M2-ASCI

Course 6 : Introduction to causality

• Lecture 6 : Lecture 6.pdf.  

• Notebook of Lecture 6 : Lecture6.ipynb. Data : hotel_bookings.csv

• Lab 6 : Lab6pdf. Data : ihdp_npci.csv 

Course 5: Interpretability

• Lecture 5: Lecture 5.pdf.  

• Lab 5 : Lab5.pdf.  Datasets : winequality.csv. 

• Notebook of Lab5 : Lab5.ipynb

Course 4: Introduction to neural networks

• Lecture 4 : Lecture 4.pdf.  Notebook : Lecture4.ipynb

• Lab 4 : Lab4.pdf. Data : training_data.csv, test_data.csv. 

Course 3: Decision Trees, Ensemble methods, Quantile regression

• Lecture 3 Lecture 3.pdf.  

• Notebook of Lecture 3 : Lecture3.ipynb. 

• Lab 3 : Lab3.pdf. Datasets : FinancialDistress.csv

Course 2: Beyond OLS

• Lecture 2: Lecture 2.pdf.  Notebook : Lecture2.ipynb 

• Lab 2: Lab2.pdf. Dataset hitters.csv. 

Course 1 : Basics on Random Variables with Python

• First lectures on Random Variables : CM1a.pdf, CM1b.pdf. Notebook : Lecture1.ipynb

• First lab : Lab1.pdf. Notebooks :  Ex1.ipynb, Ex2_Ex3.ipynb

Basics on Python

• Introduction to Python : IntroductionPython.ipynb 

• Introduction to the Numpy library: IntroductionNumpy.ipynb

• Introduction to the Pandas library : IntroductionPandas.ipynb. The dataset president_heights.csv

• Complements to the Pandas library : ComplementsPandas.ipynb

Introduction to Python for data science : Tutorial or Other Tutorial

Install google colab : google colab

Use google colab : Tutorial

Import data on colab : Tutorial