Master
2025/2026
Selected Topics in Data Science
Type:
Elective course (Data Analytics for Politics and Society )
When:
1 year, 4 module
Open to:
students of one campus
Instructors:
Yaroslav Snarski
Language:
English
ECTS credits:
3
Course Syllabus
Abstract
During the course students will use Python and JupyterLab for Windows as a tool for data processing and statistical computations. Students are assumed to be familiar with high school math program, have basic computer literacy and some programming experience in R, Python or Julia, have courses of university level in statistics (e.g. Statistics 101), and be willing to work hard to learn the essentials of data science.
Learning Objectives
- The goal of this course is to acquaint students with data science methods and terminology, and to teach them how to implement these methods using Python programming language.
Expected Learning Outcomes
- understand key terminology from Data Science and its connection to Social Science research methodology
- choose data science algorithms appropriate to research questions
- use Python programming language for data analytics
- design and present explain extract, transform, load pipelines
- know key concepts, approaches, obstacles and limitations of applying Data Science tools to Social Science problems
Course Contents
- Topic 1: Intro to Data Science
- Topic 2: Summarization
- Topic 3: Prediction
- Topic 4: Inference
- Topic 5: Causality
Interim Assessment
- 2025/2026 4th module0.2 * Активность + 0.25 * Домашнее задание + 0.3 * Тест 1 + 0.25 * Тест 2
Bibliography
Recommended Core Bibliography
- Vanderplas, J.T. (2016). Python data science handbook: Essential tools for working with data. Sebastopol, CA: O’Reilly Media, Inc. https://proxylibrary.hse.ru:2119/login.aspx?direct=true&db=nlebk&AN=1425081.
Recommended Additional Bibliography
- 9781785284571 - Romano, Fabrizio - Learning Python - 2015 - Packt Publishing - http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=1133614 - nlebk - 1133614