Python for Data Analysis (Part 2)

2026/2027

Academic Year

ENG

Instruction in English

4

ECTS credits

Delivered at:

Department of Physics

Course type:

Compulsory course

When:

3 year, 3, 4 module

Course Syllabus

Abstract

The course is aimed to introduce data analysis using Python. The first part of the course is dedicated to the basics of Python where the topics related to the basics of this programming language are covered. The second part of the course introduces the work with real-life data within social sciences and international relations. The course is specifically designed for people with no prior experience in programming.

Learning Objectives

• To provide a hands-on introduction to Python and its basic applications in the field of data science.

Expected Learning Outcomes

• Select research question for the group project
• Skill of applying hypotheses testing and statistical inference.
• Skill of computing descriptive statistics

Course Contents

• Descriptive statistics. Measures of location: mean, median, mode. Measures of spread: standard deviation, interquartile range, range. Percentiles. Robust statistics. Data transformations.
• Interactive plots in Python. Introduction to plotly. Finding suitable representation of the data.
• Basics of probability theory. Distributions, sampling, t-tests. Introductory hypotheses testing and statistical inference.
• Introduction to linear regression. Estimation techniques. Evaluating the quality of the regression model. Model interpretation.
• Drawbacks of the linear regression approach. Stability of the coefficients across different parts of the dataset. Rolling estimations.
• Overfitting. Occam’s Razor principle. In-sample and out-of-sample model evaluation. Measuring predictive accuracy of the model.
• Class wrap-up and discussion of the group project.

Assessment Elements

• Домашнее задание
• Экзамен
  Экзамен проверяет комплексное владение материалом всех разделов и способность переносить методы на новые, не разбиравшиеся детально в течение модуля задачи.

Interim Assessment

• 2026/2027 4th module
  0.2 * Домашнее задание + 0.1 * Домашнее задание + 0.7 * Экзамен

Bibliography

Recommended Core Bibliography

• Python for data analysis : data wrangling with pandas, numPy, and IPhython, Mckinney, W., 2017