KINT 8095 - Python for Social Scientists

This course consists in an introduction to the python programming language, tailored for social sciences practices. As such, after a short review of what the students already know and making sure everyone has a solid understanding of python's basic mechanisms, students will experiment with various topics such as web scraping, crawling, API usage, data wrangling, machine learning, data visualization, fuzzy matching, etc. This course will also be an occasion to discuss broader computer science topics such as algorithmics, information theory, graph theory etc. so that students may re-apply skills learned during this course in different contexts and with different programming languages.

Learning Outcomes

1. Programming skills in Python

2. Basic skills in computer science

3. Data science literacy

4. Insights about engineering tasks for research

Professional Skills

Ability to develop scripts written in Python to solve real-life data science problems. Related skills will be equally useful for a career in academia, in the public/international sector or in the private sector.

Guillaume PLIQUE
English
- In Class Presence: 4 hours a week / 24 hours a semester - Research and Writing for Individual Assessments: 4 hours a week / 48 hours a semester
Students are expected to attend the class with a proper computer (not a tablet or a pad, as it can be very complicated to develop and execute custom scripts on those). Some alternatives can be provided using cloud services in case a student does not possess a fitting computer.
Autumn and Spring 2023-2024
Python for Social Scientists
One individual assessment that will consist of full use-cases where students have to solve a problem using python scripts. The scripts, their documentation and the resulting data, typically, will be graded and will count toward 90% of the total grade. Participation will also be graded as 10% of the total grade.
This course is geared toward 10-20 students and as such, a lot of time is dedicated to coaching students individually when they start working on the numerous use-cases and exercises of the course. We will therefore have plenty of time for individual feedback.