Fundamentals of Decision Making🔗
| Course title | Coordinator | Volume | Content |
|---|---|---|---|
| Data Engineering Fundamentals | Christophe Huet | 10h | Capture the Flag: Linux, Git, SSH, Python and NumPy |
| Statistical Models for Decision Making | Benjamin Bobbia, Laurent Risser | 24h | Descriptive Statistics, Probability, Linear Models, Regression Analysis, Mixed-Effects Models |
| Optimisation for Decision Making | Zoé Krug | 13h | Combinatorial, Stochastic, and Continuous Optimisation, Automatic Differentiation |
This class is the introductory foundation of the program, providing the computing, statistical, and mathematical tools needed for data-driven decision making. It is divided into three modules:
Data Engineering Fundamentals🔗
A Capture the Flag introduction to the data scientist's toolbox: the Linux command line, Git, SSH, and Python with NumPy.
| Schedule | ||
|---|---|---|
| 03/09 AM | Linux, bash | 3h |
| 04/09 AM | SSH, Git | 4h |
| 04/09 PM | Python, Numpy | 3h |
Statistical Models for Decision Making —🔗
descriptive statistics, probability, and linear and mixed regression models.
| Schedule | ||
|---|---|---|
| 07/09 AM | Descriptive Statistics | 3h |
| 08/09 AM | Probabilities | 3h |
| 09/09 AM | Linear models | 3h |
| 09/09 PM | Simple Linear regression | 3h |
| 15/09 PM | Multiple Linear regression | 4h |
| 23/09 PM | Mixed-effects models | 3h |
| 28/09 PM | Written Exam | 1h |
| 30/09 PM | TBD | 2h |
| 07/10 PM | TBD | 2h |
Optimisation for Decision Making🔗
combinatorial, stochastic, and continuous optimisation, with a practical on automatic differentiation.
| Schedule | ||
|---|---|---|
| 14/09 AM | Continuous Optimization | 3h30 |
| 16/09 AM | Combinatorial Optimization | 1h30 |
| 21/09 AM | Combinatorial Optimization | 2h |
| 22/09 PM | Stochastic Optimization | 2h |
| 28/09 PM | Written Exam | 1h |
| 12/10 PM | Auto-differentiation | 3h |