ISSSV1337 ¨C Political Data Science Hackathon

Course description

• Course content
• Learning outcome
• Admission
• Prerequisites

• Teaching
• Examination
• Evaluation

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 students are interactive. The course is project-oriented and team-based as we build a fundamental 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

If you would like to take this course, you must apply directly to the International Summer School. Apply here: https://nettskjema.no/a/326885

Only students admitted to the course may take part in instruction.

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

Examination

The evaluation is based on the student's contribution to the team-based project and the team's final presentation.

Explanations and appeals

• Explanation of grades and appeals

Resit an examination

• Illness at exams / postponed exams
• Resitting 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.