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134 course(s) shown
Write well-structured professional documents: styles, headers and footers, tables, table of contents, templates, track changes and PDF export.
Learn to analyze sales data, calculate KPIs, build reports and automate repetitive tasks in Excel, with exercises taken from real office work.
Prepare invoices, expense tracking, financial tables, reconciliations and monthly reports with formulas, pivot tables and indicators.
Manage lists, schedules, tracking sheets, staff data and reports, and build simple dashboards for your office tasks.
Structure a presentation, design clear slides, insert charts and tables, use speaker notes and present with confidence.
Word, Excel, PowerPoint and email applied to real work situations: write, calculate, present and communicate effectively.
Write clear emails, organize your inbox, manage attachments and exchanges, and adopt good communication habits at work.
Use Gmail, Drive, Docs, Sheets and Calendar to work as a team: sharing, comments, collaborative documents and spreadsheets.
Organize and automate your routine tasks: letters, tracking tables, schedules, document filing and internal communication.
Word correspondence and documents, case and deadline tracking with Excel, email management, reports and the use of AI while respecting confidentiality.
Excel, reports, client data, financial tables, email and generative AI applied to the daily work of an accounting firm or department.
Learn how to use generative AI to write professional emails, summarize documents, create reports, improve presentations and automate repetitive office tasks.
Understand what language models are, what ChatGPT, Gemini and AI assistants can do, their limits and the rules of responsible use.
Choose and use AI assistants for your professional tasks: writing, brainstorming, summarizing and organizing work.
Write effective instructions: context, task, constraints, examples, output format, iteration and evaluating the quality of answers.
Apply AI to your documents and spreadsheets: emails, meeting notes, summaries, presentations, Excel help and repetitive tasks.
Emails, reports, meeting notes, document summaries, translation and task extraction, to save time every day.
Document analysis and summaries, drafting help, research and correspondence, with systematic verification and respect for confidentiality.
Reports, help with data analysis and Excel, summaries, client communication and task automation, with checking of the results.
Analyses, reports, presentations, communication and decision support: use AI to save time while staying in control.
Use AI to understand, revise, organize your work and write, while respecting academic integrity.
Literature workflows, understanding papers, brainstorming, help with writing, code and data analysis, with source verification.
Use AI assistants to write, explain, test and debug code, while keeping the review and responsibility for the result.
Hallucinations, verifying information, personal data, confidential information, intellectual property and professional responsibility.
Core concepts: structured data, CSV and Excel files, data quality, missing values, basic statistics and first visualizations.
Clean your data, use formulas, tables and pivot tables, and build charts for a first analysis.
Query a database: SELECT, WHERE, ORDER BY, GROUP BY, aggregations, joins and subqueries, with the basics of databases.
Understand the role of indicators, data sources and dashboards in steering a business.
Import Excel or CSV files, clean them with Power Query, build a data model and create an interactive Power BI dashboard from realistic business data.
Design a solid data model (relationships, star schema) and write DAX measures for your indicators.
Import, clean, transform and combine data sources in a reproducible way before analysis.
Combine Excel and Power BI to prepare data, track indicators and produce management reports.
A complete path to become operational in data analysis: clean data in Excel, query it with SQL, model it, build a Power BI dashboard and draw useful business conclusions.
Choose the right chart, structure a dashboard and present your results in a readable, honest way.
Descriptive statistics, distributions, correlations and inference concepts useful for interpreting data.
Learn programming with Python: variables, conditions, loops, functions, files and first useful programs, with no prior knowledge.
Learn to import CSV and Excel files, clean data with Pandas, analyze it and produce charts with Matplotlib, in Jupyter notebooks.
Handle numerical arrays with NumPy: vectorized calculations, indexing, statistics and processing of measurement data.
Import, clean, filter, group and merge datasets with Pandas, from CSV and Excel files.
Create clear charts and figures for your analyses and publications: curves, histograms, scatter plots, formatting.
Work in notebooks: run code, document an analysis, share and reproduce your results.
Statistics, tests, optimization, interpolation and signal processing with SciPy, on examples from the sciences.
Analyze geological data: samples, coordinates, statistics and simple maps, with cleaning and visualization.
Analyze experimental and biological data: cleaning, statistics, group comparisons and visualization.
Analyze environmental measurement series: cleaning, statistics, trends and visualization.
Automate your calculations and the analysis of test and measurement data: import, processing, statistics and charts.
Import laboratory or field data, clean it, handle missing values, run statistics and produce reproducible figures with Python, Pandas, SciPy and Matplotlib.
Discover the principles of machine learning: data preparation, simple models, evaluation and interpretation with Python.
Find the relevant scientific literature with Google Scholar, ScienceDirect, Scopus, Web of Science, PubMed and IEEE Xplore: search operators, citation tracking and related articles.
Master search operators, alerts, citation tracking and the discovery of related articles.
Search and compare the literature in the major academic databases: advanced queries, filters, indicators and exports.
Build effective queries in PubMed: keywords, MeSH terms, filters and reference export.
Find papers, standards and conference proceedings in engineering and computing: advanced search, filters and export.
Organize your bibliography: import references and PDFs, sort them, insert citations in Word or LibreOffice and automatically generate your bibliography in the required style.
Organize your PDFs and references with Mendeley and insert citations and bibliographies in your documents.
Understand citation styles and set up a reliable method to manage your references from the start to the end of a piece of work.
Write papers, dissertations and theses with LaTeX and Overleaf: structure, equations, tables, figures, references and BibTeX bibliography.
Structure a dissertation or thesis in Overleaf: chapters, figures, tables, references and templates.
Create and maintain your .bib files, choose a style and produce a clean bibliography in LaTeX.
Organize your writing, notes and sources with digital tools suited to a dissertation, a paper or a thesis.
Chain literature search, reference management, analysis and writing into a coherent workflow.
Data, code, documentation, Git and notebooks: make your analyses reproducible and shareable.
Use AI to explore the literature, summarize papers and prepare texts, while verifying sources and respecting academic integrity.
A track that follows the real work of a researcher: find the literature, manage references with Zotero or Mendeley, analyze data with Python, then write with LaTeX and Overleaf up to the scientific paper.
Learn to program in Java from scratch: variables, conditions, loops, methods, classes, exceptions and first console applications, writing code in every session.
Classes, inheritance, interfaces, polymorphism and encapsulation: design clear, reusable programs.
Generics, advanced collections, streams, concurrency, reflection and good practice for writing robust code.
The recent language features: records, sealed classes, pattern matching expressions, text blocks.
Lists, sets, maps, comparators and generics: choosing and using the right data structure.
Lambdas, functional interfaces and Streams to process data concisely.
Threads, executors, synchronization and concurrent structures: write reliable parallel programs.
Connect a Java application to a relational database: queries, transactions and good data-access practice.
Design and develop REST APIs: resources, HTTP codes, JSON formats and documentation.
Dependency injection, the Spring container, configuration and the lifecycle of beans.
Build a professional web application with Spring Boot: REST API, database access with JPA, validation, error handling and automated tests.
Build REST APIs with Spring Boot: controllers, DTOs, validation, error handling and documentation.
Model your entities, manage relationships, write queries and optimize data access.
Authentication, authorization, securing APIs and security good practice for Spring applications.
Design a microservices architecture: decomposition, service-to-service communication, configuration and resilience.
Unit, integration and API tests for your Spring Boot applications.
Write readable, maintainable code: design principles, refactoring and common design patterns.
Choose and document an architecture: layers, modularity, hexagonal architecture and technical trade-offs.
Build structured, well-presented web pages: semantic HTML, CSS and layout.
Variables, functions, the DOM and events: make your pages interactive.
Static typing, interfaces and generics to write safer JavaScript applications.
Components, templates, services, routing and forms: build an Angular application.
State management, RxJS, performance, testing and architecture of real-size Angular applications.
Build a complete application: an Angular frontend connected to a Spring Boot API.
Consume a REST API from a frontend: requests, error handling, authentication and code organization.
Design interfaces that adapt to phones, tablets and computers.
Track the history of your projects and collaborate with Git and GitHub: commits, branches, merges, conflicts and pull requests, for code as well as research documents.
GitLab repositories, CI/CD pipelines, runners, variables and automated deployment.
Containers, images, volumes and networks: run and package applications with Docker.
Describe and run multi-container applications with Docker Compose.
Pods, deployments, services and configuration: the essential concepts to deploy on Kubernetes.
Containerize an application with Docker and automate its build, tests and delivery with a CI/CD pipeline.
Automate tests, builds and deployments with GitHub Actions workflows.
Service models (IaaS, PaaS, SaaS), storage, networking, security and costs: the basics of the cloud.
The essential Google Cloud services: compute, storage, networking and access management.
The essential AWS services: compute, storage, networking and identity management.
The essential Microsoft Azure services: compute, storage, networking and identity management.
Deploy and run a Spring Boot application on a cloud platform: configuration, containers and monitoring.
Describe and reproduce an infrastructure with code: principles, tools and good practice.
Prepare for the Java OCP exam: review of the exam topics, practice questions, analysis of frequent pitfalls and a revision method. The academy prepares you for the exam; it does not issue the certification.
Prepare for the ISTQB Foundation Level exam: software testing fundamentals, test design techniques, static testing and test management. The academy prepares you for the exam; it does not issue the certification.
Practice for AWS exams: topic review, sample questions and a revision method. The academy does not issue the certification.
Practice for Microsoft Azure exams: topic review, sample questions and a revision method. The academy does not issue the certification.
Practice for Google Cloud exams: topic review, sample questions and a revision method. The academy does not issue the certification.
Practice for GitLab certification exams: topic review, sample questions and a revision method. The academy does not issue the certification.
Practice for Docker certification exams: topic review, sample questions and a revision method. The academy does not issue the certification.
Practice for Kubernetes certification exams: hands-on exercises, topic review and a revision method. The academy does not issue the certification.
Practice for DevOps certification exams: topic review, sample questions and a revision method. The academy does not issue the certification.
Common threats, protecting accounts and data, digital hygiene and security good practice.
Understand common attacks on web applications and how to protect against them.
The ten most widespread web security risks, with examples and countermeasures.
Input validation, secrets management, dependencies and common mistakes to avoid in Java.
Sessions, tokens, OAuth 2.0 and access control: design safe identity and permissions.
Encryption, hashing, signatures and certificates: understand and use cryptography correctly.
Command line, files, permissions, processes and basic administration.
Use Linux every day: shell, scripts, package management and development tools.
IP addressing, DNS, HTTP, firewalls and network troubleshooting.
Apply authentication, authorization, validation and hardening to a Spring Boot application.
Probability, inference, tests and regression: the statistical foundations of machine learning.
Supervised and unsupervised learning, overfitting, evaluation: the key concepts.
Build and evaluate machine learning models with Python.
Pipelines, models, cross-validation and hyperparameter tuning with scikit-learn.
Create and select relevant variables to improve your models.
Deal with missing values, outliers and inconsistent formats before modeling.
Metrics, validation, bias and interpretation of a model's results.
Neural networks, training and applications, with practical examples.
How large language models work and how to integrate them into applications.
Call AI APIs from your applications: requests, costs, errors and security.
Build applications that answer from your documents: chunking, retrieval and generation.
Design AI-based agents and automation workflows: tools, control and limits.
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