Data Science training for programmers

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Data Science training for programmers

Business analysts and programmers are continuously looking to expand their skillset. Within their field of expertise, advanced analytics and data science is getting more and more attention. This 5-day course teaches you all the necessary basics to become a data scientist. During this course, we'll take you end to end through all the relevant topics of data science. We'll teach you the basic concepts in Statistics and Machine Learning, introduce you to the different Python packages (Pandas, Numpy, Pyplot, Scikit-learn, Tensorflow), help you discover and prepare the data and build machine learning models. We’ll go through practical exercises and make sure that at the end of this course, you have everything you need to become a data scientist. After the course, we challenge you with a real-life case to further develop your skills.

Course Objective

This training is the perfect first step in becoming a data scientist or for further expanding your skillset. After this 5-day course, participants will be able to embark in end-to-end data science projects with all the necessary skills needed. Of course, practice makes perfect so we highly encourage the participants to continue developing their skills after this training. A first step is to continue with the real-life case we share at the end of the session. 


  • Analysts or programmers who are looking to expand their skillset
  • Python experience is not required but helpful 
  • Basic mathematics and statistics knowledge
  • Enthusiastic, eager to learn and willing to invest time after the course


  1. What’s changing in the world and why are we here today
  2. Statistics, Machine Learning and Artificial Intelligence: what’s it all about?
    1. Theoretical overview of statistics
    2. Machine learning: models and feature importance
    3. Artificial intelligence
    4. Data science methodology
  3. Data Discovery
    1. ML Python packages: Numpy, Pandas, Pyplot
    2. Data visualization with Python
  4. Data Preparation
    1. Data preparation process
      1. What about outliers
      2. Missing values
      3. Features and how to define them
  5. The process of Machine Learning and practical examples
    1. The machine learning process
    2. Model performance evaluation
    3. Machine learning with Python: Scikit-learn
  6. Deep learning using Tensorflow
    1. Theory on Tensorflow
    2. Machine Learning with Tensorflow
  7. Embedding Machine learning solutions in your organization and practical examples
    1. Concrete & practical examples of machine learning
    2. Applications


Eager to get started? Contact us to know when our next sessions will be given or if you want a tailored session for your company.