotidien, et évalueront l’efficacité des solutions automatisées.

Join our immersive 5-day training program designed just for you Dive into hands-on sessions with AI tools like ChatGPT, learn prompt engineering, and master deep learning concepts Understand AI ethics tailored for web development and discover how to seamlessly integrate AI-powered chatbots into your WordPress site with your own knowledge base Transform your web offer with the power of AI! As generative AI reshapes the digital landscape, WordPress webmasters have a unique opportunity to enhance their websites by integrating AI-powered chatbots These intelligent assistants can revolutionize user engagement, provide personalized experiences, and streamline interactions, giving your site a competitive edge This comprehensive 5-day training program is specifically designed to equip WordPress webmasters with the skills and knowledge to harness generative AI effectively The program will be starting with the fundamentals of artificial intelligence and its practical applications in web development The training emphasizes hands-on experience with state-of-the-art AI tools, including language models like ChatGPT, image recognition software, and speech synthesis technologies By engaging directly with these tools, you’ll gain a practical understanding of how AI can be leveraged to enhance your website’s functionality and user experience Understanding the ethical implications of AI is crucial The program delves into the ethical considerations specific to webmasters, ensuring that you implement AI responsibly and sustainably You’ll explore ethical frameworks from leading organizations and learn about regulations like the European AI Act, equipping you with the knowledge to navigate the complexities of AI ethics confidently A significant portion of the training focuses on deep learning and large language models (LLMs) You’ll demystify concepts like neural networks, transformers, and datasets, gaining insights into how these technologies power AI applications The course also covers prompt engineering, teaching you how to craft effective prompts to elicit high-quality responses from AI models—a vital skill for creating engaging chatbots In the latter part of the program, you’ll apply your newfound knowledge directly to your WordPress site The training guides you through the process of designing, implementing, and integrating specialized and domain specific AI-powered chatbots into your website You’ll learn about tools and frameworks that simplify this process, even if you’re not a coding expert By the end of the training, you’ll have a fully functional chatbot tailored to your site’s needs and knowledge base, ready to enhance user engagement and provide valuable services to your audience.

Ce cours offre une introduction aux bases de l’intelligence artificielle (IA), en mettant l’accent sur l’utilisation de ChatGPT et Copilot, notamment pour des métiers administratives Vous découvrirez les principes fondamentaux de l’IA, comment interagir efficacement avec des systèmes comme ChatGPT et Copilot et les différentes applications possibles dans divers domaines Une formation idéale pour quiconque souhaite comprendre et exploiter le potentiel des technologies d’IA pour la création de contenu, l’automatisation et d’autres cas d’usage concrets.

This course aims to equip intermediate and experienced developers with the skills needed to effectively integrate AI tools into their development practices Participants will learn how to use AI to improve productivity, document their code, perform code reviews, debug and refactor The program is designed to be interactive, with practical exercises and demonstration sessions to meet the specific needs of participants.

This immersive one-day course offers a comprehensive exploration of artificial intelligence's impact on HR functions, from recruitment to performance management It addresses both HR professionals who want to modernize their practices and job seekers eager to better understand the methods used by recruiters in the AI era Participants will discover the fundamentals of AI applied to HR: generative AI, chatbots, automation, and prompt engineering Through concrete workshops, they will learn to write effective prompts, optimize a recruitment process, design a mini-training plan, or build an HR dashboard with key indicators The training covers AI uses in recruitment (CV screening, pre-qualification, sourcing), HR administration (onboarding, request management), L&D (personalized recommendations, microlearning), and performance management (real-time feedback, engagement, KPIs) A demonstration of the Aura HR tool will illustrate automation possibilities in a real environment Finally, a collaborative discussion in World Café format will address ethical, legal, and human issues related to AI adoption in organizations.

Alors que l’IA générative redéfini le paysage numérique, les webmasters WordPress ont une opportunité unique d’améliorer leurs sites Web en intégrant des chatbots alimentés par l’IA Ces assistants intelligents peuvent révolutionner l'engagement des utilisateurs, offrir des expériences personnalisées et rationaliser les interactions, donnant ainsi à votre site un avantage concurrentiel Ce programme de formation complet de 5 jours est spécialement conçu pour doter les webmasters WordPress des compétences et des connaissances nécessaires pour exploiter efficacement l’IA générative Le programme débutera par les fondamentaux de l'intelligence artificielle et ses applications pratiques dans le développement Web La formation met l'accent sur l'expérience pratique des outils d'IA de pointe, notamment des modèles de langage tels que ChatGPT, des logiciels de reconnaissance d'images et des technologies de synthèse vocale En vous engageant directement avec ces outils, vous acquerrez une compréhension pratique de la manière dont l’IA peut être exploitée pour améliorer les fonctionnalités et l’expérience utilisateur de votre site Web Comprendre les implications éthiques de l’IA est crucial Le programme approfondit les considérations éthiques spécifiques aux webmasters, garantissant que vous mettez en œuvre l'IA de manière responsable et durable Vous explorerez les cadres éthiques d'organisations de premier plan et découvrirez des réglementations telles que la loi européenne sur l'IA, vous fournissant ainsi les connaissances nécessaires pour naviguer en toute confiance dans les complexités de l'éthique de l'IA Une partie importante de la formation se concentre sur l'apprentissage profond et les grands modèles de langage (LLM) Vous démystifierez des concepts tels que les réseaux de neurones, les transformateurs et les ensembles de données, et découvrirez comment ces technologies alimentent les applications d'IA Le cours couvre également l'ingénierie des prompts, vous apprenant à créer des prompts efficaces pour obtenir des réponses de haute qualité à partir de modèles d'IA, une compétence essentielle pour créer des chatbots attrayants.Dans la dernière partie du programme, vous appliquerez vos nouvelles connaissances directement pour votre site WordPress La formation vous guide tout au long du processus de conception, de mise en œuvre et d'intégration de chatbots spécialisés et spécifiques à un domaine alimenté par l'IA dans votre site Web Vous découvrirez les outils et les frameworks qui simplifient ce processus, même si vous n'êtes pas un expert en codage À la fin de la formation, vous disposerez d’un chatbot entièrement fonctionnel adapté aux besoins et à la base de connaissances de votre site, prêt à améliorer l’engagement des utilisateurs et à fournir des services précieux à votre public.

Ce cours complet sur l’IA générative pour le marketing permet aux professionnels d’acquérir les compétences nécessaires pour exploiter l’intelligence artificielle dans la transformation des stratégies marketing Les participants exploreront comment l’IA générative améliore la création de contenu, l’analyse des données et la personnalisation des campagnes Le programme allie théorie et pratique, avec des ateliers interactifs et des études de cas réels Les participants maîtriseront des outils d’IA générative tels que ChatGPT, MidJourney et Perplexity Le cours aborde également les considérations éthiques et les stratégies pour surmonter des défis courants comme les biais algorithmiques À la fin, les participants seront en mesure d’intégrer des solutions d’IA générative dans leurs processus de travail.

Cetta formation va permettre à chaque participant de comprendre les bases de l’intelligence artificielle appliquée au test logiciel, découvrir ses cas d’usage concrets, manipuler des outils IA pour générer, analyser et automatiser des tests, identifier les limites et bonnes pratiques à respecter, et construire un plan d’action pour intégrer l’IA dans leur stratégie QA.

The course provides a comprehensive overview of how to leverage generative AI within the design thinking framework Participants will explore practical applications of AI tools across the empathize, define, ideate, prototype, and test stages Through hands-on exercises, case studies, and collaborative activities, participants will learn to harness AI to gather deeper insights, generate novel ideas, rapidly prototype concepts, and refine solutions based on data-driven feedback The training emphasizes ethical considerations and responsible AI use, ensuring participants can effectively integrate AI while maintaining a human-centered approach.

This course introduces project managers to the transformative potential of Artificial Intelligence (AI) across the entire project lifecycle (planning, execution, monitoring, and communication) Learners will explore how AI tools automate scheduling, optimize resource allocation, and assess risks through predictive analytics Real-world examples illustrate how AI can dynamically adjust timelines based on project data, team capacity, and shifting priorities Participants will also gain practical experience using AI-driven assistants—such as chatbots for reporting and dashboards for data visualization—to streamline workflows and decision-making By the end of the course, project managers will be equipped to integrate AI responsibly and effectively into their project management strategies.

Cette formation immersive d’une journée propose une exploration complète de l’impact de l’intelligence artificielle sur les fonctions RH, du recrutement à la gestion de la performance Elle s’adresse à la fois aux professionnels RH qui souhaitent moderniser leurs pratiques et aux demandeurs d’emploi désireux de mieux comprendre les méthodes utilisées par les recruteurs à l’ère de l’IA.Les participants découvriront les fondamentaux de l’IA appliquée aux RH : IA générative, chatbots, automatisation, et prompt engineering Grâce à des ateliers concrets, ils apprendront à rédiger des prompts efficaces, optimiser un processus de recrutement, concevoir un mini-plan de formation ou encore bâtir un tableau de bord RH avec des indicateurs clés.La formation couvre les usages de l’IA en recrutement (tri de CV, préqualification, sourcing), en administration RH (onboarding, gestion des demandes), en L&D (recommandations personnalisées, microlearning), et en gestion de la performance (feedback en temps réel, engagement, KPIs).Une démonstration de l’outil Aura HR viendra illustrer les possibilités d’automatisation dans un environnement réel Enfin, une discussion collaborative en format World Café permettra d’aborder les enjeux éthiques, juridiques et humains liés à l’adoption de l’IA dans les organisations.

Unveil the hidden stories behind artworks using the power of AI This hands-on course introduces participants to creative uses of AI in analyzing visual art Using tools such as ChatGPT, Grok, Copilot, and DALL·E—without any coding required—participants will explore how AI interprets artistic styles, symbolism, composition, and context Through guided exercises and prompt engineering, they will combine traditional research with AI-driven exploration The course also encourages collaborative learning and includes ethical reflections on AI-generated content, including bias, authorship, and cultural sensitivity.

Cette formation vise à introduire à l'utilisation pratique et contextualisée de l'IA générative dans les tâches quotidiennes des participants Les participants apprendront à formuler des prompts efficaces pour divers outils d'IA, optimisant ainsi leurs flux de travail dans les services administratifs et de support Au travers de cinq sessions interactives, les participants seront introduits au domaine et à ses enjeux et exploreront des cas pratiques, des démonstrations en direct, et des exercices guidés pour maîtriser l'utilisation des technologies d'IA générative disponibles sur le marché La formation comporte les modules suivants : .Introduction à l'IA Générative.Ethique et Intelligence Artificielle.Techniques de Conception de Prompts.Applications Pratiques, études de cas et Scénarios d'Utilisation.Mini-projet IA Générative en situation professionnelle.

Cet atelier intensif d'une journée et demie initie les débutants à la création web moderne grâce à l'IA Les participants découvriront comment l'intelligence artificielle peut aider chacun à créer son premier site web personnel, même sans expérience préalable en codage La formation met l'accent sur la compréhension des possibilités de la création numérique plutôt que sur la formation d'experts techniques Grâce à une exploration guidée d'outils d'IA conviviaux, les apprenants créeront une page d'accueil simple tout en se familiarisant avec le fonctionnement du développement web moderne La formation privilégie la découverte pratique aux concepts techniques complexes, rendant la création web accessible à tous les publics et à tous les âges

Cet atelier de 3 jours apprend aux développeurs à exploiter l'IA pour prendre des décisions cruciales en amont du développement d'applications Les participants apprennent à utiliser l'IA comme un conseiller stratégique pour l'architecture, la modélisation des données, la conception UX/UI et le choix des technologies, tout en maintenant un esprit critique tout au long du processus La formation met l'accent sur la création de prototypes aux bases solides qui facilitent les transitions vers les environnements de production Grâce à des sessions d'idéation en direct, les développeurs découvrent comment Vibe Coding transforme les paradigmes de développement traditionnels en permettant une collaboration en temps réel avec les utilisateurs finaux dès les premières phases de développement.

This course is tailored for individuals seeking a fundamental understanding of AI and its practical applications in trading This is a practice-oriented course that focuses on real-world data, Al techniques, and market insights Participants will learn how Al models analyze financial data, predict market trends, and automate trading decisions The course also covers geopolitical, economic, and policy-driven factors that influence financial markets and Al-based strategies This course is ideal for beginners and intermediate learners While basic Python and trading knowledge is preferable, it is not mandatory By the end of the course, learners will confidently apply AI beyond trading, leveraging its potential across diverse industries.

Executive programme enabling decision-makers to strategically adopt and govern AI in financial services

Executive programme focused on blockchain, tokenisation, and data infrastructure for financial services

Executive learning platform connecting financial professionals with cutting-edge FinTech solutions

Gaussian Random Variables (characteristic function, CLT, stability properties, Stein's lemma) Gaussian Random Vectors (definition, characteristic function, existence, uniqueness in law, multivariate CLT, density, Hermite polynomials) Gaussian Random Processes (definition, modifications, uniqueness in law, function of positive type, existence, Brownian motion, continuity) Fractional Brownian Motion (definition, existence, Hölder regularity)

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Types: Variables, Strings, Booleans, Recursion, Repetitions, Lists and Collections, Files, Global Variables and Function Arguments, Object-Oriented Programming Concepts, Object-Oriented Programming vs Procedural Programming, Inheritance and Polymorphism, User Interfaces, Exceptions, Object Serialization, Modules Organization, Jupyter Notebooks, NumPy, Pandas

Course content: 1. Introduction, history of programming languages; 2. Programming Language Concepts; 3. Lambda calculus; 4. Binding, Parameter passing modes; 5. The Algol family of languages; 6. ML; 7. Type systems and type inference; 8. Scope, Functions, and Storage Management; 9. Control structures and exceptions; 10. Modularity and abstraction; 11. Object Orientation; 12. Concurrent Programming; 13. Summary and Review

1. Introduction to computational problem solving and the Python programming language. 2. Basic syntax and semantics of Python. 3. Functions and modules. 4. Problem solving and recursion. 5. Structured types and function objects. 6. Files and exceptions. 7. Testing. 8. Debugging. 9. Iterators and generators. 10. Floating-point numbers. 11. Introduction to object-oriented programming in Python. 12. Introduction to popular libraries: mathplotlib, NumPy and Pandas. 13. Introduction to software engineering.

This course covers the basics of scientific programming with Python. It is aimed at people who have done some programming before, perhaps on an undergraduate course, but need a refresher before starting their Masters or Doctoral degrees at the University.

Program correctness: types of errors (syntax, logic, run-time), the concept of a specification, defensive programming (e.g., secure coding, exception handling), code reviews, testing fundamentals and test-case generation, the role and the use of contracts (including pre- and post-conditions), unit testing. Simple refactoring. Modern programming environments: Code search, Programming using library components and their APIs. Documentation and program style. Introduction to software process models (e.g., waterfall, incremental, agile); activities within software lifecycles. Programming in the large vs. individual programming. Evaluation of software process models. Software quality concepts. Process improvement. Software process capability maturity models. Software process measurements. Software quality assurance and the role of measurements. Release management. Requirements analysis and design modelling tools. Describing functional requirements using, for example, use cases or users’ stories. Properties of requirements including consistency, validity, completeness, and feasibility. Software requirements elicitation. Describing system data using, for example, class diagrams or entity-relationship diagrams. Non-functional requirements and their relationship to software quality (cross-reference IAS/Secure Software Engineering). Evaluation and use of requirements specifications. Requirements analysis modelling techniques. Acceptability of certainty/uncertainty considerations regarding software/system behaviour. Prototyping. Basic concepts of formal requirements specification. Requirements specification. Requirements validation. Requirements tracing. System design principles: levels of abstraction (architectural design and detailed design). Structural and behavioural models of software designs. Relationships between requirements and designs: transformation of models, design of contracts, invariants.

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This course offers a comprehensive introduction to programming fundamentals using Python, emphasizing practical application through hands-on workshops. Students will learn core concepts such as control flow, data structures, and algorithms, progressing to object-oriented programming, file I/O, and interacting with APIs. The curriculum culminates in project-based learning, applying Python to real-world scenarios like sentiment analysis and signal processing.

Basic programming skills, with interactive Python worksheets for plotting. This course is an introduction to both computation for physics, and computational physics: a training in the computer skills needed to implement common physics calculations, and an introduction to the types of physical models which require computational treatment.

Introduction to Computation and Programming Languages, weeks: 1. Elements of Python Programming Language, weeks: 2, 3. Concepts of Object-Oriented Programming, weeks: 4, 5. Basics of Software Design, weeks: 6. Data Structures and Data Visualization, weeks: 7, 8. Introduction to Machine Learning and AI Tools, weeks: 9, 10. Systems Design, APIs and Applications, weeks: 11.

Basic programming concepts (variables, data structures, conditions, loops, functions, I/O) illustrated through simple, medicine-relevant applications. Data mining, analysis & transformation. Basic machine learning. Image or sound analysis. Programming of Medical devices / IoT.

In this course, the term 'data' is seen centric and we will look at data from different perspectives. We will discuss selected aspects of Data Preparation and Preprocessing, Data Statistics, Data Security, Data Privacy, Data Management, Big and Small Data, Data Retrieval, Data Visualization, and Data Analytics.

The course includes the following topics: 1. General introduction to Artificial Intelligence2. Problem resolution, search algorithms3. Games, alpha-beta pruning4. Meta-heuristics, genetic algorithms, swarm algorithms5. Constraint programming6. Markov Decision Processes, reinforcement learning7. Learning models for regression, classification, clustering8. Evaluating the performance of a learning model9. Decision trees, forests10. Artificial neural networks11. Unsupervised learning, k-Nearest neighbors, self-organizing maps, growing neural gas

The course will start with motivating the study of probability and statistics, highlighting its applications in everyday life and specifically in physics and engineering. We will then cover descriptive statistics and how to avoid common statistical errors. Next, we will establish the fundamentals of probability theory, including discrete random variables, stochastic simulations, and finally draw connections to modern topics such as artificial intelligence and data science.

This course will focus on two statistical modeling approaches: top-down through parametric models and bottom-up via data-adaptive methods. Specifically, it will cover classical distributions and their limitations, flexible distributions for complex modern datasets, interpretable machine learning, and the distinction between these two modeling cultures.

High Dimensional Regression Model Parameter Estimation Under Constraints: Lasso and Related Estimation Procedures Variable Selection Introduction to Principal Component Analysis Basic Elements of Random Matrix Theory: Semi-Circle Law, Marcenko-Pastur Distribution, Tracy-Widom Distribution Estimation of High Dimensional Covariance and Precision Matrices; Hard and Soft Thresholding

This lecture confronts students with real instances of such problems. They are first asked to model the problem, and next proposed solutions include exact methods, relaxations, approximations, heuristics, and meta-heuristics. These practical study cases are supported by theoretical lectures on Problem Solving (1st semester).

Concept of Algorithm; Complexity of Algorithms; Sorting Algorithms; Mathematical Background; Data Structures; Hashing; Graph Algorithms; Classification of Algorithms; Complexity of Problems.

Notre formation s’articule sur différents chapitres comprenant tout d’abord l’introduction sur l’histoire et évolution du langage, la place de R Studio ainsi que le positionnement de R dans la data-science Ensuite, nous expliquons : la découverte de R et R studio, à savoir : le téléchargement et l’installation / Les 4 fenêtres de l’interface R Studio / Les différents outils de R Studio / Installation des packages /Réglage des options / L’aide.Il est question également d’organiser son travail sous R à savoir : projet R, notion de working directory, workspace et son architecture Les bases du langage R intègrent les types de données : numériques, facteurs, chaînes de caractères, booléens, dates, etc, l’assignation, les opérateurs et les fonctions mathématiques Les différentes structures de données concernent : les vecteurs: initialisation et manipulation, les data.frame : initialisation et manipulation, les listes : initialisation et manipulation et quelques fonctions utiles Un chapitre parle de l’importation et l’exportation de données : les différents formats de fichiers : csv, txt, l’utilisation de l’outil d’importation ainsi que quelques fonctions de vérification.Les journées suivantes sont consacrées aux descriptions des données : Fonctions descriptives pour les variables numériques et catégorielles, la création de table de contingence et des proportions Ensuite la visualisation des données avec les fonctions graphiques de base et le package esquisse et les fonctions : déclaration, arguments, appel, boucles, structures de contrôle if, ifelse, apply Un chapitre explique comment trouver de l’aide en ligne, sur la fonction apropos, le site bookdown, la communauté R ainsi que les forums de développeurs.La formation se terminera par les mises à jour des packages, de RStudio, de R ainsi que la présentation de quelques packages incontournables comme dplyr pour la manipulation des données, ggplot2 pour la réalisation de graphiques et rmarkdown pour la génération de rapports dynamiques automatisés.

This course offers a detailed exploration of R programming covering syntax, data types, data structures, control structures, and statistical analysis Participants will engage in hands-on exercises to apply R in practical scenarios, enhancing their data manipulation and visualisation skills.

IMPORTANT INFORMATION: Pre-registration for this course is now available Following your completion of the questionnaire linked below, you will be contacted by Digital Learning Hub to approve your registration Submit a pre-registration request.This module is designed for beginners who are new to R and version control Participants will learn the essential functions in R for data manipulation and visualization They will also be introduced to GitHub for version control, learning how to set up repositories, commit changes, and collaborate with others By the end of this module, participants will have a solid foundation in R and GitHub to use in their future projects.

IMPORTANT INFORMATION: Pre-registration for this course is now available Following your completion of the questionnaire linked below, you will be contacted by Digital Learning Hub to approve your registration Submit a pre-registration request.This module teaches participants how to streamline and automate their R development workflows by setting up CI/CD pipelines Participants will learn to integrate tools like Jenkins or GitHub Actions for automated testing, package management, and deployment By the end of the module, participants will be able to manage dependencies using renv, perform automated testing, and set up deployment processes for their R applications in production.

Dans cette formation, nous définirons d'abord les statistiques et leur rôle central en IA, notamment dans des domaines comme l'apprentissage supervisé, où les données servent à entraîner des modèles À travers des exemples concrets, nous introduirons les types de données (quantitatives et qualitatives) et les échelles de mesure (nominale, ordinale, ratio) utilisées pour structurer l’information.Les concepts fondamentaux des statistiques descriptives (tendances centrales, dispersion, corrélation, covariance, représentation graphique) seront illustrés par des applications en IA, telles que l’analyse de données d’entraînement ou la sélection des variables clés pour les modèles Nous explorerons aussi les principes clés de la probabilité (événements disjoints, indépendants, probabilité conditionnelle, théorème de Bayes), indispensables pour comprendre des algorithmes comme les modèles bayésiens Les lois de distribution (normale, binomiale, Poisson) et l’espérance mathématique seront abordées dans le cadre de la modélisation de la variabilité des données.Enfin, après avoir posé ces bases, nous approfondirons les notions avancées d’inférence statistique, d’intervalles de confiance et de tests d’hypothèses, en montrant comment ces outils permettent d’évaluer la performance des modèles, de tester la fiabilité des prédictions et d’optimiser les résultats en machine learning.

This course covers the fundamental concepts of probability theory, including sample space, events, and the axioms of probability It explores both the Bayesian and frequentist viewpoints, conditional probabilities, independence, and introduces the two-state Markov chain Additionally, the course discusses Bayes' theorem, naïve Bayes, classical probability distributions (both discrete and continuous), and key concepts such as expected value and variance The course offers a solid foundation in probability theory with real-world applications.

Ce cours aborde les concepts fondamentaux de la théorie des probabilités, y compris l'univers, les événements et les axiomes de la probabilité Il explore les points de vue bayésien et fréquentiste, les probabilités conditonnelles, l'indépendance, et introduit les chaînes de Markov à deux états.De plus, le cours aborde le théorème de Bayes, l'algorithem “naïve Bayes”, les distributions de probabilités classiques (discrètes et continues), ainsi que des concepts clés tels que l'espérance et la variance Le cours offre une base solide en théorie des probabilités avec des applications concrètes.

This course will provide learners with comprehensive knowledge of Python, starting from the basics and progressing to proficient data analysis skills It will commence with an introduction to Python, covering fundamental topics such as syntax, variable declaration, loops, conditions, and various predefined modules Advancing further, the course will delve into interacting with files, including Excel sheets, and data exchange A solid understanding of object-oriented programming (OOP) and inheritance will be provided to facilitate deeper exploration of Pandas, NumPy, Matplotlib, and Seaborn modules Practical application of the knowledge gained in the course will culminate in the creation of a real-life project.

This course on Text Processing with Python provides the fundamental techniques of handling and manipulating text data Starting with basic string operations, the course progresses to more sophisticated methods such as tokenization, stemming, lemmatization, and the removal of stop words Participants will gain practical experience with regular expressions and will be introduced to libraries like NLTK, Matplotlib, and WordCloud for efficient text processing and visualization By the end of the course, participants will be equipped with the skills to perform basic text processing for various applications in natural language processing and data analysis.It provides a beginner-friendly but practical foundation in key text processing techniques: cleaning and manipulating raw text, tokenization, regular expressions, and basic visualization You’ll also get hands-on experience with Python libraries to prepare real text data for machine learning and natural language processing tasks.

Data science is a rapidly evolving field at the intersection of statistics, computer science, and business acumen Understanding data and its hidden patterns play a crucial role in decision-making and strategic planning in today’s data-driven world Python, due to its readability and a wide array of scientific libraries, is a widely used programming language in data science This module aims to equip learners with fundamental concepts and practical skills of data science using Python.

This course provides a comprehensive introduction to regression analysis, using Excel as a tool The course takes the participants through the principles of regressions, linear curve fitting, the principle of “ordinary least squares, forecasting and develops examples using Excel and Visual Basic for Applications (VBA).

The course is intended to provide a solid foundation in the language and its applications in data analysis and business operations The course is tailored towards business analysts and other professionals who are interested in data analysis using Python.Python has become the language of choice for data analysis and business operations due to its ease of use, simplicity, and versatility This course will provide a comprehensive introduction to Python programming language, which includes data types, control structures, functions, data analysis, and data visualization.The course is designed to help students learn how to write and execute Python programs, analyze data using Python, and visualize data using Python libraries such as Pandas, Matplotlib, and Seaborn Upon completion of this course, learners will be able to apply Python programming to data analysis tasks and business operations, as well as analyze time series and price options using stochastic models In addition, the course will cover the manipulation of flat files and interaction with SQL databases.

This course will cover the essential statistical concepts that underpin a data science task The course will start with an overview of the most important concepts of descriptive statistics (mean/median/mode, dispersion, skewness, quantiles, percentages, graphical representations), followed by an introduction to probability with particular focus on the most important probability distributions (uniform, Gaussian, Poisson, binomial, etc.) Equipped with this knowledge, the course will then dive into statistical inference, first point estimation and confidence intervals, and second hypothesis testing These concepts will be explained intuitively and examples of how to use them will be provided Depending on the participants’ interests and experience, we may go further into hypothesis testing (ANOVA, goodness-of-fit testing, etc.) Finally, the course will touch upon statistical learning by discussing linear regression and logistic regression.

Ce cours couvre les concepts statistiques essentels pour la science des données Il débutera par une introducton aux statistiques descriptives (moyenne, médiane, mode, dispersion, asymétrie, quantles, pourcentages, représentatons graphiques), suivie d'une présentaton des principales distributons de probabilité (uniforme, gaussienne, de Poisson, binomiale, etc.).Armés de ces bases, les partcipants se plongeront ensuite dans l'inférence statistique, en abordant l'estimation ponctuelle et les intervalles de confiance, ainsi que les tests d'hypothèses Ces concepts seront expliqués de manière claire et accompagnés d'exemples pratiques En foncton des attentes et de l'expérience des partcipants, nous approfondirons certains aspects des tests d'hypothèses (ANOVA, tests de conformité, etc.).Enfn, si le temps le permet, le cours pourra toucher l'apprentissage statistique avec une discussion sur la régression linéaire et logistique.

Luxembourg has always been known as a media hub, offering cross-border services initially in television, then satellites, and more recently internet- and tech-based services (ICT sector including data services, FinTech, and others). The application of EU law in a national context can be demonstrated using Luxembourg as an example. The courses have focused on newly proposed or enacted pieces of EU legislation, such as the AI Act. The course typically covers several areas that change annually depending on the sector's new developments: after a general presentation of the ICT landscape with a focus on legal and regulatory aspects and the historical development of 'placing Luxembourg on the ICT map', a selection of focus areas are covered. Regularly, new developments in Luxembourg's telecommunications legal framework are discussed. More recently, examples have included the legal implications of introducing autonomous cars, adapting laws concerning the financial sector for FinTech solutions, using Blockchain technology in various sectors and regulating AI. Other examples include electronic money and payments, virtual currencies, terms and conditions of e-commerce providers, digital evidence, cloud computing etc. The course is taught by a practitioner with relevant experience in consultancy, political decision-making, and academic analysis of tech law contexts, demonstrating how the law is applied and how legal frameworks can be used to implement policy goals and contribute to global competition. Parts of the course are presented together with external experts from various fields.

This course aims to provide an overview of the key legal instruments primarily at EU level, as well as at the international and national (Luxembourgish) level, that regulate cybersecurity.

Introduction to EU's framework for digital sovereignty - tools, mechanisms, and actors; Chips market: the Chips Act as a mean to secure EU digital sovereignty; 5G infrastructure deployment: geopolitical and legal challenges; Digital infrastructure sharing imperative: perspectives on an EU Cloud; Software and software development: issues of liability in the context of automated processes/decisions; Artificial Intelligence (AI): opportunities and challenges for the EU digital sovereignty; Blockchain and Distributed Ledger Technologies: beyond the hype - socio-economic and legal perspectives for the EU digital sovereignty; Quantum technologies: securing strategic autonomy through quantum R Data protection: General Data Protection Regulation (GDPR) as a flagship regulation for a digital sovereign EU; Digital services: the Digital Services Act for a safe and accountable online environment; Digital Markets Act: a bid for fairness towards and between 'gatekeepers'? ; European Digital Identity: the idea of a personal digital wallet for EU citizens and residents; Intellectual property: towards a harmonized EU patent rules to boost innovation, investment, and competitiveness; Beyond efficiency and legal niceties: Ethics and technology

Importance of protecting intangible heritage and intellectual property in the knowledge economy - Main IP rights - Software, Open source and Artificial Intelligence - Confidentiality and contracts 1. Importance of protecting intangible heritage and intellectual property in the knowledge economy - Main IP rights - Software, Open source and Artificial Intelligence - Confidentiality and contracts 2. Patents and databases – The patent system – Information contained in a patent – Accessible online databases 3. Good research practices in patents – Database interrogation methods – Developing a research strategy – Practical exercises (Patent search on Espacenet / Patentscope) 4. Technological surveillance - The importance of surveillance and its utility - Informetrics, statistics, and analysis - Practical exercises (Statistics on patents on Espacenet)

Introduction to Intellectual Property, Intellectual Property Rights, Intellectual Property and Internet, Intellectual Property and Software, Intellectual Property and Open Source, Intellectual Property and Artificial Intelligence, Intellectual Property and Virtual Creations, Patent Search, Intellectual Property Information

The course is structured to deliver a learning experience that combines theoretical insights with practical applications Participants will dive into the legal, ethical, and technological challenges posed by AI-generated content, examining the implications for copyright protection, creative industries, and legal frameworks The course covers critical aspects including technological innovations, regulatory developments, and emerging best practices for managing intellectual property.

If assisted tools are acknowledged to improve process of organisations, there are also growing concerns about their embedded algorithms which remain biased Like previously with General Data Protection Regulation (GDPR), companies will have to comply with an upcoming EU regulation relating to their use of AI assisted systems in order to prevent the perpetuation of historical patterns of discrimination (e.g., against women, certain age groups, persons with disabilities, or persons of certain racial or ethnic origins or sexual orientation).This training will help organisations cope with the future AI Act To learn more about this regulation: https: //artificialintelligenceact.eu/

If assisted tools are acknowledged to improve process of organisations, there are also growing concerns about their embedded algorithms which remain biased Like previously with General Data Protection Regulation (GDPR), companies will have to comply with an upcoming EU regulation relating to their use of AI assisted systems in order to prevent the perpetuation of historical patterns of discrimination (e.g., against women, certain age groups, persons with disabilities, or persons of certain racial or ethnic origins or sexual orientation).This training will help organisations cope with the future AI Act To learn more about this regulation: https: //artificialintelligenceact.eu/

Ce cours porte sur le règlement européen relatif à l’intelligence artificielle (« AI Act » ou « RIA »).Il permet de comprendre les notions clés, cerner les différents niveaux de risques ainsi que les méthodes de gouvernance pour encadrer le déploiement de l’IA.

This course provides a comprehensive introduction to artificial intelligence (AI) by exploring its definitions, ethical challenges, and regulatory frameworks First participants will gain a foundational understanding of AI through practical definitions and real-world applications in key industries such as healthcare, finance, and transportation Furthermore, topics will include discussions on intelligence, consciousness, machine learning, and the ethical challenges surrounding AI, such as privacy concerns, surveillance, and biases in AI systems.Lastly, the course will focus on the European Union’s AI regulatory framework, particularly the EU AI-Act Participants will explore the human-centric and risk-based approaches of the Act, delving into its core principles and objectives aimed at promoting responsible AI development and implementation across Europe.

Delve into the EU AI Act’s framework with this comprehensive course, offering in-depth insights on compliance, systemic risks, GDPR considerations, and the development of AI policies.This specialized course offers a deep dive into the EU AI Act Modules cover a range of pressing topics, from understanding the AI Act key requirements to identifying potential AI harms and understanding the importance of AI literacy and data quality Additionally, participants will gain practical knowledge on the interplay between the AI Act and GDPR.By the end of this course, attendees will be equipped to navigate the AI Act confidently, implement compliant practices, and foster responsible AI literacy across their organizations This training is ideal for those seeking an actionable roadmap to meet current and emerging AI regulatory standards.

Cette formation vise apporter les éléments de compréhension des concepts et enjeux juridiques de la sécurité de l’information Elle permet ensuite aux participants de contrôler et de valider leur conformité.

Loin du jargon juridique cette formation se propose de délivrer des clés et participer à développer des réflexes chez les informaticiens (développeurs ou responsables de la sécurité des systèmes d’information, …) Il s’agira également de prouver que les délégués à la protection des données peuvent faire l’effort de comprendre les techniciens et vice versa, en prenant compte les contraintes de chacun.

L’importance des données dans le paysage numérique actuel et des considérations spécifiques liées à leur gestion au Luxembourg ne cessent de croître.Les données sont devenues un atout essentiel pour les organisations dans tous les secteurs, favorisant une prise de décision éclairée, améliorant l’efficacité opérationnelle et permettant l’innovation Cependant, une gestion efficace des données nécessite une compréhension approfondie des bonnes pratiques de l’industrie et des cadres réglementaires nationaux et européens Dans ce contexte, le cadre légal de la gestion des données joue un rôle crucial afin de garantir la protection des droits des citoyens, et en vue de promouvoir la transparence et l’établissement de lignes directrices pour un traitement dit « responsable » des données.Via un tour d’horizon des principes de gestion des données, des pratiques et du paysage réglementaire, ce cours dote les participants des connaissances nécessaires pour faire face aux complexités de la gestion des données au Luxembourg Il couvre des sujets tels que les données ouvertes, les lois sur la protection des données, la qualité des données, les considérations relatives à l’infrastructure, l’analyse et les aspects éthiques de l’utilisation des données.Objectifs de la formation : .1 Fournir aux participants des bases solides en matière de principes et de pratiques de gestion des données adaptées au contexte luxembourgeois 2 Donner aux participants les compétences et les connaissances nécessaires pour gérer et exploiter efficacement les données dans divers contextes organisationnels 3 Aborder le paysage réglementaire spécifique et les lois sur la protection des données au Luxembourg et au niveau européen 4 Lier les connaissances théoriques aux pratiques de gestion des données dans la vie de tous les jours.Le cours est constitué de présentations, de discussions interactives, d’études de cas et d’exercices pratiques.

This course provides you with an introduction to the requirements of the General Data Protection Regulation (GDPR), how to comply and demonstrate compliance, how to address practical issues, and how to embed data protection into operations.

This course provides you with an introduction to the requirements of the General Data Protection Regulation (GDPR), how to comply and demonstrate compliance, how to address practical issues, and how to embed data protection into operations.

Dans un monde où les appareils connectés, l'intelligence artificielle et les réseaux sociaux suivent chacun de vos mouvements, comprendre vos droits en matière de vie privée n'est plus une option: c'est essentiel.Cette formation explore les principes fondamentaux du RGPD et de la protection de la vie privée Vous decouvriez comment vos données sont utilisées, et vous reprendrez le pouvoir grâce à des astuces, reflexes, pour rester en sécurité, conforme et maître de vos informations.

In a world where connected devices, AI and social media track every move, understanding your privacy rights is no longer optional: it's essential This training dives into the core principles of GDPR and privacy protection You will learn how your data is used (and misused) and gain back power with the tools to stay safe, compliant, and in control.

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This course aims to familiarize students with the main social and democratic issues raised by the digitalization of the media. In particular, the course will deal with the effect of digitalization on the use of media (with a focus on social media and AI), the protection of fundamental rights, the economy and political independence of the media, the formation of public opinion, the media governance (including platforms) and the role of public service media and community media. The course attempts to adopt a global perspective by including cases from the EU, US and other countries from the "global south". Attention will also be paid to the case of Luxembourg, drawing on data obtained as part of the "Media Pluralism Monitor" surveys which was launched in 2016. As part of the course, students will be invited to attend participate and a series of conferences organized in the context of the project Medialux resulting from a scientific collaboration with the Department of Media, Connectivity and Digital Policy from the Ministry of State.

Given the numerous social, economic, ecological, and societal challenges, it is essential for schools and society to empower students to engage independently and responsibly in their communities and to address associated issues constructively (OECD, 2018). The Education in the Digital Age: Opportunities and Challenges course, accordingly, focuses on exploring and critically reflecting on the global megatrends and their inherent impact on education. In accordance with the OECD (2025) report “Trends Shaping Education” the course will deal with five intertwining topics: a) education in the AI age, b) democracy, media literacy and critical thinking skills, c) shifts in working expectations and flexibility, d) climate education, and e) knowledge, skills and attitudes for the future. To critically reflect and discuss on the impacts on education, students will work with several scenarios, “trigger” questions, and empirical studies that will support the development of a group project exploring the many futures of education, with particular focus on the implications for schooling in Luxembourg.

Artificial intelligence (AI) represents one of the digital world domains that is currently the subject of an important development all over the world. This means that European citizens are and will be confronted, in their professional and personal lives, with digital products that include some artificial intelligence components.This course covers the following aspects: – the Artificial Intelligence landscape and the position of deep learning within it.– the practice of some accessible artificial intelligences experimenting the various techniques in different application domains: Text generation (e.g. ChatGPT), Object recognition in images or videos, Handwriting recognition, Generation of images from text, Automatic translation of text in all languages, Speech recognition, Speech synthesis.– the ethical and legal dimensions of artificial intelligence.– the impact of AI on society and on the job market.– the important notions which constitute the basis of the field of deep learning (neural network, weight, activation function, batch, bias, cost function, dropout, epoch, forward/backward propagation, gradient descent, hidden layer, parameters, hyper-parameters, input, output, learning rate, dataset, data augmentation, training, validation, test datasets, architecture, ANN, CNN, RNN, GAN, Transformers).– the practice in groups through a mini-project the ethical, legal and societal impact analysis of a specific AI product or technology.

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Advances in computing technology, especially in Artificial Intelligence, raise the very real prospect of imminent, radical changes to everyday human life: e.g. in employment, healthcare, military conflicts, privacy, education, legal processes and institutions, etc. Trying to work out the ethical implications of these changes is surely one of the most pressing questions that humanity currently faces. AI and related technologies also raise profound philosophical questions concerning the nature of the mental, intelligence, rationality and knowledge.In this course we will try to approach some of these ethical and philosophical questions from an inter-disciplinary perspective. The course will be taught by faculty members from both the Philosophy and Computer Science departments and is open to students studying either the Masters in Contemporary European Philosophy or the Masters in Information and Computer Science.

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1) Introduction (Afshin) 2) Fundamentals of ML, DL and NLP in Information System (Afshin) 3) The rise of GenAI/LLM in Information System (Afshin) 4) Optimizing LLMs for domain specific intelligence via fine tuning (Afshin) 5) Agentic AI (Igor) 6) Human – AI interaction (cooperation, collaboration): the need of human in the loop (Igor) 7) Hybrid Intelligence: the need to empower humans (Igor) 8) Explainable AI: the need of human to understand the AI-driven decision-making (Igor) 9) Applications of Emerging Technologies in health (Afshin) 10) Applications of Emerging Technologies in recommender systems (Igor) 11) Applications of Emerging Technologies in democracy and public governance (Igor) 12) Data and ethical considerations of Emerging Technologies (Afshin) 13) Dark side of Emerging Technologies (Gilbert Fridgen, Afshin KHADANKISHANDI, Igor Tchappi) 14) Q&A (all)

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This course explores the rapidly evolving world of Generative AI (GenAI), providing students with a comprehensive understanding of cutting-edge techniques, methodologies, and applications. The course bridges technical knowledge and practical applications of GenAI in multilingual and multicultural contexts. Key topics that are covered in the course include natural language processing, text, image, sound and video generation, ethical considerations, and the impact of AI on global communication. Through interactive lectures, hands-on group projects, and critical discussions, students will gain the skills necessary to analyze and implement AI-driven solutions. By the end of the course, participants will be well-equipped to leverage GenAI in their respective fields of personal and/or professional interest, fostering innovation in diverse settings.

Since Maurice Halbwachs' foundational work in the interwar period and then Pierre Nora's in the 1970s and 1980s, memory studies have become largely internationalized. This course will focus on the transdisciplinary fundamentals of memory studies, their historiography, and will then orient towards digital memory studies and their applications to European societies. We will insist more particularly on Generative AI and their 'embedded' views on the past. This course will particularly examine the new role of Generative AI-driven chatbots and image generation platforms in shaping our collective memory.

This seminar explores how historical research, publication, and teaching practices have changed due to mass digitization, omnipresent access to computers and the internet, machine learning, and AI. Students will critically reflect on these developments and learn how to benefit from them.This course combines theoretical approaches with hands-on sessions. Students will be introduced to discussions on how digitization challenges concepts such as source criticism, transparency, and authenticity. They will learn to work with tools such as Zotero and Tropy for information management and become aware of how the politics of digitization of cultural heritage institutions shape the data available to us.Machine learning techniques today add new layers of information to digitized and born-digital sources at previously unthinkable scales. Taking the example of newspaper and radio sources, students will explore the added value of e.g. text reuse detection, topic modeling, and named entity recognition.Interactive notebooks (Jupyter, Google Colab) change how historians conduct and publish their research by integrating code for data analysis, text, and different types of media. Students will explore these new practices and take the Journal of Digital History as an example.ChatGPT and other large models for language and images are changing the ways in which we create texts and media, how we obtain and process information, and thereby challenge existing modes for the production of knowledge. We will experiment with the opportunities offered by them and discuss the technical, epistemological, and ethical challenges inherent in their use for research and teaching.Finally, shifting to new forms of publication, teaching, and public engagement in the digital age, we will explore games, virtual exhibitions, tools like Omeka S, and other forms of interactive historical storytelling.Sessions will include elements of group discussion, (guest) lectures, work in small teams, and individual work on concrete case studies using the introduced digital tools wherever possible. The course is designed to offer a gentle introduction to the tools and techniques described here; no technical skills are required at the onset.

This course offers a practical and critical introduction to journalism through the lens of contemporary media production. Students will explore how journalistic principles can be applied across diverse formats such as podcasts, video interviews, and social media reels or shorts. Emphasis is placed on both content creation and delivery, equipping students with the tools to report, present, and publish professionally in a digital-first media landscape.The curriculum covers key elements of journalistic practice, including interview preparation and structure, effective questioning techniques, and ethical language use. Students will also examine performance-based skills such as voice control, camera presence, and using a teleprompter to enhance credibility and audience trust.A special focus is placed on AI's growing role in media production. Students will engage in discussions about the risks, benefits, and ethical considerations of AI-enhanced content creation, gaining an informed perspective on the evolving digital media landscape. Through collaborative exercises, practical workshops, and guided reflections, participants will leave the course with a toolkit of strategies to communicate effectively in diverse media formats.

The aim of this course is to provide students with multidisciplinary knowledge that can further foster their growth as artists, academics, and professionals in the Computer Graphics, Visual Storytelling, and Media Studies fields. Each year guest speakers will bring fresh perspectives on different topics.2024-25 Lectures and Guest Speakers: – Introduction to Visual Literacy (Ira Yeroshko, 22 TUs): Unlock the secrets behind the images that shape our world. In this foundational course, we will explore the ten threshold concepts of photography, revealing how this essential medium forms the bedrock of many visual arts. Photography is not just about capturing moments—it’s about constructing, influencing, and interpreting the visual world around us. As aspiring animators, understanding these concepts will empower you to create more compelling, nuanced, and impactful visuals in your own projects.Through engaging discussions and an examination of diverse photographic projects across various techniques and genres, you’ll gain insights into the evolution of image-making, the ways in which images can be manipulated, and the powerful role they play in visual storytelling. Although this course is entirely theoretical, it will provide you with the critical tools and inspiration to experiment with new ideas and integrate them into your animation work. Whether you’re just starting out or looking to deepen your understanding of visual media, this course offers a unique opportunity to enhance your creative toolkit and rethink how you approach your craft.– "The making of" of The Glass House (Boris Labbé, 2 Tus)

This module offers a guided introduction to modern social science youth research. At the center is the reading and discussion of Furlong's book 'Youth Studies: An Introduction'. References to current youth research enable topics such as youth unemployment, identity, and health to be discussed in depth and contextualized. In this semester, it is particularly interesting how digital media and generative AI shape youthful life worlds. Bibliography: Youth Studies: An Introduction by Andy Furlong, accessible online through the university library and further articles according to the overview at 'Termine' (see Syllabus on Moodle). Supplementary materials are available on Moodle.

Introduction to Music Philosophy This introductory course in music philosophy will trace major discoveries in music history such as the invention of musical notation, polyphony, and the tonal system. Besides, we will examine how the aesthetic experience of music has changed and to what extent music can be understood as an expression of the world. A focus will be on the music of the 20th and 21st centuries, with the development of atonal music, conceptual music, and musical postmodernism. Finally, the seminar will look into the current shift towards digital music, where music is no longer composed in the medium of musical notation, but in the medium of samples, or where composing is accomplished by AI programs. Together we will analyse classical texts of music aesthetics such as by Boetius, Wagner and Adorno. However, a substantial part of the seminar will be used to listen to musical examples and discuss them. You do not have to prepare for the seminars, but you have to write 10 short 400 word essays after every session on the topic of the last seminar. (There are no other exams). The seminar language is primarily German, but you can also speak English. Likewise, the short essays can be written in German as well as in English. All texts (in German and/or English) will be uploaded on Moodle. The Seminar will take place in the Maison des Sciences humaines: MSH E01 0127-081, Literature & Language Lab

The theme of this course is the handling of digital technologies for use in secondary education - including current developments in the field of Artificial Intelligence. Didactic potential and possible challenges will be discussed and critically examined. Alternating between practice and theory, participants will learn various digital tools and AI applications, test them in concrete lesson scenarios, and reflect on their application based on subject didactic concepts.

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Ancient and medieval philosophical theories could still define humanity. Even in modern times, these definitions were starting to pose problems and become outdated. Today, the emergence of robotics and other technologies capable of transforming humans themselves and their environment constitutes a profound transformation of the image of humanity and its environment. This course will focus on two issues: 1) First, theories of interaction between social robots and humanity; 2) And presenting different theories of human transformation (transhumanist, post-humanist theories) and their socio-political consequences as they are discussed today in philosophy. Bibliography Jean-Michel Bernier and Laurent Alexandre, Do Robots Fall in Love? , Dunod editions, Paris, 2006 Jean-Gabriel Ganascia, Artificial Intelligence. Towards a Programmed Domination? Le Cavalier Bleu, Paris, 2017 Robot Ethics 2.0. From Autonomous Cars to Artificial Intelligence (Ed.) Patrick Lin, Ryan Jenkins, Keith Abney, Oxford University Press, 2017 Allen Buchanan, Beyond Humanity. Ethics of Biomedical Enhancement, Oxford University Press, Oxford, 2011 Paul Dumouchel, Luisa Damiano, Living with Robots. Essay on Artificial Empathy, Seuil, Paris, 2016.

Digitalization has a huge impact on economic activities, business models and whole industries. This course explores the driving mechanisms behind the fundamental changes. We will discuss, for instance, why data is considered the new oil, how advances in machine learning and natural language processing change the organization of economic activity, and why big tech companies hire economists next to software engineers. We will draw on tools and insights from a number of fields, including industrial organization, labor economics, the economics of innovation, and applied econometrics. Topics covered include e-commerce, big data, artificial intelligence, app markets, gaming, and virtual currencies.

This is a continuous control lecture. It is a lecture based on individual project with a major part of the student workload being homework. Some mandatory individual tutoring sessions are planned throughout the semester to follow-up on the student projects work-in-progress. – Part 1 – Fundamental concepts in Green IT Introduction to Sustainability and Green IT Green Software Engineering – requirements of sustainability criteria Introduction to Sustainability standards and Assessment methods in the IT industry Green Software Engineering – design and production – Part 2 – Green Software Engineering in Practice Students Software Project Web application design review and improvement suggestions. Academic writing of a sustainability report – Part 3 – Research in Green IT Reading scientific work in the area of GreenIT. Student review of greenIT research article.

This course, developed in collaboration with the Ministry for Gender Equality and Diversity, is designed to enhance participants' understanding of AI and its ethical dimensions, equipping them to responsibly address the challenges and opportunities AI technologies present Through interactive tools, hands-on activities, and real-world problem-solving, participants will develop AI projects that incorporate ethical principles to create inclusive and responsible solutions Over three days, the course begins with a comprehensive introduction to AI, covering machine learning, deep learning, and their applications, as well as exploring the future of AI technology On the second day, participants explore AI tools to support project development, propose their own projects, and delve into AI ethics, stakeholder analysis, and thought-provoking ethical dilemmas such as self-driving cars The final day focuses on bias, fairness, explainable AI (XAI), and regulatory frameworks like the EU AI Act and GDPR, concluding with participants presenting their projects while considering the ethical principles learned throughout the course.

This engaging workshop invites participants to discover how Emotional Intelligence (EI) supports their professional resilience in a technology-driven world.Through real-world scenarios, group discussions, and a personal roadmap exercise, participants learn how to stay emotionally grounded, communicate clearly, and remain ethically aware in AI-integrated workplaces The session blends neuroscience-based concepts with practical emotion regulation tools to help participants thrive in the digital age.

This course offers crucial insights into the societal impact of AI and computing, especially how technology influences democratic functions and what responsible professional practice entails for IT professionals and managers You'll explore recurring key concepts in computer and AI ethics that illuminate today's challenging digital landscape Gain practical understanding of principal approaches to addressing complex issues, including the importance of supporting regulatory frameworks such as the evolving AI Act.

L’intelligence artificielle générative (GenAI) révolutionne notre façon de travailler et d’interagir avec la technologie Cependant, elle présente aussi des défis en matière de fiabilité, de confidentialité et de sécurité Cette formation vous propose une approche pragmatique et accessible pour comprendre comment ces technologies fonctionnent, pourquoi elles présentent certaines limitations et quels sont les risques associés à leur utilisation Nous commencerons par démontrer les capacités impressionnantes de la GenAI à travers des exemples concrets, avant d’analyser ses faiblesses et leurs causes technologiques Nous aborderons ensuite les aspects de gouvernance, les impacts juridiques et éthiques, ainsi que les menaces potentielles (désinformation, deep fakes, abus criminels) Enfin, nous vous fournirons des conseils pratiques pour une utilisation sécurisée et responsable.

L’intelligence artificielle (IA) représente l’un des domaines du monde numérique qui fait actuellement l’objet d’un développement important partout dans le monde Cela signifie que les citoyens sont et seront confrontés, dans leur vie professionnelle et personnelle, à des produits numériques qui incluent des composants d’intelligence artificielle.Cette formation aborde de façon claire, pratique et avec de nombreuses interactions avec les participants, les notions clefs de l’IA et de étudie son impacte sur l’emploi et l’éthique des sociétés.

LIST-led applied ethics/sustainability in AI workshop

Digital technologies have experienced exponential growth for over 20 years While they have contributed to major societal advancements and created opportunities for economic players, they also face growing environmental and social impact challenges To meet the ever-increasing demand more sustainably, IT professionals must address these issues First, they need to understand the sources and factors of these impacts Then, they should adopt a life-cycle thinking approach to enhance their creativity and support the design and deployment of more sustainable digital solutions in the coming years.

What is data analysis? Python basics, Built-in Data Structures, Functions, and Files NumPy basics: Arrays and Vectorized Computation Data Acquisition, Preparation and Management Data Visualization Time Series Introduction to Modeling Libraries in Python Data Analysis Examples

Interactive data visualization is a key paradigm of exploratory data analysis. It transforms latent data patterns into visual patterns, in order to support sensemaking and data-driven decision-making. Students will learn the value of visualization, principles, and skills for designing, programming, and evaluating data visualizations and demonstrate these skills through hands-on activities. Students will also be exposed to data visualization research problem areas and methods. Topics include: tree visualization; network visualization; tabular/multidimensional data visualization; text and document visualization; temporal visualization; maps and geospatial visualization; interaction techniques; and visualization evaluation. Students will also learn to implement Web-based data visualization pipelines using Javascript libraries on the frontend, such as D3.js and/or echarts, combined as needed with a Python backend for data analytics, for example leveraging Pandas, scikit-learn and networkX.

Chapter 1: Basics in NLP 6 hoursOverview: Origins and challenges of NLP-Need of NLP, python and NLTK for NLP, Text Wrangling and cleansing- Text cleansing, sentence splitter, tokenization, stemming, lemmatization, stop word removal, rare word removal, spell correction. Chapter 2: Text Preprocessing and Morphology 12 HoursCharacter Encoding, Word Segmentation, Sentence Segmentation, Introduction to Corpora, Corpora Analysis. Inflectional and Derivation Morphology, Morphological analysis and generation using Finite State Automata and Finite State transducer. Chapter 3: Language Modelling 12 HoursWords: Collocations- Frequency-Mean and Variance –Hypothesis testing: The t test, Hypothesis testing of differences, Pearson’s chi-square test, Likelihood ratios. Statistical Inference: n -gram Models over Sparse Data: Bins: Forming Equivalence Classes- N gram model – Statistical Estimators- Combining Estimators Chapter 4: POS Tagging and Text Classification 12 HoursParts of Speech Tagging – Tagging in NLP, Sequential tagger, N-gram tagger, Regex tagger, Brill tagger, Machine learning taggers-MEC, HMM, CRF, NER tagger, Types of learning techniques, Text Classification-Sampling, Naïve Bayes, Decision trees, Stochastic gradient descent, Support vector machine, Text clustering Chapter 5: Syntax and Semantics 12 HoursShallow Parsing and Chunking, Shallow Parsing with Conditional Random Fields (CRF), Lexical Semantics, WordNet, Thematic Roles, Semantic Role Labelling with CRFs. Statistical Alignment and Machine Translation, Text alignment, Word alignment, Information extraction, Text mining, Information Retrieval, NL interfaces, Sentimental Analysis, Question Answering Systems, Social network analysis. Chapter 6: Recent Trends and Applications of NLP 6 hoursRecent trends in NLP, Applications of NLP: Transforming text, Sentiment Analysis, Information retrieval, text summarization, Question and Answering, Automatic Summarization

The course Introduction to Computational Text Analysis and Text Interpretation (ICTATI) will present an overview of methods and tools for computational text analysis from areas such as corpus linguistics, sentiment analysis, topic modelling, and word embedding. Based on an application-oriented approach, it will illustrate how these techniques can assist the researcher in answering or formulating linguistic, historical or literary research questions. For instance, in analysing concept evolution in a collection of cultural history journals, main topics in parliamentary releases, specific vocabularies in corporate discourse or sentiment-based plot arcs in a set of novels. The course will provide the bases for understanding the functionality of various user interfaces and for developing simple text analysis applications via programming in R and Python. It will encourage the students to combine creativity and critical thinking and reflect on their experience when analysing texts through digital technologies. The course sessions will include short introductions of the tools and data to be studied, hands-on activities and discussions, which are intended to serve as examples of miniature projects and inspiration for the assignments. The final assessment will consist of individual projects. For this, the students will use tools and data of their choice from those seen in class, and their own explorative and interpretative scenarios in experimenting with these devices and describing their experience.

The course will cover two topics on advanced techniques: Cluster Analysis, Principal Component Analysis (PCA)

This course equips students with essential tools and expertise to thrive in the modern data-driven era. Its comprehensive program is divided into three building blocks, each focusing on critical aspects of data analysis. They enable students to master Python programming, machine learning techniques, and database management with SQL.The “Python” block helps students foster their programming skills. From basic syntax to advanced data manipulation, students explore how Python serves as the backbone of data analysis. The block dives into libraries like NumPy and Pandas to efficiently process and visualize data, before moving to more advanced analytical tasks.The “Machine Learning” block provides a comprehensive introduction to machine learning techniques, among which different text analysis tools like ChatGPT. This block illustrates with concrete examples how these techniques are relevant from both financial and computer science perspectives.The “SQL” block takes a chapter from the Data Science course in the Master in Mathematics (specialising in Financial Mathematics) to enable students to manipulate big databases. The SQL management system is particularly useful in handling and retrieving information from structured data.

Data Analysis is an interactive course packed with timely industry case studies and insights from a diversified group of practitioners who work with data on a daily basis. The course aims to equip students with basic analytical skills that would allow them to progress in their future careers. Furthermore, the course offers practitioners’ perspectives on job roles requiring data analysis that give students insights and advice into their first steps on the career ladder. The course cuts through the academic theories and scholarly areas that would not have an immediate benefit to students to focus on practical applications of the data analysis skills. The course uses Excel to walk students through real applications of data analysis and improve both their analytical and technical skills.

Definition and Process. Data Mining, Data Science, and the Big Data Hype. Data Quality and Preprocessing* Data Privacy and Security. Data and Information Visualization. Machine Learning for Clustering, Classification, Association Discovery, Sequential Pattern Analysis, and/or Time Series Analysis.

This interdisciplinary course offers a comprehensive exploration of how data science and machine learning techniques are applied to address environmental and climate challenges. Students will delve into both theoretical concepts and practical applications through engaging lectures and hands-on exercises. The course is designed to familiarize students with the types of data commonly used in environmental and climate science. This includes time series and geospatial data from environmental monitoring systems or simulations, as well as aerial imagery from drones and satellites. Students will learn the methodologies for acquiring, accessing, and analyzing these data types. They will also explore the insights typically sought from environmental data and understand the primary objectives of environmental data analytics. The curriculum provides an overview of essential concepts and algorithms in data analytics and machine learning that are commonly used in environmental data analytics. It covers a spectrum of topics from basic multivariate time series analysis to advanced techniques such as deep neural networks and physics-informed neural networks. Throughout the course, students will learn how these algorithms can be effectively implemented to answer prevalent questions in the field of environmental data analytics, preparing them to contribute to solutions for real-world environmental problems.

Alteryx Designer – Introduction is an excellent opportunity to discover how Alteryx can help solve even the most complex data challenges Alteryx is a data analytics platform designed to simplify and automate the process of preparing, analyzing, and sharing data insights It enables users—from business analysts to data scientists—to perform complex data operations with minimal coding, using a visual, drag-and-drop interface This one-day hands-on course is perfect for beginners, or anyone looking to explore and learn the basics of Alteryx It’s designed for all professionals who work with data, regardless of their analytics background If you’re comfortable using Excel, you’ll find it easy to pick up Alteryx Designer During the course, you’ll explore fundamental concepts and key tools for data preparation, blending, and workflow automation, including basic macros By the end, you’ll have the confidence and skills needed to start creating your own efficient data workflows.

In today's data-driven business landscape, your goal is to highlight the benefits of Business Intelligence, particularly in terms of data visualization Our training program is specifically designed to showcase the advantages of storytelling and visual analytics to your participants By choosing our training, you're opting for an approach that will transform how your organization perceives and utilizes data, providing a significant competitive edge Course highlights.Comprehensive curriculum covering data visualization principles and best practices.Focus on creating impactful, aesthetically appealing, and meaningful visualizations.Emphasis on user-centric design and audience engagement.Interactive sessions to reinforce learning and practical application.Comprehensive methodology – the Dashboard Model Canvas to collect the ‘right’ requirements and build impactful dashboards

Dans un monde axé sur la donnée, savoir interroger et visualiser l’information est une compétence précieuse Ce cours vous initiera aux bases du SQL pour extraire et manipuler des données efficacement Vous découvrirez ensuite comment exploiter Grafana pour créer des visualisations interactives et comprendre les tendances cachées derrière les chiffres.Ce programme s’adresse aux professionnels en reconversion, demandeurs d’emploi, étudiants ou toute personne souhaitant apprendre les bases de l'analyse de données sans prérequis technique.À travers des exercices pratiques et des études de cas concrets, vous développerez des compétences directement applicables dans divers secteurs (finance, marketing, IT, etc.).

Numbers alone don't inspire action Learn how to make your data speak This interactive session guides participants through the process of transforming data into powerful stories The curriculum covers visualization, narrative structure, and audience engagement, going beyond simple presentations Through case studies and hands-on practice, participants learn to create data-driven narratives that lead to meaningful change.

This intensive one-week course on Data Visualization provides participants with comprehensive knowledge and practical skills needed to create effective and insightful visualizations From understanding the fundamental principles to mastering advanced tools and techniques, this course covers every aspect of data visualization Participants will engage in hands-on exercises, real-world case studies, and interactive projects that will equip them to transform raw data into meaningful visual insights The course is designed for both beginners and those with some experience in data visualization.

With a focus on simplicity and practicality, this course is designed to equip participants with the necessary skills and knowledge to harness the full potential of Power BI as a powerful analytics tool.Throughout the course, participants will be introduced to the foundational concepts of data analysis, starting with data acquisition and preparation.As the course progresses, learners will explore the multitude of visualization options that Power BI offers They will discover how to create compelling and interactive reports and dashboards, combining different chart types and customizing visuals The training objectives are as follows: .Introduce Power BI Fundamentals: The training aims to provide a comprehensive introduction to Microsoft Power BI, ensuring participants understand its core features, capabilities, and potential as a data analysis and visualization tool.Develop Data Manipulation Skills: Participants will learn how to acquire data from various sources.Master Data Visualization Techniques: The training seeks to familiarize learners with a wide range of data visualization options available in Power BI Participants will gain the expertise to create engaging and interactive reports thanks to basic DAX measures and calculated columns.Understand Data Modeling and Relationships: Participants will grasp the concepts of data modeling and relationships within Power BI.Foster Data-Driven Decision-Making: Participants will learn how to identify relevant Key Performance Indicators (KPIs) and use data visualizations to drive better business decisions.Upon successful completion of this course, attendees will be equipped with the capability to explore, analyze, and visualize data effectively using Power BI.

In this course, participants will build upon their foundational knowledge of Power BI and delve deeper into its advanced features and functionalities The main objectives are: .Understanding and effectively utilizing Power BI's advanced data transformation capabilities to clean, shape, and prepare data for analysis.Exploring advanced visualization options, customizing visual elements, and using interactive features to enhance data storytelling.Learning how to create calculated columns, measures, and calculated tables using DAX to perform complex calculations and support data analysis needs.Applying best practices for data modeling and optimizing data relationships to improve report performance and accuracy.Understanding how to collaborate and share reports securely with others using Power BI's collaboration and sharing capabilities.Addressing real-world data analysis challenges and case studies to apply the acquired skills in practical scenarios.Staying up-to-date with the latest features and updates in Power BI, enabling participants to adapt and leverage new functionalities effectively.The combination of these objectives ensures that participants are equipped with a comprehensive skill set to become proficient Power BI users capable of conducting advanced data analysis and generating valuable insights for their organizations.By the end of the course, attendees will have a proficient understanding of the tool’s advanced features, empowering them to conduct in-depth data analysis and produce compelling, data-driven reports and visualizations.

This course focuses on enabling learners to become proficient Qlik Sense designers, capable of creating interactive visualizations, building data models, and generating insightful reports to gain valuable business insights from their data.Introduction to Qlik Sense: Understand the key features and capabilities of Qlik Sense as a data analysis and visualization tool.Data Loading and Data Modeling: Learn how to import and transform data from various sources, and create efficient data models for analysis.Creating Visualizations: Master the art of building interactive and visually appealing charts, graphs, and other data visualizations to represent insights effectively.Exploring Data Insights: Discover how to navigate and interact with data visualizations to derive meaningful insights and identify trends in the data.Designing Dashboards and Reports: Gain proficiency in creating interactive dashboards and reports that provide a holistic view of data analysis results.Collaboration and Sharing: Understand the methods for sharing and collaborating on Qlik Sense apps with other users and stakeholders.Best Practices in Qlik Sense Design: Learn industry best practices and design principles for creating effective and user-friendly Qlik Sense applications.

The Qlik Sense Data Architect course is a comprehensive program tailored for experienced data professionals who aspire to become proficient in architecting and optimizing data solutions within the Qlik Sense environment Participants will learn advanced data modeling techniques, data integration strategies, performance optimization, and security measures, equipping them with the skills to design and implement robust data architectures that support complex analytical requirements.Advanced Data Modeling: Participants will learn advanced data modeling techniques, including creating optimized data structures, handling complex relationships, and implementing associative data models to ensure efficient and accurate data analysis.Data Integration Strategies: Attendees will explore various data integration methods, including data loading from multiple sources, data blending, and data transformation, to consolidate diverse datasets into a cohesive and comprehensive data model.Performance Optimization: Learners will understand how to optimize data load scripts.Advanced Visualization: Participants will master advanced visualization techniques, including complex chart expressions, set analysis, and advanced calculations, to create sophisticated and interactive visualizations that provide deeper insights into the data.Security Implementation: Attendees will learn how to implement data security measures in Qlik Sense, including row-level security and data access restrictions, to ensure data privacy and compliance with data governance policies.Scalability and Deployment Best Practices: Participants will gain insights into scalable application design and deployment best practices.Real-world Use Cases: Through practical exercises and real-world use cases, participants will apply their knowledge to solve complex data architecture challenges, providing them with hands-on experience and practical skills.By the end of the training, participants will have achieved a high level of expertise in Qlik Sense data architecture, enabling them to design and implement efficient, secure, and scalable data solutions to empower data-driven decision-making and analytical processes within their organizations.

Le cours “Introduction aux Différents Outils de Reporting” est conçu pour initier les apprenants à la gamme d’outils de reporting disponibles sur le marché pour la visualisation des données à partir d’un entrepôt de données ou d’un datamart À travers une combinaison de conférences, d’ateliers pratiques et d’études de cas réels, les apprenants exploreront les fonctionnalités, les avantages et les applications pratiques de différents outils de reporting Ce cours vise à doter les participants des compétences nécessaires pour sélectionner et utiliser l’outil de reporting le plus approprié pour visualiser et interpréter les données, renforçant ainsi leur capacité à prendre des décisions éclairées basées sur des ensembles de données complexes À la fin du cours, les apprenants auront une compréhension approfondie de la manière de communiquer efficacement les informations tirées des données à travers des visualisations convaincantes, les préparant ainsi à des rôles nécessitant une expertise en intelligence d’affaires et en analyse de données.

This course takes the participants through the data analysis flow: from ingesting and transforming data, to basic analysis and visualization, using Microsoft Excel, reviewing the basic principles around Exploratory Data Analysis and Statistics, applied to univariate and bivariate data exploration.

This immersive course is designed for beginners and professionals seeking hands-on experience with Power BI You’ll start with foundational skills like data retrieval and transformation, then progress to advanced techniques such as DAX expressions, data modeling, and workspace management Through guided workshops, live demos, and a capstone project, you’ll learn to turn raw data into actionable insights while collaborating with peers By the end, you’ll confidently design end-to-end analytics solutions and deliver value through compelling visualizations.

Cette formation prépare les participants à l’examen de certification PL300 Elle couvre l’ensemble des compétences nécessaires pour collecter, transformer, modéliser et visualiser les données avec Power BI Les participants apprendront à concevoir des rapports interactifs, à appliquer les meilleures pratiques de gouvernance des données et à répondre aux besoins métiers à travers des analyses pertinentes.

Découvrez les fondamentaux de la Power Platform de Microsoft pour automatiser les processus, analyser les données et créer des applications sans code.Cette formation prépare les participants à l’examen de certification PL900 Elle couvre les composants clés de la Power Platform : Power Apps, Power Automate, Power BI, Power Virtual Agents et Dataverse Les participants apprendront à identifier les cas d’usage, à créer des solutions simples et à comprendre les capacités d’intégration de la plateforme.

Cette formation approfondie s’adresse aux agents de la fonction publique souhaitant maîtriser l’architecture des données dans Qlik Sense Elle couvre la connexion aux bases de données, le scripting avancé, la modélisation des données et les meilleures pratiques en matière de structuration des applications analytiques Les participants apprendront à concevoir un modèle de données robuste, à gérer les dimensions temporelles et la sécurité, ainsi qu’à optimiser les performances des applications Qlik Sense Veuillez apporter votre propre ordinateur portable avec votre accès Qlik Sense pour l'utiliser pendant le cours Veuillez également envoyer votre IAM à registrations@dlh.lu avec votre inscription.

Cette formation offre une introduction complète à la plateforme Qlik Sense au CTIE, en mettant l'accent sur la gestion des accès, la modélisation des données et la création de visualisations interactives Les participants apprendront à exploiter le modèle associatif de Qlik, à se connecter à diverses sources de données et à concevoir des tableaux de bord efficaces à l’aide de Qlik Sense et de la bibliothèque Vizlib La formation est dédiée aux agents de la fonction publique Veuillez apporter votre propre ordinateur portable avec votre accès Qlik Sense pour l'utiliser pendant le cours Veuillez également envoyer votre IAM à registrations@dlh.lu avec votre inscription.

This learning track encompasses the fundamental concepts and techniques for working with the complete Tableau Analytics Platform, which includes Tableau Desktop, Tableau Cloud, Tableau Server and Tableau Prep.The courses are designed for individuals who are new to Tableau or have some experience It caters to anyone who deals with data, regardless of their technical or analytical expertise.Whether you’re a data analyst, data scientist, or an individual just starting with Tableau (or even someone comfortable with Excel), this course will help you get up to speed You’ll learn essential skills for analyzing data in Tableau, including connecting to data sources, customizing data, creating visualizations, and building interactive dashboards.

This three-day course caters to individuals who are new to Tableau It is suitable for anyone who deals with data, irrespective of their technical or analytical expertise Whether you are a data analyst, data scientist or administrator who is just starting with Tableau or anyone comfortable with Excel can get up to speed with Tableau.This course covers the basic concepts and techniques working with Tableau Analytics Platform (Tableau Desktop, Tableau Server/Cloud), allowing you to analyse data and share insights effectively Tableau Desktop lets you create beautiful visualizations and dashboards with an intuitive, drag-and-drop interface that lets you easily analyse, see and understand your data.In this course, you will develop the essential skills necessary to analyse your data effectively using Tableau You will gain expertise in connecting to various data sources, whether they are live or local extracts Additionally, you will learn how to modify metadata, group fields, as well as employ sorting and filtering techniques Comparing subsets of data using sets will also be covered.Furthermore, this course will equip you with the ability to work with multiple data sources through relationships, joins, unions, and blends You will learn how to construct commonly used charts, such as bar and line charts, text tables, highlight tables, bar-in-bar charts, dual axis charts, scatter plots, and maps.Customizing data will be achieved by writing calculations and by using Table Calculations By utilizing advanced analytics features, you will be able to incorporate reference lines and showcase data distributions.Moreover, creating parameters will facilitate user exploration of data within the view To convey your insights effectively, you will learn how to construct interactive dashboards and stories that guide specific data analysis Finally, you will gain knowledge on sharing workbooks and publishing content on either Tableau Cloud, Tableau Server or Tableau Public.

This three-day course has been specifically designed for individuals who already possess a good level of proficiency in using Tableau and are eager to enhance their skills further.It is required that you have either completed the ‘Tableau Creator – Fundamentals’ course or have a minimum of three months of experience using Tableau.Prior to beginning this course, it is essential that you possess knowledge of how to establish a connection to data, create visualizations by utilizing fields on shelves and the marks card, filter, and sort data, as well as construct a dashboard It is important to note that this course caters to individuals who are using Tableau Desktop (compared to web authoring).You will learn how to effectively restructure data by using Data Interpreter, pivots, and splits Furthermore, you will gain a deeper understanding of data sources, including how to replace them, employ relationship and join calculations, and create unions using wildcard searches.Specifically focusing on challenges associated with blended data, you will receive valuable guidance on selecting the most appropriate method for combining data.To enhance your filtering skills, you will learn how to use advanced filtering techniques to analyse subsets of data and filter across multiple data sources within a dashboard Additionally, you will develop analytical capabilities by aggregating dimensions in calculated fields, customizing table calculations, and using level-of-detail (LOD) expressions to perform complex analysis.To achieve more advanced analytics, you will be introduced to creating advanced parameters and displaying data trends and forecasts.In relation to using geographical maps, you will discover the advantages of employing marks layers to present a greater amount of data Furthermore, you will become familiar with best practices for selecting and formatting map types, as well as using background layers.In terms of creating robust and customized dashboards, you will learn how to develop navigation buttons, use layout containers, and create personalized colour and shape palettes.Lastly, you will become proficient in managing the content you have published, which includes data sources, extracts You will gain knowledge on how to create subscriptions and set alerts on published views that you wish to track.

Pre-registration for the BI Academy is now available! Click on the button, and you will be redirected to a form to complete your application Your application will then be evaluated, and selected candidates will be contacted for the next steps in the selection process Pre-Registration is open until 14 February 2025 Applications will be processed after that date Submit a pre-registration request.BI Academy is a technical program dedicated to non-technical women and people from other underrepresented groups who are interested in making a career shift to IT.Whether you're coming from a different industry or looking to pivot within your current tech-adjacent role, this program equips you with the skills and confidence needed to excel in the world of Business Intelligence What You'll Learn Business Intelligence Fundamentals: Learn how to gather, analyze, and interpret data to make informed business decisions Hands-On Experience: Work on real-world projects to apply your learning and build a strong portfolio Soft Skills Empowerment: Enhance your communication, teamwork, and problem-solving abilities crucial in the IT industry Why Choose the BI Academy? Tailored for Non-Technical Backgrounds: No prior IT experience required We start from scratch and build your skills from the ground up Expert Instruction: Learn from experienced industry professionals passionate about helping women succeed in IT Supportive Community: Join a network of like-minded individuals, fostering collaboration and support throughout your journey Career Guidance: Access resources and mentorship to navigate job searches, interviews, and career advancement strategies Who Can Apply? .We are looking for ambitious and courageous non-technical women and people from other underrepresented groups from diverse backgrounds eager to transition to IT roles such as Business Analysts, Data Analysts, or Business Intelligence Specialists.

The Business Intelligence Training Path is a comprehensive and structured program designed to equip participants with the essential skills and knowledge required to excel in the field of Business Intelligence (BI) This training path covers a wide range of topics, starting from fundamental concepts and gradually progressing to advanced techniques, tools, and methodologies used in BI.

Découvrez comment utiliser Microsoft Fabric pour ingérer, interroger et traiter des flux de données en temps réel avec Synapse Real-Time Analytics et KQL.Voici un aperçu des sujets abordés: .Commencer avec les analyses en temps réel.Utiliser les flux d’événements en temps réel dans Microsoft Fabric.Interroger des données dans une base de données KQL dans Microsoft Fabric.L’analyse des flux de données en temps réel est une capacité essentielle pour toute solution moderne d’analyse de données Vous pouvez utiliser les capacités d’analyse en temps réel de Microsoft Fabric pour ingérer, interroger et traiter des flux de données.La deuxième partie du cours traite le flux d’évènement par une introduction à Microsoft Fabric Eventstream dans le cadre des analyses en temps réel (RTA).Dans la troisième partie nous fournissons une brève introduction aux requêtes KQL (Kusto Query Language) en utilisant Querysets et les principales différences entre KQL et T-SQL lors de l’utilisation de Querysets.

This training focuses on providing a comprehensive overview of the essential components that make up Business Intelligence (BI) and Data Analytics processes.Join our training program to explore the vital components of Business Intelligence (BI) and Data Analytics processes Gain insights into data collection, warehousing, visualization, analysis, reporting, performance management, and advanced analytics techniques.Thanks to a theoretical presentation illustrated by specific cases, this course will allow you to understand in one day, the key BI factors helping managers to drive informed decision-making.

IMPORTANT INFORMATION: Pre-registration for this course is now available Following your completion of the questionnaire linked below, you will be contacted by Digital Learning Hub to approve your registration Submit a pre-registration request.This module focuses on creating interactive web applications using R Shiny, teaching participants to build, customize, and deploy Shiny dashboards and user interfaces The module covers reactive programming concepts, creating dynamic user interfaces, and practical examples of dashboards By the end, participants will be able to develop fully functional, responsive Shiny applications for real-world business or data analysis purposes.

IMPORTANT INFORMATION: Pre-registration for this course is now available Following your completion of the questionnaire linked below, you will be contacted by Digital Learning Hub to approve your registration Submit a pre-registration request.In this module, participants will dive deeper into advanced features of R Shiny, such as session management, database integration, and using Shiny modules for more complex web applications By the end of the module, students will be able to develop, deploy, and manage Shiny applications that can handle larger datasets, integrate with databases, and provide enhanced user experience.

IMPORTANT INFORMATION: Pre-registration for this course is now available Following your completion of the questionnaire linked below, you will be contacted by Digital Learning Hub to approve your registration Submit a pre-registration request.This module teaches participants how to apply R in business contexts Participants will learn to automate business processes, generate reports and dashboards, and integrate R with other software tools to enhance decision-making The module also covers creating custom R functions and packages tailored for business applications, with a focus on real-world business use cases like reporting, analysis, and optimization.

This course takes the participants through the data analysis workflow tasks: from ingesting, cleaning and transforming data, to basic data analysis and visualization, all using Microsoft Excel.The course will cover logical functions for conditional decision-making, text processing functions for data cleaning, look-up functions to extract data from our dataset, and selection of charts based on the type of data and analysis desired.After completing the course you will have the basic tools needed to use Excel to begin your data analysis journey and will have the foundation needed to further develop your knowledge of Excel.

Historical Techniques of Information Processing Conversion of Bases and Calculations Boolean Logic General Structure and Functioning of Computers Introduction to Automata Presentation of UTMs (Universal Turing Machine) Introduction to Algorithmics Tools for Collaboration and Evolution of Languages Introduction to Computer Networks Introduction to Artificial Intelligence

The course will cover: Introduction to AI, Problem solving by searching, Knowledge representation and reasoning, Machine Learning, Deep learning, Reinforcement learning, Natural Language Processing (NLP), Traditional NLP, Large language models, Ethical considerations of AI

Weeks: 1: Introduction to Natural Language Processing, 2: Text Preprocessing, 3-5: Text Classification, 6: Vector Semantics, 7: Language Modeling, 8: Text Representation, 9: Introduction to Neural Networks, 10-12: ChatGPT prompting, 13-14: Projects Presentation and Exam preparation

In today's world, we often focus heavily on the "intelligence" behind Artificial Intelligence (AI) and overlook the significance of the "artificial" part leading to many misconceptions, myths, and conspiracy theories AI is frequently portrayed as having human-like thinking abilities or being an all-knowing force that can replace human judgment These ideas contribute to confusion and even fear surrounding AI In this course, we aim to demystify AI by separating fact from fiction, exploring how AI works, and highlighting the real-world applications shaping our daily lives Throughout the course, we will explore how AI operates, address common myths (e.g., "AI can think like a human" or "AI is inherently biased"), and uncover the ethical challenges that come with its integration into society By the end, participants will have a clearer understanding of AI’s capabilities and limitations, and how to navigate the reality of AI in today's tech-driven world unveiling both its opportunities and its potential risks.This course is designed to provide a realistic, accessible introduction to AI, helping participants move beyond fear and misconceptions to embrace the potential of this technology in their everyday lives.

In this journey, the learner gets an overview the history of AI, its ups and downs, a glimps behind the scene how it works and how an AI system can be used in daily life Some AI systems are analysed to provide precise illustrations on the opportunities and limitations of AI ChatGPT and Copilot are studied more in detail, how prompting helps to fully profite of this chatbot and how similar AI systems may help us in our professional work.

Dans cette démystification, l’apprenant obtient un aperçu de l’histoire de l’IA, de ses hauts et de ses bas, d’un aperçu des coulisses de son fonctionnement et de la manière dont un système d’IA peut être utilisé dans la vie quotidienne Certains systèmes d’IA sont analysés pour fournir des illustrations précises des opportunités et des limitations de l’IA ChatGPT est étudié plus en détail, comment les incitations aident à tirer pleinement profit de ce chatbot et comment des systèmes d’IA similaires peuvent nous aider dans notre travail professionnel.

Dans cette démystification, l’apprenant obtient un aperçu de l’histoire de l’IA, de ses hauts et de ses bas, d’un aperçu des coulisses de son fonctionnement et de la manière dont un système d’IA peut être utilisé dans la vie quotidienne Certains systèmes d’IA sont analysés pour fournir des illustrations précises des opportunités et des limitations de l’IA ChatGPT est étudié plus en détail, comment les incitations aident à tirer pleinement profit de ce chatbot et comment des systèmes d’IA similaires peuvent nous aider dans notre travail professionnel.

Dans cette démystification, l’apprenant obtient un aperçu de l’histoire de l’IA, de ses hauts et de ses bas, d’un aperçu des coulisses de son fonctionnement et de la manière dont un système d’IA peut être utilisé dans la vie quotidienne Certains systèmes d’IA sont analysés pour fournir des illustrations précises des opportunités et des limitations de l’IA ChatGPT est étudié plus en détail, comment les incitations aident à tirer pleinement profit de ce chatbot et comment des systèmes d’IA similaires peuvent nous aider dans notre travail professionnel.

In this journey, the learner gets an overview the history of AI, its ups and downs, a glimps behind the scene how it works and how an AI system can be used in daily life Some AI systems are analysed to provide precise illustrations on the opportunities and limitations of AI ChatGPT is studied more in detail, how prompting helps to fully profite of this chatbot and how similar AI systems may help us in our professional work.

In this journey, the learner gets an overview the history of AI, its ups and downs, a glimps behind the scene how it works and how an AI system can be used in daily life Some AI systems are analysed to provide precise illustrations on the opportunities and limitations of AI ChatGPT is studied more in detail, how prompting helps to fully profite of this chatbot and how similar AI systems may help us in our professional work.

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Dans le paysage économique en constante évolution d’aujourd’hui, la transformation digitale est indispensable pour rester compétitif Ce cours interactif de 3 heures offre une compréhension claire et pratique de la transformation digitale pour des professionnels de tous secteurs Les participants exploreront des leviers clés tels que le cloud computing, l’intelligence artificielle, le big data, la cybersécurité et l’Internet des objets, tout en découvrant comment ces technologies redéfinissent les industries, optimisent les opérations commerciales et améliorent l’expérience client Grâce à des études de cas réels, des activités pratiques et des discussions en groupe, le cours simplifie des concepts complexes et propose des stratégies concrètes pour naviguer dans le changement digital.

After a brief introduction to artificial intelligence (AI), its role in our daily lives and current scientific research on the subject, participants discover that this technology can be used to create works of art The various stages in the design of artificial intelligence are covered: data collection and preparation, training and finally evaluation of the AI Through presentations and hands-on activities, this workshop highlights the importance of data and how it can be used to train intelligent machines It also enables participants to form their own opinions on artificial intelligence and understand the impact on our society of new tools such as ChatGPT.

Ever wondered how a computer can answer any of your questions or generate images based on your descriptions? That’s the magic of Generative AI – a powerful new technology that allows computers to create things based on a text input But how does it work? In this workshop, we’ll go behind the scenes of programs like ChatGPT and discover how AI learns to understand language.We’ll journey into the brain of these AI programs You will understand how they encode the meaning of words, the way they connect words with each other, and how they learn the nuances of human language You will also learn that these AI “brains” need a lot of energy to learn and to think, without being perfect! We’ll see how sometimes they make mistakes, and how difficult it can be to spot such an error We’ll explore the applications of Generative AI, from translating languages and classifying text to creating art.However, we’ll also talk about the importance of using these tools responsibly and understanding their limitations and their impact on our planet We’ll learn that our own creativity and critical thinking are still very important So, are you ready to understand the power of Generative AI and learn how to create your own stories, art, and even magic? Join us on this exciting adventure!

A hands-on generative AI workshop for secondary students explaining how AI “brains” like ChatGPT and DeepL work, their energy use, limitations, and ethical impact

Hands-on workshop exploring AI through art and coding, emphasizing the role of datasets, creative thinking, and forming personal perspectives on AI

After a brief introduction to the state of art of computer vision, you will be given get a clear picture of what a computer actually sees How is a digital image created and processed by a computer? You will then dive into the basics of computer vision to understand how a computer could recognize a number in an image by looking for some of its characteristics (or features) This will lead to the use of neural networks in the field of computer vision A simple yet accurate explanation of neural networks such as perceptrons, convolutional neural networks and haar features will be provided.

The Interaction Design course follows the following core topics. Many of these combine lectures with studio time in the classroom.Introduction to Interaction DesignUnderstanding UsersNeeds, Requirements and Hierarchical Task AnalysisPrototypingConceptual DesignPhysical DesignEvaluation

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The focus is on digital innovations that disrupt established industries. We will do an opening module that highlights the fundamental change that all economies and almost every industry are going through. We will talk about new concepts of competition, platforms, artificial intelligence, cloud computing, Internet of Things and 3D-Printing. We will discuss Google, Amazon, Spotify, TripAdvisor and other business cases. In the process of these discussions, we will learn about disruptive digital innovations, why data is considered the new oil, scalability, network effects, technology strategy, and why intelligent digital assistants like Siri, Alexa and co might be predecessors of another industrial revolution.

This 12-hour “Journey Mapping” course is designed to provide participants with a thorough understanding of the journey mapping process, from research to actionable recommendations Participants will actively engage in field research, collecting real user data through interviews or observations, and translating these findings into actionable journey maps By focusing on real-world scenarios, the course ensures participants develop a strong ability to connect with user needs and deliver meaningful UX improvements.By the end of the course, participants will have practiced every stage of the journey mapping process, from identifying user pain points to creating a complete journey map and proposing actionable design recommendations.

Understanding user needs is critical, but effectively prioritising them while ensuring alignment with business goals is the key to impactful UX and product strategies.This 24-hour training program equips participants with the methodologies and frameworks to collect, analyse, and prioritise user needs while keeping business objectives at the core Participants will engage in hands-on exercises, role-playing scenarios, and group discussions to simulate real-world decision-making processes.Through user research, prioritisation frameworks, and stakeholder alignment techniques, attendees will leave the course with actionable skills that they can apply immediately in their projects.By the end of the course, participants will master structured prioritisation processes, enabling them to make informed UX and product decisions that drive both user satisfaction and business success.

Continuous Product Discovery, and particularly Teresa Torres' approach, are becoming increasingly popular in companies These methods promise to align product development more closely with actual user needs and enable teams to conduct user research independently and continuously However, there is a critical problem: Team members such as product owners, requirements engineers, or developers are often suddenly tasked with conducting user interviews, without having the necessary methodological foundation.This master class addresses precisely this challenge: We teach the basic skills and techniques you need to conduct high-quality user interviews as part of Continuous Discovery You will learn how to create structured interview guidelines, conduct interviews professionally and analyse your results efficiently – with the support of modern AI tools if required This will ensure that your discovery activities deliver truly valuable insights and not just superficial confirmations of your assumptions.Why you should not miss this master class.Methodological confidence: Gain confidence for your first own user interviews through sound methodology.Quality assurance: Ensure that your discovery activities deliver valid insights and are not distorted by methodological errors.Increasing efficiency: Learn how to organise the entire interview process - from preparation to analysis - in a time- and resource-efficient way.AI as support: Learn how modern AI tools can support you without compromising methodological quality.Direct applicability: Receive concrete templates, checklists and exercises that you can use immediately in your day-to-day work.Practice-orientated approach: Benefit from a balance between methodological principles and pragmatic approaches for day-to-day business.

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Introduction to machine learning security and offensive AI / Basic tool setup - Evasion attacks on computer vision systems, white-box and black-box threat models, transferability. Malware detection using AI and its pitfalls / Introduction to the end of year project. Dense task security with application to healthcare and autonomous driving. Tabular attacks in constrained domains, with application to financial systems. Attacks on NLP model / Escape game. Privacy of AI systems / Detection of generated content. Distribution drifts. Poisoning attacks. Attacks on biometrics systems. Certified robustness. Regulations and auditing.

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Information security 1.1. Public-key cryptography (Jean-Sebastien Coron) 1.1.1. Introduction to public-key cryptography: RSA encryption, signatures, and DH key exchange (recap) 1.1.2. Security models in cryptography. How to encrypt and sign securely with RSA. OAEP and PSS 1.1.3. Public-key infrastructure. Certificates, SSL protocol 1.2. Blockchain protocols (Sergiu Bursuc) 1.2.1. Basics of Bitcoin and of the crypto blockchain 1.2.2. Bitcoin / blockchain privacy and scalability 1.2.3. Multi-party computation 1.3. General cryptographic protocols (Peter Ryan) 1.3.1. Authenticated key-exchange (AKE) and password-based authenticated key-exchange (PAKE) 1.3.2. Zero-knowledge protocols 1.3.3. Authentication/identification protocols 1.3.4. Secure voting schemes 1.3.5. Quantum key establishment 1.4. System security and trusted computation (Marcus Völp) 1.4.1. Operating system security mechanisms and policies 1.4.2. Trusted execution and authenticated boot

This course is designed to provide participants with a comprehensive understanding of crypto-assets as a new asset class. Decentralisation technologies have garnered significant attention as a disruptive force that could challenge traditional structures and business models, particularly in the financial sector. In response to this transformative landscape, financial professionals must comprehend the potential, applications, and challenges these technologies pose. This course equips students with a critical perspective, essential to navigate the rapidly evolving financial landscape with confidence and make informed decisions in their professional pursuits.The first part of the course focuses on the fundamentals of crypto-assets and blockchain technology, from a financial markets perspective. Participants explore the core concepts of cryptocurrencies, blockchain, and distributed ledger technology, understanding the mechanics of hash functions, hash pointers, and consensus mechanisms to understand how to assess crypto-asset value and risks in crypto markets.By introducing a socio-technical perspective, the second part of the course delves deeper into decentralized finance and information security, thereby equipping financial professional with a holistic understanding of the interplay of the technical system as well as the social system. More concretely, students will learn how to reason about technology not only from a technical perspective (e.g., the building blocks of blockchain) but also from a social perspective (e.g., considering regulatory requirements).Additionally, the students will deepen their knowledge on cybersecurity management in decentralized finance, overviewing the corresponding role of artificial intelligence and privacy-enhancing technologies. They will further explore how blockchain can aid or hamper financial crime and integrity. Finally, they will be introduced to interdisciplinary design of central bank digital currencies, including the role of digital wallets.The purpose is to look beyond the blockchain hype by conducting an in-depth analysis of the factors influencing risks in decentralization technologies. This provides a balanced picture of the impact of blockchain-based technology stacks on end-users and other stakeholders.The third part of the course focuses on the institutional adoption of blockchain technology. Students explore the potential of smart contracts for investment banks in managing traditional investments. By studying a practical case, they will convert traditional financial products into smart-contract instruments.

Generative AI (GenAI) is transforming the way we work and interact with technology However, it also introduces challenges in terms of reliability, privacy, and security This training provides a practical and accessible approach to understanding how these technologies work, why they have certain limitations, and what risks come with their use.We will start by demonstrating GenAI’s impressive capabilities through real-world examples, before analyzing its weaknesses and their technological causes We will then explore governance aspects, legal and ethical impacts, and potential threats (disinformation, deep fakes, criminal misuse) Throughout the training, we will provide practical guidance on how to use AI securely and responsibly.

Research-led workshop on latest AI methods in security

1. Data Storage 1.1 Datatypes in SQL 1.2 Records 1.3 Blocks & Addressing 1.4 Data Modifications 1.5 Five-Minute Rule 2. Indexing 2.1 Primary Indexes vs. Secondary Indexes 2.2 B+ Trees 2.3 Hashing-based Indexes 2.4 Multi-Dimensional Indexes 3. Query Optimization 3.1 Physical Query Operators 3.2 Cost Models for Query Optimization 3.3 Join-Order Optimization & Dynamic Programming 4. Concurrency Control 4.1 Transactions 4.2 Transaction Manager 4.3 2-Phase Locking 4.4 Optimistic Concurrence Control 5. Datalog 5.1 Non-Recursive Datalog: Syntax & Interpretation 5.2 Recursive Datalog 6. Data Warehousing (OLAP vs. OLTP) 6.1 OLAP Schemas (Fact vs. Dimension Tables) 6.2 Data Cubes 6.3 Business Intelligence

The course starts by introducing the basic techniques to model both entities and their relationships and object-oriented representations in a relational database schema with respective normal forms. Next, students learn to formulate relational queries in the structured query language (SQL) and implement database constraints and triggers via PL/SQL and stored procedures. Moreover, we will have a look at advanced data-warehousing techniques for extracting, loading, and transforming (ETL) data, as well as for online analytical processing (OLAP) and online transaction processing (OLTP). Finally, an outlook complements the topics onto recent trends in NoSQL databases, which are highly relevant for implementing “Big Data” applications based on the Apache Hadoop, SparkSQL platforms. The course proposes a practical assignment, in which students implement a data-warehousing application by using the various platforms: Entity-Relationship Model (ERM) and Object Definition Language (ODL), Relational Data Model, Schema Design and Normal Forms, Relational Algebra, Structured Query Language (SQL), Constraints and Triggers, Stored Procedures in PL/SQL, Embedded SQL and JDBC, Data Warehousing, Extract-Transform-Load (ETL), Online Analytical Processing (OLAP) and Online Transaction Processing (OLTP), MapReduce Principle for Distributed Data Management using Apache Hadoop, NoSQL Databases using SparkSQL

The course is about classical and new techniques involved in the Big Data paradigm. The course combines two key dimensions of Big Data: Part I – Design and development for big data The first part of the course will discuss databases and distributed computing algorithms for hosting and processing very large amounts of data: Conceptual modeling (ER Model), Relational Model (Algebra and SQL), Schema design (ER to Relational) Files and Access methods (except DHT) Distributed Databases, Data Warehouse and C-Store NewSQL, Distributed Hash Tables, MapReduce, NoSQL The main goal of the first part is to spark discussion about the Trade Offs between classical data processing techniques and upcoming ones for big data. Part II – Data mining, classification and aggregation Objectives of Part II are to guarantee that students have a basic knowledge to automatically process and analyze huge amounts of data. In particular, the two main objectives are to 1) extract information from a data set and 2) transform and store the data in a convenient way for further use (data mining, classification and aggregation): Classification (random forest, …) Clustering (k-means, …) Outlier and anomaly detection (local outlier factor, …) Regression (least squares, …)

Database Security, Transactions, Concurrency Control, Data Warehousing, ETL, OLAP, OLTP, and Data Mining, MapReduce, NoSQL Databases

Course content 1. Information Management (IM) 1.1. Information Management Concepts 1.1.1. Information systems as socio-technical systems 1.1.2. Basic information storage and retrieval (IS&R) concepts. 1.1.3. Information capture and representation 1.1.4. Supporting human needs: searching, retrieving, linking, browsing, navigating 1.1.5. Information management applications 1.1.6. Declarative and navigational queries, use of links 1.1.7. Content analysis and indexing 1.1.8. Quality issues: reliability, scalability, efficiency, and effectiveness 1.2. Relational Databases 1.2.1. Mapping conceptual schema to a relational schema 1.2.2. Keys and foreign-keys, referential integrity 1.2.3. Relational algebra and relational calculus 1.2.4. Relational database design 1.2.5. Functional dependencies 1.2.6. Decomposition of a schema; lossless-join and dependency-preservation properties of a decomposition. 1.2.7. Candidate keys, super keys, and closure of a set of attributes 1.2.8. Normal forms (2NF, 3NF BCNF) 1.2.9. Multi-valued dependencies (4NF) 1.2.10. Join dependencies (PJNF, 5NF) 1.2.11. Representation theory 1.3. Query Languages 1.3.1. Overview of database languages 1.3.2. SQL (data definition, query formulation, update sublanguage, constraints, integrity) 1.3.3. Select-project-join queries. 1.3.4. Aggregations and group-by 1.3.5. Over-operator and sliding windows. 1.3.6. Subqueries in SQL 1.3.7. Constraints and triggers 1.3.8. Stored procedures and PL/SQL 1.3.9. QBE and 4th-generation environments 1.3.10. Different ways to invoke non-procedural queries in conventional languages. 1.3.11. Overview of other major query languages (e.g., XPATH, SPARQL)

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Data is a vital resource for organizations, as it supports business decisions and propels modernization efforts.Data reuse refers to using existing data to gain new insights or answer new questions, rather than collecting new data This practice is becoming increasingly important in today’s data-driven world, where the amount of data is growing at an unprecedented rate Data stewardship enables access to and re-use of data for public benefit in a systematic, sustainable, and responsible way.This course aims to establish a solid knowledge base for data stewardship It delves into the basics of data stewardship, including the skills of successful stewards, their functions, obligations, essential principles and practices Additionally, the course covers a fundamental understanding of data governance framework, policies, procedures, and its importance in every organization In this course, a team of experts will guide you through four segments After finishing, you’ll be equipped to apply your newly acquired skills and expand your learning journey in data stewardship, responsible data re-use, and effectively managing data.

This conceptual 3-day course dives into the foundations of BI and data modeling, emphasizing theory and best practices You’ll explore data architecture design, relational and dimensional modeling, ETL strategies, and governance frameworks Through case studies and collaborative exercises, you’ll learn to align data models with business goals and ensure scalability Perfect for analysts, architects, and decision-makers.

L’analyse des données est un processus qui permet d’analyser les performances d’une entreprise, qu’elles soient passées, présentes ou futures, et aide les décideurs à prendre des actions éclairées Dans le cadre de ce processus d’analyse, les organisations passent par trois étapes Premièrement, elles collectent des données à partir de systèmes informatiques opérationnels et de sources externes Deuxièmement, elles transforment et préparent ces données pour l’analyse Troisièmement, elles exécutent des requêtes sur ces données et créent des visualisations de données, c’est-à-dire des tableaux de bord Business Intelligence (BI) et des rapports pour mettre les résultats analytiques à la disposition des utilisateurs pour une prise de décision optimale L’objectif de cette activité étant de prendre les meilleures décisions est de permettre aux entreprises par exemple d’augmenter leurs revenus, d’améliorer leur efficacité opérationnelle et d’obtenir des avantages concurrentiels par rapport à la compétition.

When processing big data, performances are critical Without a proper use of data structures, processing rapidly becomes lengthy and can generate huge financial and technical overheads.This course begins with a general overview of computers and cloud architectures, to recall the base mechanisms and highlight the components that can hinder the performances The notion of complexity in software engineering is then introduced and used as a baseline for evaluations of the structures The remainder of the day is an alternation of theory and reminders on data structures and practice sessions.The aim is to raise awareness on which structure is most appropriate for which case, and that the technology used to process (software and hardware) has an impact on performances.

L’analyse des données est un processus qui permet d’analyser les performances d’une entreprise, qu’elles soient passées, présentes ou futures, et aide les décideurs à prendre des actions éclairées Dans le cadre de ce processus d’analyse, les organisations passent par trois étapes Premièrement, elles collectent des données à partir de systèmes informatiques opérationnels et de sources externes.Deuxièmement, elles transforment et préparent ces données pour l’analyse.Troisièmement, elles exécutent des requêtes sur ces données et créent des visualisations de données, c’est-à-dire des tableaux de bord Business Intelligence (BI) et des rapports pour mettre les résultats analytiques à la disposition des utilisateurs pour une prise de décision optimale L’objectif de cette activité étant de prendre les meilleures décisions est de permettre aux entreprises par exemple d’augmenter leurs revenus, d’améliorer leur efficacité opérationnelle et d’obtenir des avantages concurrentiels par rapport à la compétition.

Ce cours complet propose une introduction aux rôles essentiels du paysage de la donnée Après un aperçu général de l’écosystème data, les participants découvriront le cycle de vie complet des données, de la collecte à la visualisation, et comprendront comment les différents professionnels interviennent à chaque étape.Des explications détaillées seront fournies sur les principaux rôles tels que Data Architect, Data Engineer, Data Analyst, Data Scientist, Spécialiste BI, et Administrateur de Bases de Données (DBA), en mettant en lumière leurs missions principales, les compétences requises et les perspectives de carrière associées.Les participants recevront également des conseils sur les soft skills indispensables, les parcours de formation, les certifications pertinentes, ainsi que des ressources utiles pour réussir dans ces métiers En dernière partie du cours, ils prendront part à un atelier théorique immersif : chacun choisira un rôle data à explorer et simulera les tâches associées, pour expérimenter concrètement les prises de décisions et défis rencontrés par les professionnels du secteur.Ce cours est idéal pour toute personne souhaitant mieux comprendre les nombreuses opportunités offertes par le monde de la donnée et identifier plus clairement la voie qui lui correspond.

Ce module fait partie d’un Learning Track plus vaste qui compte 24 heures réparties sur deux modules Ce module en particulier est composé de deux parties Dans la première partie, les apprenants reçoivent un cours d’introduction sur l’émergence du “Data Analytics” Les apprenants découvriront et comprendront l’avènement des systèmes d’information décisionnels Dans la deuxième partie, il y aura des travaux dirigés sur la modélisation d’un Data warehouse Les apprenants comprendront les méthodes et les enjeux d’une modélisation multidimensionnelle.

Le cours “Introduction aux Outils ETL: Apprenez à connaître les outils ETL disponibles sur le marché pour alimenter un entrepôt de données/datamart ” est conçu pour familiariser les apprenants avec les technologies essentielles de l’Extraction, de la Transformation et du Chargement (ETL), qui sont fondamentales dans la construction d’un entrepôt de données ou d’un datamart À travers des conférences théoriques, des démonstrations pratiques et des exercices appliqués, ce cours offre un aperçu complet des outils ETL disponibles sur le marché, en mettant l’accent sur leurs fonctionnalités, leurs caractéristiques uniques et leurs applications réelles dans le domaine de l’intelligence d’affaires Les participants apprendront comment ces outils aident à intégrer les données provenant de différentes sources, à les transformer selon des besoins spécifiques, et à les charger dans des entrepôts de données pour une analyse approfondie Ce programme est parfait pour ceux qui cherchent à maîtriser les pratiques modernes de gestion des données et à améliorer les processus de prise de décision dans un environnement professionnel.

Le cours “Introduction à l’émergence de l’analyse de données: Comprendre l’avènement des systèmes d’information d’aide à la décision” explore l’histoire et l’importance de l’analyse de données dans les systèmes d’information d’aide à la décision, retracant son évolution depuis le traitement de données de base jusqu’à l’utilisation sophistiquée des mégadonnées et de l’IA dans les pratiques commerciales contemporaines À travers un mélange d’aperçus théoriques, d’études de cas pratiques et de projets pratiques, les étudiants exploreront le développement et l’application des systèmes d’aide à la décision, acquérant une maîtrise des techniques d’analyse de pointe telles que l’apprentissage automatique et l’analyse prédictive Ce programme complet équipe les apprenants des compétences nécessaires pour analyser les données de manière critique, développer des idées exploitables et exploiter les systèmes d’information pour une prise de décision éclairée, les préparant à des carrières réussies dans le domaine dynamique de l’analyse de données et de la Business Intelligence.

Ce module fait partie d’un Learning Track plus vaste qui compte 24 heures réparties sur deux modules Ce module en particulier est composé de trois parties de travaux pratiques Dans la première partie, les apprenants réalisent un pipeline data avec Talend et dans la deuxième partie, ils réalisent un pipeline avec Datastage Enfin, dans la troisième partie, ils créent un pipeline data avec Azure Data Factory.

The course is a two-day intensive program on modelling information, focusing on structured processes of critical thinking and abstraction to extract core content from text, diagrams, and data, and structuring them systematically by availing of a suitable modelling language Different scenarios, such as constructive topic exploration, theory representation, learning from textbooks, and database design use modelling languages of increasing complexity These languages will be introduced and their respective suitability for various knowledge-intensive tasks will be thoroughly assessed over the two days.

Apprenez à transformer les données en actifs analytiques réutilisables en utilisant les composants de Microsoft Fabric Ce programme de formation vous guidera à travers : .La création et la gestion d’un lakehouse grâce à Microsoft Fabric.Le processus d’ingestion et de transformation des données.La gestion des entrepôts de données.La création et l’optimisation de modèles sémantiques.La supervision du cycle de vie du développement analytique.Le choix et l’utilisation d’un framework de modèle Power BI.La surveillance et l’analyse des données en temps réel via Power BI.Ce cours offre une approche complète pour acquérir les compétences nécessaires afin de déployer avec succès une architecture analytique moderne.

Ce cours est conçu pour développer vos compétences de base en ingénierie des données sur Microsoft Fabric, en vous concentrant sur le concept Lakehouse Il s’adresse principalement aux professionnels des données qui sont familiers avec la modélisation des données, l’extraction et l’analyse Il est conçu pour les professionnels qui souhaitent acquérir des connaissances sur l’architecture des lakehouses, la plateforme Microsoft Fabric et comment utiliser des analyses de bout en bout en utilisant ces technologies.

Explorez le processus d’entreposage de données et apprenez à charger, surveiller et interroger un entrepôt dans Microsoft Fabric.Ce programme de formation couvre les bases de l’utilisation des entrepôts de données dans Microsoft Fabric, une plateforme complète pour les données et l’analyse Voici un aperçu des sujets abordés : .Démarrer avec les entrepôts de données : Apprenez les fondamentaux des entrepôts de données, comprenez comment les entrepôts de données fonctionnent dans Fabric, et apprenez à interroger, transformer, sécuriser et surveiller vos données.Charger des données dans un entrepôt de données : Explorez les stratégies de chargement de données, utilisez des pipelines de données et T-SQL pour charger et transformer des données dans votre entrepôt.Interroger un entrepôt de données : Maîtrisez l’utilisation de l’éditeur de requêtes SQL et explorez les outils de requêtes visuelles et clients pour interroger efficacement votre entrepôt.Surveiller un entrepôt de données : Apprenez l’importance de surveiller votre entrepôt de données en examinant les métriques de capacité, l’activité actuelle et les requêtes pour assurer un fonctionnement optimal.Ce programme fournira aux participants les connaissances et les compétences nécessaires pour utiliser efficacement les entrepôts de données dans Microsoft Fabric, renforçant ainsi leur capacité à gérer et analyser les données de manière optimale dans un contexte professionnel.