Make sure you’re familiar with a set of software programs to meet your needs. On Tilburg Science Hub, you can learn about how to configure most of these software programs.

Statistical software

• by far most students use R for data preparation/cleaning and model estimation. We have collected a few tips on how to learn R here. We definitely recommend to you to get up to speed with data manipulation using data.table in R and joining data with data.table in R. Of course, dplyr and tidyverse are also a great packages to use!
• If your more into data science, Python is probably the way to go!

Managing data-intensive research projects

• Managing a data-intensive research project can be quite a challenge. That’s why we have created Tilburg Science Hub, which features a boot camp that shows you how to manage such projects efficiently. Check out some of the tutorials right away!

Code versioning and automated pipelines

• Please version your code on GitHub, and review the course material of Data Preparation and Workflow Management. Please sign up for a GitHub account, and tell us your username so we can add you to relevant code repositories.
• To work efficiently on your empirical research project, you will automate your project pipeline. Get started early on!
• Get inspired by code snippets that we’ve used in our research projects (GitHub repositories, GitHub Gists)

Writing

• Most students write their theses in Word, but a growing number of students has started to shift to a locally installed Latex distribution, or its (freemium) cloud-based alternative Overleaf. You can start typing directly in one of the Tilburg-branded templates.

Others

• Does your data sit somewhere in a database? Then learn SQL (for structured databases) or NoSQL (for unstructured databases)!
• Do you plan on collecting some data via web scraping and APIs, then we recommend you to refresh your Python skills and familiarize yourself with managing your scraping project.