Data science … the next step in Six Sigma?
Six Sigma represents the first generation of computer-aided analysis techniques, such as regression, design of experiments and control charts. Driven by discoveries in mathematics and the tremendous power of modern computers, the last decades have seen the emergence of a totally new brand of analytics from statistical learning, machine learning and AI.
Also, where Six Sigma uses powerful techniques to get the most out of small datasets (say, 𝑁=20 to 100 or so), modern IT infrastructures and the IoT, cheap storage and computing capacity, and the resulting huge streams of data enable totally new applications of analytics, where data could also be images, audio or natural language.
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”).
What it could bring to you?
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.
What will you 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.
- Data engineering 101, data pipelines, and SQL and No-SQL.
Six Sigma, DfSS and Lean Six Sigma green belts and black belts eager to enrich their expertise with machine learning and data science.
Location and cost:
Eindhoven: High Tech Campus 29 The costs are €2.950 (excl. VAT) per participant. Included are four training days, course material, tools, refreshments and daily lunches.
Course duration and number of participants:
4 modules of one day from 9.00 am to 17.30 pm. Given the interactive form of the course, the number of participants will be around 8-10 persons.
In the case of one or more non-Dutch speaking participants, the training is given in English.
Day 1: May 18
Day 2: June 8
Day 3: June 9
Day 4: June 22
After completing the full training, participants receive proof of participation.
Besides his affiliation with HI, Jeroen de Mast is a professor at the University of Waterloo and Academic Director at the Jheronimus Academy of Data Science. Jörg Bewerunge is a lead data scientist and project manager.