MATLAB: data skills and analytics for the social sciences

Professional skills courses

Course information

ECTS: 2.5
Number of sessions: 4
Hours per session: 3
Course fee:

  • free for PhD candidates of the Graduate School
  • € 575,- for non-members
  • consult our enrolment policy for more information


Telephone: +31 10 4082607 (Graduate School).

In the academic year 2023-2024 this course will take place online.

Session 1
January 9 (Tuesday) 2024
Online (Teams)

Session 2
January 16 (Tuesday) 2024
Online (Teams)

Session 3
January 23 (Tuesday) 2024
Online (Teams)

Session 4
January 30 (Tuesday) 2024
Online (Teams)


In this course, you will learn pre-processing and analyzing social science data with MATLAB.
Learning and using MATLAB has several valuable advantages:

  • MATLAB provides a versatile learning platform that will help you master advanced data skills, research methods and statistical and AI models that are otherwise more difficult and time-consuming to acquire and learn.
  • MATLAB will shorten the time you need to learn applying advanced research methods to your own research questions.
  • MATLAB helps you with working with the heterogeneous, multimode, and large datasets that are becoming increasingly important in the social sciences and humanities.
  • With MATLAB you can produce high quality visualizations and build a strong Open Science visibility record.
  • Learning MATLAB is a long-term investment; it is worth your time if you want to prepare for a ‘data-rich’ career.

Aims and working method

This four-module online course follows a learning-by-doing approach using MATLAB interactive notebooks. After an introduction to MATLAB’s fundamentals, you will learn to prepare, process, and analyze social science datasets and research questions with MATLAB. Specific attention will be paid to MATLAB’s graphics capabilities. Depending on your interests, specific topics (such as maps, multilevel data, graphs, and large datasets) will be discussed. Students are encouraged to bring their own data.

Learning objectives

After completing this course participants will:

  • Know how to work with MATLAB, Toolboxes and add-on packages;
  • Master elementary MATLAB data skills for social sciences research;
  • Master elementary skills to translate social research questions into MATLAB data structures and datasets;
  • Understand how to apply MATLAB’s analytic capabilities to their own research;
  • Understand the applicability of MATLAB for their research projects.

Entry level

The course is useful for students who have no prior knowledge of and experience with MATLAB. Familiarity with a statistical package (SPSS, Stata, R, SAS) and/or a programming language (Python, R) is recommended.

Preparations and requirements

Students need to prepare for 2 hours homework per session. In addition, before session 1 starts students should have completed the MATLAB Onramp (2 hours, self-paced course online).

It is assumed that students have installed MATLAB and both the Text Analytics toolbox and the Statistics and Machine Learning Toolbox before the course starts. More information on how to install MATLAB and MATLAB Toolboxes can be found here and at the EUR employee work support page.

Students can install the MATLAB 2023A software directly from the EUR Software Center or download the latest MATLAB version from MathWorks. A MathWorks account is needed to download the latest version of the MATLAB software (choose MATLAB individual as license type). Click here to register for a MathWorks account. A MathWorks account is also required to make use of MATLAB Drive where all course materials will be shared.

MATLAB’s minimum system requirements are described here. A minimum of 8 GB of RAM is advised.

Entry level

The course is useful for students who have no prior knowledge of and experience with MATLAB. Some familiarity with a statistical package (SPSS, Stata, R, SAS) and/or a programming language (Python, R) is recommended.

Session descriptions

Session 1: Mastering essential MATLAB data skills
The first session focuses on mastering the basics of the ‘MATLAB language’ and working with the MATLAB Live Editor. How to read MATLAB code syntax and how to work with MATLAB data structures are key elements of the first session. In addition, several use cases and visualizations will be used to illustrate MATLAB’s capabilities.

Session 2: Analyzing data with MATLAB
This session focuses on analyzing data. Various modes of analysis will be illustrated with MATLAB datasets e.g., regression analysis, analysis of variance, factor analysis and clustering methods. In addition, students will learn the basics of using (custom) MATLAB plots to visualize research insights. Students are encouraged to bring their own data (BYOD) in this session.

Session 3: Use cases and featured topics
Depending on students’ interests, we will further zoom into specific social science research practices and methods. The overarching goal is to help students learn from data with MATLAB through real-world examples, teaching data generation skills, and fostering analytical skills. Again, depending on students’ interests this session might focus on analyzing data through e.g., general linear models, non-linear models, or Bayesian strategies.

Session 4: Share, reuse and engage
In session 4 students present a case study or "data story" based on their own dataset(s), or a dataset they worked with so far. Students demonstrate what they have learned so far through e.g., analysis examples, data visualizations or sharing MATLAB workflows. The focus of this session is practical knowledge sharing and transfer of learned MATLAB skills and applied methods.

About the instructor

Rob Grim has held positions as a Data Analyst, as a Research Data Specialist and as Head of Research Support. He currently works as Business/Economics & Data Librarian at the EUR and as a member of the Erasmus Data Service Centre (EDSC) team. Rob is a Carpentries teaching instructor and has extensive experience with data-preprocessing, and data analytics in various science disciplines. He has an interest in statistics, cognitive science, and machine learning. Rob has a background in Psychology.