Fundamentals of Python programming
• Python data structures (lists, tuples, dictionaries).
• List traversal and generation (itertools, iterators, generators and comprehension lists).
• Good practices: exceptions, type checking, etc.

Code organization and quality
• Comments and cleanliness (docstring, linters, pep8, etc.).
• Modularity and reusability of code (file import, Object Oriented Programming and polymorphism).
• Algorithms and complexity.

Distribution, isolation, and package management

Introduction to scientific programming
• Scientific programming vocabulary and statistical analysis.
• Main machine learning algorithms (supervised analysis, unsupervised analysis, classification, and regression).
• The scientific stack: Numpy, Scipy, Scikit-learn, Pandas, Sympy, Matplotlib.

Descriptive statistics and data structures
• Data management with pandas: import, dataframes, slicing, mapping (reading, formats, date management).
• Visualization with Matplotlib.

Machine Learning with Scikit-learn
• Presentation, linear modelling and prediction, classification with Scikit-learn.

Scientific computing with Numpy
• Presentation, data structure, indexing, slicing, iterating.

Scientific computing with Scipy
• Overview, linear algebra, application.