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Syllabus

Overview

Course & Instructor

What will you learn in this course? Our goal is to give you the ability to understand, explain, and perform modern social science research with a special focus on data analysis and inference. You will be able to read and understand the methodology of most academic articles in the social sciences, but more importantly you will have a foot in the door of the data science world. The ability to collect and analyze data in a sophisticated manner has become a crucial skill set for the modern job market across industries. Through a combination of lectures, hands-on exercises, and a final project, you will learn how to clean, visualize, and interpret complex datasets, gaining valuable insights into citizens' attitudes, behaviors, and broader political trends. Finally, you will obtain data literacy that will help you be a critical consumer of evidence for the rest of your life.

Draw The Owl

This course introduces students to cutting-edge open source research tools for social science data analysis.

  • We will rely on Python within Visual Studio Code (using an interactive Python script workflow), leveraging core data science libraries such as: pandas for data management, Altair for data visualization, and statsmodels for statistical modeling.
  • For reproducible academic writing and formatting, students will use Typst.

Goals

  1. Visualize, summarise, and analyse real-world data using reproducible code based on cutting-edge open source tools for data analysis.
  2. Empirically test theories, including the derivation of hypotheses, conceptualization, measurement and inference.
  3. Understand the scientific method and critically evaluate scientific information.

By the end of the course, you will be able to perform univariate and bivariate data analyses, have an understanding of multiple linear regression and statistical inference, and gain exposure to data science and computational methods.

Requirements

The course is designed for social science students with no previous experience of quantitative methods, statistics or computer programming.

  • The most important requirement is motivation to work hard on likely unfamiliar material.
  • The second most important requirement is to have access to a computer in order to participate and complete the various activities. From there, we will learn as we go!

Warning

In order to fight against digital inequalities among students, Sciences Po Bordeaux sets up a support system under certain conditions (presentation of a purchase invoice; N-1 tax notice of your reference tax household ; notification of CROUS scholarship; certificate of attendance for the current year).

For any questions relating to this device and in order to submit your request, please write only to the following address, specifying your needs:

Structure

This course has 12 modules that span over two semesters of 6 class meetings. During the class meetings students have the opportunity to accomplish various activities. The activities allow students to incrementally assimilate critical concepts that will lead them to deliver a fully reproducible end to end scientific paper.

Class Meetings

Each class meeting is divided into three distinct parts to build practical data analysis skills:

  • Theory & Research Concepts: The instructor introduces core methodological foundations using curated empirical political science articles as illustrative case studies.
  • Student Group Live Demo: A designated student group conducts a 10–15 minute hands-on demonstration using a prepared handout (Python script or Typst document). Presenting groups can book a meeting with the instructor ahead of time to get direction. This is immediately followed by a constructive peer discussion ( Peer Discussion) initiated by a randomly selected student group using the "I like, I wish, I wonder" feedback framework.
  • Hands-on Lab: Students work in VS Code with guided instructor coaching to apply these techniques directly to their survey data and advance their milestone deliverables.

Grading

The course is evaluated over two semesters through cumulative milestones, interactive participation, and a final reproducible research project:

Activities Percentage Description
Milestones 50% 5 cumulative milestones (10% each) bridging research design, data wrangling, visualization, and modeling
Participation 30% Student Group Live Demos (2 per group), peer discussion, and active engagement
Research Paper 20% Original, reproducible scientific paper (~4,000–5,000 words in Typst) with complete analysis code

Communication

This course uses a WhatsApp community as the main communication channel. Outside of course meetings, you can connect with peers and instructors by clicking the Chat tab at the top of this page and posting in an appropriate channel. The onboarding activity will walk you through getting started.

Asking questions publicly and providing answers to your peers' questions allows everyone to learn dynamically. Furthermore, if you have a question on a topic, it is likely that someone else has the same question. Finally, being active on the platform, whether by answering the questions of your peers or by asking questions, is strongly encouraged and will have a positive impact on your participation grade!

Reserve private messages or emails with the instructor for personal matters, or if needed, book a 1-on-1 meeting.