Python for Social Science: Introduction to Programming Language

The course for those who want to learn how to solve real-world data-related problems with Python programming environment! 

Course Description

Python is one of the most popular and rapidly developing programming languages. A clear syntax which facilitates learning and a plethora of built-in and third-party libraries made Python especially popular among academics and researchers of all kinds. Python has already been the first-choice language in Machine Learning and Data Science for a while, but as far as Social Sciences are becoming more digitally-oriented it is getting in demand by sociologists, economists, linguists, and other social researchers. 

The course is taught by the professionals who have hands-on experience in the field — Edgar Zhavoronkov, Software Engineer, working on Papeeria: online LaTeX editor, and Aleksei Shpilman, Director, Centre for Data Analysis and Machine Learning at HSE SPb.

Simple and powerful, Python is a great tool

Edgar Zhavoronkov
Course Lecturer

Python is the main instrument of a modern data scientist!

Aleksei Shpilman
Course Lecturer 


  • Why Python?
  • Basics of syntax
  • Ecosystem (Tools, IDEs, libraries, etc.)
  • Packages for exploratory data analysis and dataset preparation
  • Packages for machine learning application to prepared dataset
  • Packages for result analysis and interpretation

Skills and Competence

Upon completion of this course students will be able to apply sophisticated data analysis techniques to various tasks and write general-purpose code using Python.


Students should be familiar with the basics of command-line, text editor, etc. Having own laptop is also highly recommended. 

Teaching Methods

Lectures, workshops, case studies.

Final Assessment

During the course students complete certain coding tasks, and they get points for each of them. Total points will be transferred into а final grade.

Final Grade Background

Attendance (no less than 70% of the course), completion of tasks. 

Recommended Reading List

Mark Pilgrim, Dive Into Python (2004)

Mark Pilgrim, Dive Into Python 3 (2009)

Course Partner 

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