Introduction to Data Science 2
Teaching
Class hours
- Lectures : Friday, 1:30 pm to 3:00 pm (apr-mai)
- Practical (group 3) : Wednesday, 1:30 to 3:30 (apr-mai)
- Practical (groups 1 et 2) : Thursday, 1:30 to 3:30 (apr-mai)
Prerequisites
Basic probability and statistics, basic algebra, programming experience
Preparing to
Introduction to Machine Learning
Plan
| Part | Course | Practical |
|---|---|---|
| 1 | Introduction & Definitions | |
| 2 | Exploratory Analysis | Python & JupyterLab |
| 3 | Exploratory Analysis | NumPy & Matplotlib |
| 4 | Machine Learning | Pandas |
| 5 | Machine Learning | Scikit-Learn |
| 6 | Project | Project |
| 7 | Project | Project |
Assessments
100% Continuous evaluation
- Session 1: 30% Quizzes + 70% Project
- Session 2: 100% Improved Project