ISSSV1337 – Political Data Science Hackathon
Schedule, syllabus and examination date
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.
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.
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 email@example.com
Formal prerequisite knowledge
No obligatory prerequisites beyond the minimum requirements for entrance to higher education in Norway. Minimum academic requirements.
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
|3||14. jul - 15. jul||langhelg|
Explanations and appeals
Resit an examination
Special examination arrangements
Application form, deadline and requirements for special examination arrangements.
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.