Session 1 (online)
January 12 (Wednesday) 2022
Session 2 (online)
January 19 (Wednesday) 2022
Session 3 (online)
January 26 (Wednesday) 2022
Session 4 (online)
February 2 (Wednesday) 2022
This course explains and demonstrates how to prepare and process quantitative data for scientific analysis in MATLAB. MATLAB is an innovative software program that probably offers the most comprehensive scientific analysis toolset worth investing time in now. It is not without reason that MATLAB was denoted as a leader in the 2021 Gartner Magic Quadrant for Data Science and Machine Learning Platforms.
Learning to work with MATLAB has several strengths and advantages compared to other statistical programs, such as SPSS and R:
- MATLAB is a great educational platform for quantitative skills building, learning from data and working with interactive documents;
- MATLAB is standard in many academic settings and offers a vast range of ‘data tools’, packages, and course materials to facilitate efficient and effective learning;
- MATLAB’s documentation is generally recognized as ‘incomparable and exceptional’;
- In MATLAB it is easy to e.g. get started with reusable code and replication packages;
- MATLAB is user-friendly. It is easier to learn than R and, like SPSS, it offers many dialog boxes for users who prefer default dialog boxes to coding screens;
- MATLAB has many more applications than programs like R and SPSS, which are primarily used for statistical data analysis;
- Data handling and programming skills learned from using MATLAB can be easily transferred to other programs.
This course consists of four 3 hour sessions which combine examples for and hands-on exercises in importing, cleaning, merging and analyzing data in regard to topics and applications in the social sciences. Students are encouraged to bring their own data. During the course students can work alone or in small topic groups.
After completing this course participants will:
- Master elementary quantitative data skills for scientific research with MATLAB;
- know how to install and use the MATLAB toolboxes and packages;
- understand the applicability of MATLAB for their research projects.
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.
Session 1: Building your MATLAB Data Skills
The first session focuses on learning to work with the MATLAB Live Editor and learning basic MATLAB skills to prepare research data for further exploration and analysis.
Before attending please register for a Mathworks account and install the latest MATLAB software version. You can find all information about MATLAB for employees in MyEUR here.
Session 2: Bring Your Own Data (BYOD)
Students are encouraged to bring their own data (BYOD) in this session. Students are guided on how to apply MATLAB to their own research projects and which toolboxes and packages are available for (re)use. Students also learn to work with functions and loops in MATLAB.
Session 3: Featured topics and MATLAB toolboxes
Depending on the needs of the students, we will further discuss a number of possible topics, such as the TDM Toolbox, Statistics Toolbox, Mapping Toolbox, Data Visualisation (Advanced plots), Working with large datasets in MATLAB (Data Stores, Image Store, Tall Arrays), and Hands-On Exercises.
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. The focus of this session is practical knowledge sharing and transfer of learned MATLAB skills. Students demonstrate what they have learned so far through examples of MATLAB workflows, data visualisations and analysis examples.
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 has extensive experience with research data management, data-preprocessing and data analytics in various science disciplines. He has an interest in statistics, cognitive science and machine learning. Rob Grim has a background in Psychology.