Course

Data mining & business analytics

Fundamental concepts for understanding and successfully applying data mining methods (with R)

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. Using practical examples and exercises you will gain experience with data mining software, such as R, Minitab, JMP or IBM-SPSS.

Successful application of data mining methods

During this course:

  • You will learn the most common techniques in the field of applied statistics, artificial intelligence and machine learning that are important for understanding and successfully applying data mining methods.
  • Gain insight and skill in the basic techniques needed to perform analyses with relevant software in the field of data mining.

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.

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  • Information
    Trainer: Dhr. Dr. J.J.M. Rijpkema (Eindhoven University of Technology (TU/e))
    Course data: October 30, November 6, 13 and 20 - 2019
    Location: Campus Eindhoven University of Technology
    Price: € 2,295.00 ex. vat
    In cooperation with: TU/e, department of Mathematics & Computer Science
    Language
    The program can be taught in English on request.
  • Program

    Topics treated:

    • Introduction to course, software and case studies.
       
    • Data preprocessing: data cleaning, outlier detection, missing data and data transformations.
       
    • Exploratory data analysis, data visualization and data reduction.
       
    • Starting points and principles of:
      • Prediction models based on linear and PLS regression, neural networks, support vector machines, K-nearest neighbors, regression trees and rule-based models.
      • Classification models based on logistic regression, discriminant analysis, neural networks, Support Vector Machines, K-Nest neighbors, classification trees and rule-based models.
      • Cluster and segmentation models based on hierarchical and k-means clustering and Kohonen networks.
      • Association and sequence models.
         
    • Selection, validation, combination and use of models for business analytics:
      • CRISP-DM, model performance indices, bagging & boosting, resampling and regularization methods.
         
    • 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)