Part II : A gentle introduction to FairML
Course 8 : An example of bias mitigation strategy
Lab8 : Lab8.ipynb. Dataset : crime.csv
The LFR function of aif360. More details in this tutorial
More about reweighing. Also here
More details about preprocessing to correct bias. See also here
Course 7 : Group fairness
Course 7 : Lecture7a.pdf
Associated notebooks : Lecture7a.ipynb, Lecture7b.ipynb,
Dataset : adult.csv
Course 6 : Bias on data
Course : Lecture6.pdf.
Associated notebooks : Lecture6a.ipynb, Lecture6b.ipynb, Lecture6c.ipynb.
Data : compas-two-years.csv, model.csv, UCBa.csv
Course 5 : Introduction to FairML
Course : Lecture 5.pdf
Data Analysis 2
Notebook : DataAnalysis2.ipynb
Dataset : bitcoin.csv
Data Analysis 1
Part I : Stochastic Finance
Course 4 : Risk measures
Lecture 4 : Lecture4.pdf. Basel IV : the rules with additional details : see page 12 of this pdf
Course 3 : Basics on derivatives
Lecture 3 : Lecture3a.pdf and Lecture 3b.pdf
Simulation with Python of Brownian Motion and Geometric Brownian Motion
Some examples of arbitrage opportunities : Carry-trading or Cash-and-carry strategy for crypto-currencies
The concept of risk-neutral probability in the case of options : risk-neutral
Lab 3a : Lab3a.pdf.
Notebook of Lab 3a : Lab3a.ipynb
Lab 3b : Lab3b.pdf
Notebook of Lab3b : Lab3b.ipynb
Course 2 : Ratio analysis with Python
Lecture 2 : Lecture 2.pdf
Notebook of Lecture 2 : RatioAnalysis.ipynb
More on Pandas dataframe : get names of rows and columns , deal with rows and columns
Course 1 : Basics on Probability and Statistics with Python
Lecture 1 : Lecture 1a.pdf, Lecture 1b.pdf, Lecture1c.pdf.
Collect data with Yahoo Finance : Collect.ipynb
Financial Statement with Yahoo Finance : FinancialStatement.ipynb
Course 0 : 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
Introduction to the Seaborn library : IntroductionSeaborn.ipynb