ISSSV1337 – Political Data Science Hackathon

Schedule, syllabus and examination date

Choose semester

Course content

Digitalization is increasingly present in all areas of our societies, be it at professional or personal level. With the constant increase in available data across the world, how can we access and use datasets to address social, political and environmental questions relating to state and society? How do we use data for policy-informed choices for social and public good?

This course provides basic computer and programming skills as well as addressing such ethical issues and providing an introduction to the field intersecting between political science and data science.

Students will learn programming skills within R and explore how to organize project-based work in teams, including how to use Github. The end product will be an Rmarkdown report on a problem statement and a presentation.

This will be an exciting 6 week "hackathon" where student work is interactive. The course is project-oriented and team-based as we build a foundational understanding of programming and machine learning applied to practical questions and themes in political science. The course is connected to the Political Data Science (PODS) research group at the University of Oslo. The content varies from year to year according to current research areas and/or the special expertise of the course leaders and lecturers.

Learning outcome

In this course, students will:

  • Learn best practices on how to work in teams according to agile principles, including using version control (Github). Produce a data-driven report on a social issue based on descriptive and predictive work.
  • Understand how to use R for data import, data exploration and data manipulation.
  • Learn how to visualize data in R and basic principles regarding good visualization.
  • Learn how to apply machine learning models (both supervised and unsupervised) to make predictions from data.
  • Produce an Rmarkdown report presenting findings from data on a problem statement.
  • Orally present findings to relevant stakeholders.

Admission

This course will be an in person summer course 27 June - 5 August 2022 for current UiO students residing in Norway. 

The admission period for this course closed 31 March 2022. 

If you have any questions regarding admission, please contact iss@admin.uio.no

Prerequisites

Formal prerequisite knowledge

No obligatory prerequisites beyond the minimum requirements for entrance to higher education in Norway. Minimum academic requirements.

Teaching

The classroom sessions include daily teaching Monday-Friday 10:15 - 12:00 split into two hours. The first hour is focused on a traditional lecture format, while the second hour is space oriented towards group work on team-based projects and exercises. This format will change in the last week of the course with increased time allocated for team-based work.

  • 1st week: Intro to R and tidyverse. Learning how to work well in a team-based context. Intro to Github.
  • 2nd week: Getting and manipulating data. Data wrangling and merging. APIs, databases and webscraping.
  • 3rd week: Data visualization using ggplot and plotly. Learn how to produce an Rmarkdown-file.
  • 4th week: Basic statistics. Supervised machine learning (regression).
  • 5th week: Supervised machine learning (classification). Unsupervised machine learning. Some IT knowledge.
  • 6th week: Team-based work. Presentations

Tentative Weekly Schedule

Week Date Day Location Time
1 27.jun Mandag Blindern kl. 10:15-12
1 28.jun Tirsdag Blindern kl. 10:15-12
1 29.jun Onsdag Blindern kl. 10:15-12
1 30.jun Torsdag Blindern kl. 10:15-12
1 01.jul Fredag Blindern kl. 10:15-12
         
2 04.jul Mandag Blindern kl. 10:15-12
2 05.jul Tirsdag Blindern kl. 10:15-12
2 06.jul Onsdag Blindern kl. 10:15-12
2 07.jul Torsdag Blindern kl. 10:15-12
2 08.jul Fredag Blindern kl. 10:15-12
         
3 11.jul Mandag Blindern kl. 10:15-12
3 12.jul Tirsdag Blindern kl. 10:15-12
3 13.jul Onsdag Blindern kl. 10:15-12
3 14. jul - 15. jul langhelg    
         
4 18.jul Mandag SSB kl. 10:15-12
4 19.jul Tirsdag SSB kl. 10:15-12
4 20.jul Onsdag SSB kl. 10:15-12
4 21.jul Torsdag SSB kl. 10:15-12
4 22.jul Fredag SSB kl. 10:15-12
         
5 25.jul Mandag SSB kl. 10:15-12
5 26.jul Tirsdag SSB kl. 10:15-12
5 27.jul Onsdag SSB kl. 10:15-12
5 28.jul Torsdag SSB kl. 10:15-12
5 29.jul Fredag SSB kl. 10:15-12
         
6 01.aug Mandag SSB kl. 10:15-12
6 02.aug Tirsdag SSB kl. 10:15-12
6 03.aug Onsdag SSB kl. 10:15-12
6 04.aug Torsdag SSB kl. 10:15-12
6 05.aug Fredag SSB kl. 10:15-12

 

Examination

Explanations and appeals

Resit an examination

Special examination arrangements

Application form, deadline and requirements for special examination arrangements.

Evaluation

Pass/fail. Mandatory R markdown hand-in of project-based teamwork and mandatory project presentation. Daily attendance is expected of all participants. Students must attend a minimum of 75% of the lectures in order to take the final exam.

Facts about this course

Credits

10

Teaching

Every summer

Examination

Every summer

Teaching language

English