Syllabus

Overview

Course Information

• Wednesdays 09:45am-11:15pm
• A.116 | Espace Numérique

In this course introduces students to the building blocks of data science and machine learning with real world data and applications mostly in political science. The course culminates in a co-authored research paper, written by the entire class. The project is scaffolded across weekly sessions so that students both learn data science concepts and apply them directly to building a publishable research output.

The ability to collect and analyze data in a sophisticated manner is now a crucial skill set for the modern job market across industries. Learning such methods and tools requires practice. This course follows a “learn-by-doing” approach and will place emphasis on gaining experience by analyzing real-world data and solving problems using cutting edge software that is free and open source. It will allow you to apply your theoretical knowledge and articulate the methods learned in an end to end research project throughout the course. The course is designed for social science students that are not necessarily familiar with quantitative methods or computer programming.

Learning support will be provided for at least one programming language, such as C++, Julia, Python, or R, but the choice of language supported may vary between years, depending on judged benefits to students, whether in terms of pedagogy or resulting skills.

• This year, we will use Python!

Goals

1. Visualize, summarise, and analyse real-world data using reproducible code based on cutting-edge open source tools for data analysis.
2. Understand the scientific method, critically evaluate existing information, and leverage such methods in a fully reproducible end to end scientific project.
3. Develop an ethical, theoretical, and methodological understanding of machine learning and artificial intelligence from a social scientist perspective.

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:

• Aides Numériques

Class Meetings

Roles and Rotation

Each week, students work in groups of four within one of the three rotating groups: Writers, Hackers, or Reviewers. Rotating roles ensure that every student gains experience in writing, coding, and critique, while also contributing consistently to the class project. Groups remain stable within a week, so each role is carried out collaboratively rather than individually.

• Writers: Draft and revise paper sections; present their draft in class; incorporate feedback from peers.
• Hackers: Prepare demo notebooks or scripts showing the data and methods behind the paper section; deliver a concise in-class demo; refine work based on feedback.
• Reviewers: Read drafts and demos before class; provide structured, constructive feedback during the session; record comments in the shared review document.

Weekly Structure

Live meetings are held once per week and are divided into three parts of roughly 30 minutes each: a collaborative paper exchange, a theory session, and hack-time. This rhythm is designed to keep practice, theory, and production tightly linked to the class paper.

1. Collaborative Paper Exchange (student-led, 30 min)

• Writers' group presents their draft (5-10 minutes).
• Hackers' group gives a short demo of the supporting data and/or methods (5-10 minutes).
• Reviewers' group provides structured feedback on both writing and methods (5-10 minutes).
• The instructor closes with synthesis, critique, and concrete next steps (5 minutes).

Pre-class requirements:

• Writers upload their draft 48h before class;
• Hackers post demo code/scripts 48h before class;
• Reviewers prepare at least 3 written comments/questions and share them, before the next class meeting.

2. Theory (instructor-led, 30 min)

The instructor expands on the week's concepts, linking student demos to broader literature, highlighting methodological trade-offs, and suggesting readings or strategies. Students are expected to follow along on their laptops and ask clarifying questions.

3. Hack-Time (collaborative practice, 30 min)

Hands-on group work applying methods to the paper or short in-class challenges. Students extend the demos, test tools on real data, and contribute small commits, notebooks, or issues to the shared repository. A short share-out closes the session.

Assessment & Expectations

The course is evaluated over two semesters through various activities allowing students to incrementally improve data science skills. By the end of the course, students will deliver an end to end empirical research project that is fully reproducible and that leverages the acquired data science skills.

• Participation: consistent attendance, quality of oral presentations, demo clarity, and peer feedback.
• Writing contributions: drafts uploaded on time, incorporation of feedback, clarity and citation quality.
• Code & reproducibility: Hackers' notebooks/code should run end-to-end and be documented; final repository must reproduce key results.
• Final paper & deliverables: Co-authored manuscript (typst), reproducible codebase (GitHub), cleaned datasets (or scripts to produce cleaned datasets), and supplementary materials.

Activities	Weight
Participation, peer review	30%
Writing contributions	30%
Coding contributions	30%
Final paper	10%

Communication

This course uses a WhatsApp community as its main communication platform. 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 the 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 with the instructor for personal matters.