Data mining & predictive modeling

4 days
In English on request

During this course you will learn to use the techniques required to perform analyzes of large data sets to find (statistical) connections.

Fundamental concepts for understanding and successfully applying data mining methods

It is becoming increasingly easy and common to collect and store large amounts of data. This applies for example to consumer data, data on individual behavior, warranty and fault data and production processes where sensors log data on a large scale.

With the help of data mining it is possible to discover relationships and structures in such large amounts of data and to develop prediction models. Techniques from applied statistics, artificial intelligence and machine learning are used.

In this course you will learn fundamental concepts for understanding and applying data mining methods. During the course participants gain experience in the visual programming of data workflows, with which model-fit, -validation and -optimization can be easily executed in practice.

Successful application of data mining methods and business analytics

At the end of this module you will:

  • You have an overview of commonly used methods for predictive modeling from the areas of statistics, artificial intelligence and machine learning.
  • You can develop these models for standard situations independently, using software such as IBM Modeler, SAS-Enterprise Guide and Enterprise Miner or Orange to visually program data workflows.
  • You have gained practical experience with validating, interpreting and comparing alternative models and their use for decision support.

Intended for

Academics and higher professionals who are dealing in their work with data mining issues and the analysis of large data files. The course is also suitable for lecturers from universities and colleges of higher education who want to become acquainted with current methods in the field of data mining.

Background in a specific discipline is not required. Knowledge of basic statistical techniques such as testing, estimation and regression modelling is desirable.

Curious about the background of course leader Koo Rijpkema and his vision on the importance of data in the world of technology? Read the interview with Koo Rijpkema!  Or do you want to know more about the user experience during an incompany edition of this course? Then read the interview with Hendrik-Jan de Kort (SPIE)..

In English on request

Do you want to follow the course in English? Please mention this in the remarks field when you register.

Course leader

Data analysis and programming

dr. Koo Rijpkema

Eindhoven University of Technology (TU/e)

“For me, teaching means sharing knowledge and passion, inspiring and fascinating people through the application of statistics.”

This course is rated with an average of


Program manager


  • The latest post-academic knowledge and skills
  • Focused on questions that arise in a technical environment
  • Interactive and directly applicable in practice
  • Top teachers from science, research and business

Frequently asked questions

  • "Met deze cursus snap je beter wat je met data mining doet: de basis om het verantwoord toe te passen."
    Max Welling
    Welling IT Consultancy BV
  • "Zeer concrete cursus die veel moeilijkheden bij het beginnen met data mining kan voorkomen."
    Sligro Food Group
  • "Hoge snelheid, goede inhoud."
    Marc de Wolf
    Effect Photonics
  • "Goede cursus, uitstekende docent."
    Peter van der Hagen
  • "Goed overzicht van alle gerelateerde kennis. Goed extra lesmateriaal, erg leuke docent."
    Yuzhong Lin
  • "Erg goede cursus, goed boek, goede uiteenzetting, goede aanvullende literatuur. Enthousiaste docent."
    Coen Hoogervorst
    Strukton Worksphere
  • "Goede kennismaking met de materie."
    Ministerie van Defensie
  • "Cursus heeft veel inzicht gegeven, maar was op sommige punten wel pittiger dan verwacht."
    DAF Trucks NV

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Download the Study Guide

In addition to the course offerings, the Study Guide also contains the themes that we will further develop next year. Would you like a complete overview of our courses and training in your field(s)? Request the Study Guide and receive it digitally.