Kies de Nederlandse taal
Course module: 880022-M-6
Social Data Mining
Course info
Course module880022-M-6
Credits (ECTS)6
CategoryMA (Master)
Course typeCourse
Language of instructionEnglish
Offered byTilburg University; Tilburg School of Humanities and Digital Sciences; TSH Other;
Is part of
M Communication and Information Sciences
M Data Science and Society
C.D. Emmery, MSc
Other course modules lecturer
Academic year2016
Starting block
Course mode
RemarksThis information is not up to date. Check the Course Catalog 2018 or select the course via “Register”.
Registration opennot known yet

The three learning goals for the course are:

1. The student is able to understand the main principles of data collecting and data mining methods. 

2. The student knows how to pre-process and analyse social data on a large scale.

3. The student is able to perform and evaluate elementary data mining experiments.


This course has a maximum capacity of 40 participants. Questions: e-mail to:

Social Data Mining is accessible for all students (no technical background required). During the course, students will complete assignments in which they will train their basic data mining skills in the domain of social media and behaviour. The experiments and assignments will be performed with open-source data mining software weka. During the course there will be intermediate exam every two weeks to ensure that students keep on track with the course contents. The course is completed with a written exam.

This course is compulsory for students of the track Data Science: Business and Governance (2016-2017). Passing the course is a prerequisite for Master thesis/Data Science in Action in the DSBG track.

Required Prerequisites


Recommended Prerequisites


Data Science methods are becoming the main tools for acquiring information both in the business context and in scientific research. The course offers a thorough introduction to the use of data mining for the analysis of data in a wide variety of formats. Upon completion of the course, students will have acquired the skills necessary to apply data mining in support of decision making, visualisation, and discovery. In addition, students will be familiar with advanced topics such as deep learning. The perspective of the course is application-oriented and serves to provide students with the knowledge and experience that is in line with the current demand for skilled data scientists. 

Type of instructions

Lectures and hands-on sessions

Type of exams

Two-weekly tests and final exam

Compulsory Reading
  1. Research papers, see Blackboard.
Required materials
Recommended materials

Kies de Nederlandse taal