Course information

  • 4 days
  • 12 September, 9 and 10 October, and 7 November 2024


The investment is €2.990 (excl. VAT) per participant. 

Included are four training days and extensive course materials. The software used in the course is open-source and free. 

About the course HI-PP1 Data Science for Six Sigma Green Belts and Black Belts.

Data science and six sigma have much in common. Both operate on data-driven analysis techniques, offer structures for framing and solving problems, and follow a project-based approach. But also: six sigma is at least 25 years old … data science takes it to the next level. With new analytics, new forms of data, and new opportunities! 

The last decades have seen the emergence of a totally new brand of analytics from statistical learning, machine learning and AI. While six sigma focuses on optimizing business processes and current product lines (“Horizon 1 innovation”), current industry recognizes data and analytics as valuable assets in themselves, and explores data-driven business models and strategies (“Horizon 3 innovation”). 

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With this course you’ll learn:

  • Understand the role data science plays in modern business and industry
  • Understand essential techniques from machine learning
  • Have basic experience in applying techniques using Python and SciKit-Learn
  • Have an overview of the landscape of modern data engineering and architecture

Teaching professionals.

Jerry de Groot

Jerry de Groot

Data Scientist
Specialized in medical device technology after studying applied physics, Jerry learned statistics and data science through academic research in the Amsterdam Medical Center. He is keen in figuring out how things work (or don’t), loves prototyping and has a natural ability to explain complex abstractions in plain language.

Course: Data Science for Six Sigma Green Belts and Black Belts.


This course builds on your six sigma skills. By helping you to understand how new forms of data and new analytical techniques are reshaping the field.  

It gives you a solid and practical foundation in machine learning on which you can build your further mastery of the fields of machine learning and Big Data. 

Data Science for Six Sigma Green Belts and Black Belts

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What you’ll learn.


During the course Data Science for Six Sigma Green Belts and Black Belts you will learn:

  • Building on your expertise in Six Sigma, you will learn new skills.
  • Understand data science, machine learning and AI, their application in CRISP-DM projects, and how they are applied in smart industry.
  • Work in an analytics environment based on Python, Jupyter notebooks and Anaconda.
  • Practical visualization using Python’s Matplotlib and Seaborn libraries.
  • Machine learning using Python’s SciKit-Learn. Essential predictive algorithms (lasso regression, decision trees and random forests, support-vector machines, neural networks and deep learning) and unsupervised learning (principal components analysis and t-SNE).
  • Apply a practical workflow: feature engineering and data preprocessing, training a model and finetuning hyperparameters using cross validation, model evaluation and implementation in a pipeline.
  • The basics of data engineering, data pipelines and handling SQL and No-SQL data sources.

Practical information.

For whom

The course is aimed at six sigma, DfSS and Lean Six Sigma green belts and black belts eager to enrich their expertise with machine learning and data science. 
Although the course is called “Data Science for Six Sigma Green Belts & Black Belts”, it is open to anyone reasonably familiar with six sigma principles.  

The course contains various engineering examples and covers the CRISP-DM framework. Which is the data science industry standard equivalent of DMAIC. The course also contains examples and exercises in Python, a popular open-source programming language for data science. Prior knowledge with Python is not necessary. 


After completing the training, participants receive proof of participation.

Location & Dates


Eindhoven – High Tech Campus 29 

Dates 2024

Session I:

Day 1: 12 September

Day 2: 9 October

Day 3: 10 October

Day 4: 7 November

Group size  

A minimum of 8 participants with a maximum of 12 participants.