Data Preparation and Workflow Management (dPrep)

Instructor: dr. Hannes Datta LinkedIn GitHub X

Course codes: 328059-M3 (fall, block 1) and 328062-M3 (spring, block 3)

This edition: January - April 2024 | Next edition: August - October 2024

Engineer data sets and manage research projects efficiently

Welcome to the course website of dPrep.

This course teaches you to efficiently manage empirical (marketing) research projects, using tools such as R/RStudio and GitHub.

Please use the navigation bar and buttons below to access the course material.

Course schedule and modules Syllabus & learning objectives Enroll now!

Why should you take this course?

Many students and academic scholars perceive the process of doing research as rather simplistic: a bit of data cleaning here and analysis there, and you’re done. While this mostly isn’t a problem when working on small-scale projects, this approach won’t work anymore once you work on your empirical theses (e.g., for Marketing Analytics), or join an analytics-focused company.

Therefore, in this course, you will professionalize your way-of-working. In particular, we’ll zoom in on complex, reproducible data preparation workflows (think of structured and unstructured raw data, derived from multiple sources, provided in multiple delivery batches, with lots of missing data, etc.).

Throughout the course, you’ll be using open science principles documented at Tilburg Science Hub.