Political Data Science Hackathon (ISSSV1337)

Course Material

Authors

Solveig Bjørkholt

Louisa Boulaziz

Eric Gabo Ekeberg Nilsen

Introduction

What to expect from this course

This course is designed around three main goals:

  1. Introduce and teach some basic data science skills.
  • Using R and RStudio
  • Collaboration using Github
  • Data gathering (webscraping, APIs and databases)
  • Visualization and dashboards
  • Programming with text
  • Machine learning
  1. Demonstrate how to work effectively in cross-disciplinary teams.
  • Some agile work principles.
  • Team-optimization.
  1. Give an opportunity to learn and produce something work life related.
  • Work on a problem statement provided by work life organizations.
  • Get some examples on how data science can be used in work life contexts.

What is a hackathon?

This course is designed to mimic a hackathon1. Hackathons are a bit like festivals. People come together in an intense and focused setting to have a shared experience. One of the main differences to a festival, I would say, is that in a hackathon you also work together in a team to create something. By definition, hackathons are events in which “a large number of people meet to engage in collaborative computer programming.” Usually, the end goal is to produce something, like a game, a software, or an API.

Hackathons are great because they allow us to be reminded of a few important facts: (1) Team-work is awesome when it works, (2) We can create great things in short time if we work with focus and (3) One of the best ways to learn is learning by doing. Oh, and failing is a part of the process.

Naturally, we do not expect you to sit tight six weeks in a row and work with iron focus on your tasks throughout the course. However, we hope that you will take some of the ideas behind hackathons to heart. This is a playground just as much as it is a course. Learning, creating and producing is supposed to be challenging, rewarding, frustrating and - especially - fun! We hope this course will allow you to experience just that.

What will this course teach me?

This course will not cover everything data science related. In fact, it will only give you a glimpse into the world of data science. However, hopefully it will give you the knowledge you need to pursue more knowledge. Data science is a highly cross-disciplinary field, and we need people with different competences. To illustrate, look at the figure below. Many would agree that data science is some sort of cross-over between computer science, statistics and domain expertise. Often, this means building deeper knowledge in one of the circles, and knowing enough about the other circles to apply it, typically working in team-based contexts to draw on each others specialties. Being able to understand, translate and work cross-disciplinary is an incredibly useful skill.

Requirements

  • Laptop: We do not have access to lab rooms, so you will need to bring your own laptop.

  • Attendance: We would like you to show up to every session. Formally, you must attend a minimum of 75% of the sessions in order to take the final exam,

  • Team work: We hope and expect that you work together with your team members on the problem statement. You can decide yourselves how you would like to work, but we recommend meeting physically at least two times a week. This makes it easier to track progress and it often makes working more rewarding. It is also useful to set up a Teams, Slack or other communication channels. Keep in mind that you may be on different levels with regard to programming skills, and you may come from very different backgrounds. Try to utilize this rather than view it as a hindrance. We reward teams that work to make each other better - both in terms of programming skills and other skills.

  • Evaluation: This course gives 10 ECTS and is a pass/fail course. Your team is evaluated as a whole. There are three parts of the evaluation:

    • A shared github repository with your code.

    • A report on approximately 10 pages fleshing out the problem statement, work process and solution written in Quarto.

    • A final presentation where you present the content of the report.

  • Administrative: If you get sick or cannot attend for other reasons, please inform iss@admin.uio.no. If you experience trouble within the group, would like to change groups or have any concerns about the team work, please contact the course holders.

  1. To the degree that it is possible to merge ideas from a hackathon with a university course.↩︎