A 24 teaching hours course (2 ECTS) in Python focusing on aspects pertinent to data scientists, ranging from basic Python to web scraping. The course is based on the book written by Kevin Sheppard that is available here.

TP 1

Introduction of basic Python data types and structures such as functions, loops, flow control blocks, etc. NumPy library, base data types and features. Jupyter notebook with answers.

TP 2

Basic data processing: importing and exporting data files, pandas library, data structures and some of the useful features. Data for the exercises with answers.

TP 3

Graphics: Matplotlib and seaborn libraries, line and scatter plots, pie charts, histograms, subplots, etc. Data for the exercises, examples, answers.

TP 4

Statistics and probability in Python using numpy.random and scipy.stats: simulation of random variables, distribution associated functions, statistical tests. Answers to some of the exercises.

TP 5

Simple web scraping with Requests and BeautifulSoup. Jupyter notebook with answers.

Project

Jupyter notebook, PDF, data and answers.