Course introduction

The aim of the course is to develop participants’ skills in using data science methods and techniques in an end-to-end project. During three days participants will gain experience with various aspects of data science methods, from data engineering, exploratory data analysis, machine learning, neural networks and NLP. We include also a panel discussion set-up encouraging ample debates on the ethics of AI, model explainability, data fairness and bias in data science.    

  • Data Engineering

    The engine that powers any data science project. We will address feature engineering, data cleaning and manipulations, visual data exploration

  • Machine Learning

    The workhorse of data science projects. We will address subjects such as algorithm selection, dimensionality reduction, predictive modelling, model validation.

  • AI Models

    The crown on data science projects. We will address subjects as model selection, optimization, text processing, prediction. We will build step by step an AI model based on real data.

  • Ethics in Data Science

    The sensible part of every data science project. We will address in a panel discussion various ethical issues every data science professionals should be concerned with.


  • Apply dimension reduction techniques
  • Explain topic modelling techniques in natural language processing
  • Describe and apply outliers handling best practices
  • Describe and implement tools to address class imbalance issues
  • Perform basic EDA and data visualisation
  • Employ strategies for dealing with missing data
  • Explain the differences between supervised and unsupervised models
  • Develop an ML model for predictions (dimensionality reduction, training, testing, validation)
  • Evaluate ML models
  • Explain the difference between AGI and ANI
  • Describe the basic mechanisms behind neutral network

Prospective participants may indicate their interest by sending an email to


Course details
Date: 23 - 25 August, 2021
Workload: 8 hours a day
Language: English
Costs: 1.150 EUR
Target group: data scientists with at least 1 year of experience in DS or similar relevant experience (graduates, young professionals) who want to deepen their knowledge in the field of data science, machine learning, data visualisation and AI. Python is not taught in this course.
Specific entry requirements: 
- Basic knowledge of statistics
- Experience with Python
- Experience with Jupyter notebooks
- Basic knowledge of data scienceWhat to expect: A very practical, business-oriented masterclass in which participants collaborate in dealing with a project using real data and real business problems that can be solved using data science.

Course leaders

Behavioural Data Science Incubator

A team of data science enthusiasts and researchers at BMS Faculty (University of Twente). Each of them has multiple years of experience in developing data-driven methods to solve social science problems.

They spark innovation and collaboration in data science that involves human behaviour.

R. Marinescu-Muster MSc (Robert)
PhD Candidate
K.A. Kroeze MSc (Karel)
dr. A.K. Machens (Anna)
Data Scientist

Course package

3-day course package: 

  • Course participation and course material 
  • Overnight stay at the UPark Hotel on the University of Twente campus 
  • 2x Buffet-style breakfast 
  • 3x Buffet-style lunch 
  • 2 x 3-course dinner or dinner buffet & 2 dinner drinks 
  • Unlimited tea, coffee, water and soft drinks 
  • Free use of fast and secure Wi-Fi 
  • Free use of power banks for mobile phones 
  • Free parking 

Personal expenses like travel costs (plane ticket, train ticket, etc.), travel and health insurance and other expenses are not included in the fee. For more details check our terms and conditions

More information?

If you need more information regarding this course, please feel free to contact us at