About the course Data Science for Six Sigma Green Belts & Black Belts
Six Sigma represents the first generation of computer-aided analysis techniques, such as regression, design of experiments and control charts. The last decades have seen the emergence of a totally new brand of analytics from statistical learning, machine learning and Artificial Intelligence.
Where Six Sigma uses powerful techniques to get the most out of small datasets (𝑁=20 to N=100), utilizing the big streams of data is enabled by the modern IT infrastructures and the IoT, cheap storage and computing capacity. This leads to new analysis methods where data could also be images or audio.
While Six Sigma focuses on optimizing business processes and current product lines (“Horizon 1 innovation”), the current industry recognizes data and analytics as valuable assets and therefore explores data-driven business models and strategies (“Horizon 3 innovation”).
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:
- Continue the expertise in Six Sigma
- Understand data science, machine learning and Artificial Intelligence, their application in CRISP-DM projects, and how they are applied in the 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 fine tuning 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
- Understand essential techniques from machine learning, and have basic experience in applying them using Python and SciKit-Learn
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.
4 modules of one day, from 9.00 am to 17.30 pm
Locations, data & schedule
Location: Eindhoven – High Tech Campus 29
Day 1: November 11
Day 2: November 25
Day 3: November 26
Day 4: December 09
Day 1: May 10
Day 2: May 25
Day 3: May 26
Day 4: June 14
The investment is €2.950 (excl. VAT) per participant. Included are four training days, course material, tools, refreshments and daily lunches.
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) and/or in live sessions, depending on the COVID-19 situation at the time. The trainers prefer live sessions when possible.
Maximum group size: 10 participants