DS1: Introduction to Data Science


As a recurrent topic for a few years, the Data Science gathers subjects like statistics, machine learning, computer science and the domain expertise. Machine learning methods are characterized by algorithms that allow problem solving starting from data.

This training will give an insight of Data Science ecosystem. A wide range of theoretic notions, generic stakes and algorithms included in the Data Science scope will be defined. A discovering of Data Science-oriented programming will be proposed in Python as well. Regarding Data Science, beginners and little-experienced professional are the training expected audience.


Thanks to this training, you will develop the following skills:

  • Understand the notions and stakes of Data Science
  • Have an insight of machine learning algorithms, and know some application examples
  • Know the most encoutered Data Science tools (Python developing environment)


2 days


Knowledge in computer programming


This program is indicative. It could be adapted to your specific needs.

  • Presentation of Data Science major concepts

    • History, definition, framing
    • Definition of machine learning, and Artificial Intelligence
    • Presentation of some examples of Data Science realization

  • From raw data to algorithm results

    • Data, as the crux
      • variable types
      • where to find them?
      • how to store them?
      • how to preprocess them?
    • Machine learning algorithm taxonomy
      • supervised learning
      • unsupervised learning
      • other kinds of algorithms (semi-supervised, recommendation, reinforcement learning)
  • First practical work in Python

    • Working environment configuration: Python, ipython and jupyter-notebook setting up
    • numpy, pandas, matplotlib, seaborn, scikit-learn, tensorflow libraries setting up
    • First program to "do" Data Science

DS1 – Data Science


Pas de sessions inter-entreprise à Paris ou à Lyon de prévues pour le moment.

Contact us for on-site trainings (dates are flexible to your needs).