This is the official repository of the Python Course, for students enrolled in Parcours DATA - Science des Données et Intelligence Artificielle (SDIA) at Ecole Centrale de Lille.
The material is inspired and/or borrowed from courses previously given by:
- Guillaume Gautier at Ecole Centrale de Lille - Data Science and Artificial Intelligence (SDIA). Link to the GitHub repository of his course: https://github.com/guilgautier/sdia-python , and
- Pierre-Antoine Thouvenin at Université de Lille - M1 Data Science
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The list below summarizes the list of labs addressed in this course, with an indicative number of hours to be spent in class for each lab.
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After completing each lab, upload your work in the corresponding dropbox file request.
Each lab will have its own file request link and formatting instructions for submmission.
Please read carefully before submitting!
Objectives: Revisiting the basics
- Git and Github basics;
- Using an IDE (Vscode);
- Configure, structure and develop a Python project;
- Refreshers about Python programming;
- Unit-tests and Documentation;
- Useful Python references.
Remark: Make sure you are familiar with all the elements covered in this lab. These will be repeatedly used throughout this course.
Objectives: Practice with libraries commonly used in data science (numpy, scipy, pandas, hdf5, matplotlib).
Objectives: Further practice with common libraries and algorithms (numpy.fft, matplotlib, seaborn).
Objectives: Standard techniques and libraries to accelerate numpy codes (cython, numba). The implementation will be compared and validated against the corresponding scikit-learn implementation.
Objectives: Few parallelisation techniques in Python (multiprocessing, dask).
Objectives: Python libraries to crawl the web to collect data automatically (with urllib3 and Beautiful Soup).