For comprehensive training in Data Science and Data Analytics, modules can be organized in a progressive manner, from beginner to advanced.
Block 1: Fundamentals
1. Introduction to Data Science and Data Analytics
2. Mathematics for data science
Descriptive statistics
Probabilities
Linear algebra
3. Introduction to databases
Block 2: Programming
4. Python for data analysis
5. R for statistics (optional but useful)
6. SQL for database query
7. Git and GitHub (version management)
Block 3: Data Analysis
8. Data cleaning and preparation
9. Exploratory Data Analysis (EDA)
10. Data visualization with Excel, Power BI, Tableau and Python (Matplotlib, Plotly)
Block 4: Applied statistics
11. Inferential statistics
12. Linear regression and logistics
13. Time series
14. Hypothesis tests
Block 5: Machine Learning
15. Introduction to Machine Learning
16. Supervised learning
17. Unsupervised learning
18. Model evaluation and optimization
Block 6: Big Data and Cloud
19. Introduction to Big Data
20. Apache Spark
21. Hadoop (notations)
22. Introduction to the Cloud (AWS, Azure or Google Cloud)
Block 7: Artificial Intelligence
23. Deep Learning
24. Automatic natural language processing (NLP)
25. Computer Vision
26. IA general (LLM, ChatGPT, etc.)
Block 8: Business Applications
27. Data Analytics for Finance
28. Data Analytics for Health
29. Data Analytics for Agriculture
30. Business Intelligence (BI)
Block 9: Reporting results
31. Creating interactive dashboards
32. Storytelling with data
33. Preparation of analytical reports
Block 10: Professional project
34. Project to complete training on actual data
35. Construction of portfolio
36. Preparation for certifications and job interviews
Software and tools to master
Excel (advanced)
SQL
Python
R
Power BI
Table
Jupyter Notebook
Google Colab
GitHub
VS Code
For 6 months of vocational training, these modules can be divided into three levels:
Level 1 (beginner): Excel, Statistics, SQL, Basic Python.
Level 2 (Intermediary): data analysis, Power BI, Table, regression, time series.
Level 3 (Advanced): Machine Learning, Deep Learning, Big Data, Cloud, Professional Project.
This progress allows participants to acquire skills that are directly exploitable in the company while building a solid foundation for advancing to Advanced Data Science.