About the course Data Science for Six Sigma Green Belts & Black Belts
The course is a combination of lectures, hands-on work sessions, and home assignments. At the first day, you will get instructions for installing and managing an analytics environment based on Python and Jupyter Notebooks on your laptop.
Subjects of the course Data Science for Six Sigma Green Belts & Black Belts
The following topics will be discussed during the Data Science for Six Sigma Green Belts & Black Belts course:
- 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 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 and neural networks) 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.
- Data engineering 101, data pipelines, and SQL and No-SQL.
Results of the course Data Science for Six Sigma Green Belts & Black Belts
During the Data Science for Six Sigma Green Belts & Black Belts course you will learn to:
- Understand what data science is and what role it plays in modern business and industry.
- Have a working, modern analytics environment on your laptop and the skills to use it in a data-analytic workflow.
- Understand essential techniques from machine learning, and have basic experience in applying them using Python and SciKit-Learn.
- Have an overview of the landscape of modern data engineering and architecture.
- In four days’ time, you will get a solid foundation in techniques that will have great impact in the next decade.
The course Data Science for Six Sigma Green Belts & Black Belts 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.
The course consists of 4 days, from 9.00 am to 13:00 pm.
Locations, dates & schedule
Location: Eindhoven – High Tech Campus 29
Session III, from 9:00 AM – 17:30 PM – 4 modules
Day 1: 13 September
Day 2: 27 September
Day 3: 28 September
Day 4: 07 October
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.
Prof. Dr. Jeroen de Mast
Besides his affiliation with HI, Jeroen de Mast is professor at the University of Waterloo and Academic Director at the Jheronimus Academy of Data Science.
Jerry de Groot
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.
With mechanical engineering as technical background, Dories has extensive experience in modeling, testing, and reliability engineering in various projects. She is analytical, pragmatic and eager to learn. She always likes to think along with interesting topics. Experienced training in Lean Yellow belt, FMEA and Reliability Engineering.
Certificate / diploma
After completing the full training, participants receive proof of participation. 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.
Method of teaching
This training will be available as Live Online Interactive Training (LOIT).
Minimum 8 participants, maximum 12 participants.