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.
- 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.tablein R and joining data with
- If your more into data science, Python is probably the way to go. On Tilburg Science Hub, we also feature a couple of cool lessons on how to get started.
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.
- At the core of our thinking lies the use of automated project pipelines - learn how to create one for your project here.
- We recommend you to version and backup your code on GitHub. Maybe even we require you to use GitHub to download the raw data that you will work with during your thesis project. Make sure to sign up for an account, and tell us your username so we can add you to relevant code repositories.
- Get inspired by code snippets that we’ve used in our research projects (GitHub repositories, GitHub Gists)
- 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.
- Database management
- does your data sit somewhere in a database? Then learning SQL (for structured databases) or NoSQL (for unstructured databases) is recommended.
- Data collection
- Should you plan on collecting some data via web scraping and APIs, we recommend you to refresh your Python skills.