Introduction
In this course, you will learn pre-processing and analysing 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 visualisations 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.
Course information
ECTS: 2.5
Number of sessions: 4
Hours per session: 3
Key Facts & Figures
- Type
- Course
- Instruction language
- English
- Mode of instruction
- Online
What will you achieve?
- After completing this course you will know how to work with MATLAB, Toolboxes and add-on packages.
- After completing this course you will master elementary MATLAB data skills for social sciences research.
- After completing this course you will master elementary skills to translate social research questions into MATLAB data structures and datasets.
- After completing this course you will understand how to apply MATLAB’s analytic capabilities to your own research.
- After completing this course you will understand the applicability of MATLAB for your research projects.
Start dates
Session 1
January 9 (Tuesday) 2024
09:30-12:30
Online (Teams)
Session 2
January 16 (Tuesday) 2024
09:30-12:30
Online (Teams)
Session 3
January 23 (Tuesday) 2024
09:30-12:30
Online (Teams)
Session 4
January 30 (Tuesday) 2024
09:30-12:30
Online (Teams)
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
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 visualisations will be used to illustrate MATLAB’s capabilities.
This session focuses on analysing 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 visualise research insights. Students are encouraged to bring their own data (BYOD) in this session.
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 analysing data through e.g., general linear models, non-linear models, or Bayesian strategies.
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.Email address
Contact
- Enrolment-related questions: enrolment@egsh.eur.nl
- Course related questions: rob.grim@eur.nl
- Telephone: +31 10 4082607 (Graduate School)
Facts & Figures
- Fee
- free for PhD candidates of the Graduate School
- €575,- for non-members
- consult our enrolment policy for more information
- Tax
- Not applicable
- Offered by
- Erasmus Graduate School of Social Sciences and the Humanities
- Course type
- Course
- Instruction language
- English
- Mode of instruction
- Online