Mathematics for Data Science
Welcome
Data Science, more specifically Machine learning, is powered by four critical concepts: Statistics, Linear Algebra, Probability, and Calculus. While statistical concepts are the core part of every model, calculus helps us learn and optimize a model. Linear algebra comes exceptionally handy when we are dealing with a huge dataset and probability helps in predicting the livelihood of events that will be occurring. These are the mathematical concepts that you will encounter in your data science and machine learning career quite frequently.
In this course we are going to concentrate on Statistics & Probability, more specifically Statistical Inference.
This course is destined for students of majors Data & Artificial Intelligence and Data Engineering at EFREI Paris engineering school.
Schedule
| Session | Topic | Slides | Exercises sheet |
|---|---|---|---|
| 1 | Introduction to Probability theory, Random Variables |
📖 | ✍️ |
| 2 | Continuous random variables, Introduction to Statistical Inference, Sampling and Limit Theorems |
📖 and 📖 | ✍️ |
| 3 | Point Estimation | 📖 | ✍️ |
| 4 | Confidence Intervals | 📖 | ✍️ |
| 5 | Hypothesis Tests: one sample | 📖 | ✍️ |
| 6 | Hypothesis Tests: two samples | 📖 | ✍️ |
| 7 | Chi-Squared Tests + Exam | 📖 | ✍️ |