Data Science Toolkit and Applications

Presentation & objectives

This course covers the entire data science pipeline, from initial data exploration to the implementation and evaluation of machine learning models. It emphasizes hands-on experience with essential tools and libraries, preparing students to tackle real-world data science challenges. The course also incorporates best practices in software development and version control.

Key concepts covered:

  • Python programming for data science (including key libraries like Pandas, NumPy, Scikit-learn)
  • Data manipulation and storage (structured data formats, databases)
  • Data exploration and preprocessing techniques
  • Data visualization principles and tools
  • Model training and evaluation
  • Software development best practices (version control, testing, documentation)

Prerequisites :

  • Being familiar and efficient with Python programming 
  • Being familiar with basic Linux commands

You can refer to the course presentation slides if needed: Course presentation.

Assessment

By the end of this course, students will be able to:

  • [BC04] Apply best practices in software development to data science workflows
  • [BC07] Design and implement end-to-end data science projects using industry-standard tools and libraries
  • [BC02] Effectively communicate insights derived from data through visualization and statistical analysis

The evaluation will be based on a machine learning case study, where the deliverable will be a public project deposited on GitLab. This project will serve as the foundation for a portfolio that students will complete throughout their studies.

License

This content is made available under the Creative Commons Attribution-NonCommercial 4.0 International License.

Citation

To cite this educational material, please use the following reference:

Lina Fahed, Laurent Brisson. "Data Science Toolkit and Applications". IMT Atlantique. Available under CC BY-NC 4.0 license. https://hub.imt-atlantique.fr/datascience-toolkit/

You are free to:

  • Share — copy and redistribute the material in any medium or format
  • Adapt — remix, transform, and build upon the material

Under the following terms:

  • Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made
  • NonCommercial — You may not use the material for commercial purposes