Programming with Python: Data Science tools
— Data Science is the convergence of mathematics, statistics and computer science to make the most of the information contained in data. Most Artificial Intelligence (AI) methods rely on it. Python provides the Data Scientist with all the tools needed to do scientific programming. This course places particular emphasis on the quality of the code.


• Understand the challenges of scientific programming
• Discover Machine Learning
• Know Python libraries for data mining and scientific computing
• Produce robust and quality Python code


• Business Analysts
• Developers
• Statisticians


Artelys data scientists regularly participating to customer-driven projects.


Programming efficiently with Python
• Introduction to the language, first Python script.
• Presentation of development environments (Anaconda).
• Jupyter notebook: an efficient environment for the presentation and reproducibility of scientific results.

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.




Practical information

Next session
Soon to be updated

Special session available
(minimum of 4 participants, on-site or online)

Training duration
3 days


Training cost per person
1 950 € excluding taxes.
This includes the materials, meals, coffee and handouts. Computers are provided.

Entire Catalog
Available on this link

Artelys is a training institution, registered in France under registration number 11754066975.


Are you interested in this training ? Register now !



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