Data mining & predictive modeling

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

After completion of this course:

  • 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.

In consultation with the participants this course can be taught in Dutch or English. 

The techniques demonstrated with the Python-based visual programming environment Orange, can also be performed in Python and yield comparable results. Participants receive sample data files so that they can reproduce the results using their preferred data analysis software. Do you want to use Python or discover more possibilities of Python? View our course Python for engineers.

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).

Interested in related courses? Also have a look at the course  Practical data scienceMultivariate data analysisTime series analysis and forecasting, Python voor ingenieursDesign of Experiments (InCompany training) en Essentials of predictive analytics (InCompany training).   

Also take a look at the overview of all data analytics and statistics courses.

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  • Information
    The program can be taught in English on request.
  • Program

    Day 1

    • Introduction to course, software, examples and case-studies
    • Principles in predictive analytics:
      • Data pre-processing, data exploration & data reduction
      • Over-fitting and model tuning
    • Resampling methods & regularization

    Day 2             

    • Essentials of prediction modeling:
      • Overview of commonly used prediction methods, such as linear regression, neural networks, decision trees, random forests, support vector machines and nearest neighbour algorithms
    • Selection, validation, and use of predictive models in practice

    Day 3

    • Essentials of classification:
      • Overview of commonly used classification methods, such as logistic regression, neural networks, classification trees, support vector machines and nearest neighbour algorithms
    • Selection, validation, and use of classification models in practice

    Day 4

    • Essentials of cluster and segmentation models:
      • Overview of commonly used cluster and segmentation methods, such as hierarchical, k-Means and Louvain clustering
      • Selection, validation and use of cluster and segmentation models in practice
    • Epilogue: review, trends, and next steps
  • Reviews
    This course is assessed with a 8.3
    “This course will help you understand better what you do with data mining: the basis for applying it responsibly.”
    Max Welling (Welling IT Consultancy BV)
    “Very concrete course that can prevent many difficulties when starting data mining.”
    employee Sligro Food Group
    “Course has given a lot of insight, but was at some points more challenging than expected.”
    employee DAF Trucks NV
    “A good introduction to the subject matter.”
    employee Ministry of Defence
    “Very good course, good book, good explanation, good complementary literature. Enthusiastic teacher.”
    Coen Hoogervorst (Strukton Worksphere)
    “Good overview of all related knowledge. Good extra teaching material, very nice teacher.”
    Yuzhong Lin (TU/e)
    “Good course, excellent teacher.”
    Peter van der Hagen (INFOTAM)
    “High speed, good content.”
    Marc de Wolf (Effect Photonics)
    Elijan Fokkinga (Endress+Hauser)