Data Science Week Spring 2025

March 3, 2025— March 10, 2025

What is the Data Science Week?

The goal of the data science week is to introduce interested students and staff to data science in a fun and cooperative way, and help create a community of data scientists at the University of Twente, the faculty of Behavioural and Management Sciences, and beyond. BDSi and DSI organize various events during the week, including a datathon, contextual speakers, expert lectures, hands-on workshops, and a networking drink.

During the lunch breaks expert data scientists will provide lectures on the most important tools in a data scientists’ toolbox; data wrangling, modelling, and communicating results. These lectures will be structured to support the datathon materials, but can be attended without participating in the datathon itself. The lectures are followed by a hands-on practical session in which the lunch lecturerer - supported by a team of motivated coaches - will guide participants in applying the lecture materials to their datathon submissions.

(Guest) speakers will be invited during the week to provide a deeper background in the topics and methods covered in the lectures and datathon, or to put these topics in a broader context. Throughout the week there will be ample time for socialization and networking, as well as a poster presentation session and networking drink on Thursday afternoon.

Datathon

A datathon is an event in which teams collaborate and compete to create a solution to a shared problem. By learning from experts and peers and immediately applying your skills on a relevant and engaging real-world dataset, the BDSi datathons provide a great environment for both students and staff, beginners and experts to further hone their skills. For the spring 2025, we will have a new and rewarding data challenge.

Speakers

Lectures & Practicals

Every lunch break (12:45 - 13:30, Tuesday - Friday) expert data scientists from BDSi and our partners will provide a lecture on the most important tools in a data scientists’ toolbox; data wrangling, modelling, and communicating results. These lectures will be structured to support the datathon materials, but can be attended without participating in the datathon itself.

After a short coffee break (13:30 - 13:45), the lecture will be followed by a hands-on practical session (13:45 - 15:30). During these two hours, the lecturer - supported by a team of motivated coaches - will support participants in applying the lecture materials to their datathon submissions. While these sessions are meant to accompany the days’ lecture, they can be attended by any datathon participants. Coaches will be on hand to answer any questions about the days’ lecture, the datathon, or data science in general.

Posters & Drinks

On thursday afternoon, we invite all data science week participants as well as anyone interested in data science at the University of Twente to join us for a poster presentation and drinks. This is a great opportunity to mingle with the other teams, and create lasting connections with peers and data science experts!

Competition

The team with the best solution will receive the coveted BDSi Data Science trophy. All teams will also be asked to share their solutions, problems, and learning experience during the final presentations.

Who can join?

Staff, students, family, and friends

Everyone related to the University of Twente and their friends and family can join. You can join with friends, colleagues or even family. The event is open to both novices and experts, and everyone in between. You can join the datathon as a team, alone, or skip it altogether and only participate in the workshops. If you do join alone, you can choose to be assigned to a team with other data science enthusiasts, or go at it alone.

Some experience with R or Python

If you have some experience with other programming languages, you should be able to follow along with a little preparation. More information on installing and using R can be found in the What can I do to prepare? section.

If you're new to programming in general or would like a deeper understanding of R, and would rather learn from one of our colleagues, the Cognition, Data and Education (CoDE) section provides courses and materials aimed at teaching staff and Johannes Steinrücke teaches half-day introduction to R and data visualization in R workshops for PhD's (and EngD's).

If you’re confident you can participate in the datathon in another programming language, you’re more than welcome to do so (we challenge you to try in C, Fortran, Brainf***, or JavaScript). Just be aware that we probably can’t offer support if or when you get stuck.

What can I do to prepare?

Get a team

First off, get a team together. The datathon is meant to be a collaborative experience where you work alongside a variety of expertises. While you can compete on your own, we strongly suggest working together.

Set up your coding environment

If you’re new to data science, you’ll want to set up a working environment. We recommend working in R or Python, depending on your experience.

Install R and RStudio, and prepare a working environment - Our colleague Johannes Steinrücke has written a good guide on how to set up R and RStudio for your projects, including some practical advice not covered in many other sources. The guide was written for students starting with coursework with R, but is equally applicable for other data science projects.

Install Python - The Women in Data Science team maintains a set of tutorials on installing Python (using Anaconda to manage packages and environments), Jupyter notebooks and the basics of Python data structures: https://github.com/keikokamei/WiDS_Datathon_Tutorials.

Further reading

If you’re looking for more information, a competitive edge, or just a good way to spend some time, we can recommend some more reading materials:

An Introduction to Statistical Learning is a free to download book providing an excellent introduction to practical machine learning using both R and Python.

R for Data Science is a free online book compiled by Hadley Wickham and a long list of community contributors, covering the whole gamut of modern data science in R. It is well worth a look, and a good reference even for experienced data scientists.

Kaggle.com provides resources to get started with Kaggle, as well as a long list of competitions that are approachable for beginners - with code and discussions available from hundreds of other participants. Trying your hand at a competition or two is a good way to spend a rainy weekend.

Sharada Kalanidhi has written an excellent deep dive into the 2023 WiDS datathon, including links to further resources for both R and Python: https://www.widsworldwide.org/get-inspired/blog/a-data-scientists-deep-dive-into-the-wids-datathon/.

Schedule

The spring 2025 Data Science Week takes place from March 3rd to March 10th. There will be an opening lecture and talk introducing the topic, the schedule for the week, and the technicalities of competing in the datathon.

The rest of the week, there will be lunch lectures on core data science skills, followed by practicals applying those skills to the problem in the datathon. Invited speakers will be fit into the schedule based on availability and opportunity. The data science week closes on Monday the 10th with presentations from the competing datathon teams, and a final keynote.

Further details will be made available in the coming weeks and months.

Monday

March 3

Opening & introduction

12:45 - 13:30, Location: Citadel T300

Welcome to the data science week, general information, etc.

Practical & Hands-on

13:45 - 15:30, Location: Citadel T300

Introduction to the datathon, finding and matching teams, using Kaggle, installing and setting up R/Python

Tuesday

March 4

Lunch lecture

12:45 - 13:30, Location: Citadel T300

Practical & Hands-on

13:45 - 15:30, Location: Citadel T300

Wednesday

March 5

Lunch lecture

12:45 - 13:30, Location: Citadel T300

Practical & Hands-on

13:45 - 15:30, Location: Citadel T300

Thursday

March 6

Lunch lecture

12:45 - 13:30, Location: Citadel T300

Practical & Hands-on

13:45 - 15:30, Location: Citadel T300

Posters & Drinks

16:00 - 18:00, Location: TBA

(Social) networking with other participants, and other University of Twente students and staff interested in data science.

Friday

March 7

Lunch lecture

12:45 - 13:30, Location: Citadel T300

Practical & Hands-on

13:45 - 15:30, Location: Citadel T300

Sunday

March 9

Submission deadline

23:59 - , Location:

Deadline for datathon submissions on the leaderboard.

Monday

March 10

Team presentations

12:45 - 13:30, Location: Citadel T300

Presentations by the datathon participants. Solutions, challenges, and lessons learned.

Prize ceremony

13:45 - 14:00, Location: Citadel T300

Prizes for the best and most innovative teams.

Final keynote

14:00 - 14:30, Location: Citadel T300

Final keynote speech and official closing of the Data Science Week.