Introduction to Python

July 27 – August 7, 2026

Monday - Friday, 13.20 - 16.20

In-person

Language: English 

Course Description

The course covers the basics of Python programming and data analysis. The first part presents main Python topics: data types, conditions, loops, collections, and functions. In the second part, students will explore data libraries: numpy and pandas for working with dataframes, matplotlib, and seaborn for visualization. Each day of the course includes two classes: theory and practice. Course content comprises numerous practical assignments and small projects, allowing students to apply their acquired skills immediately. Upon completion, students are expected to be able to create algorithms, work with dataframes, and use basic data analysis and visualization techniques in Python.

Why Choose This Course?

Python remains one of the most popular programming languages in the world, and data analysis is a key area of technological development. This course combines fundamental programming training with hands-on experience working with real data, which enables participants to gain practical skills from the very first lesson. The programme is designed with modern trends and includes tools used by analysts and developers worldwide.

Content 

Lesson 1. Introduction to Python

Lesson 2. Operators and calculations

Lessons 3-4. Strings, lists

Lessons 5-6. Conditions, loops

Lessons 7-8. Collections: Sets, tuples, dictionaries

Lesson 9. Functions

Lesson 10. Practice on fundamentals (team mini-project)

Lesson 11. Presentation of the team mini-project

Lesson 12. Working with files and modules

Lessons 13-16. Working with datasets: introduction to pandas, indexing and filtering, data processing, grouping and aggregation

Lessons 17-18. Visualization: Matplotlib, seaborn

Lessons 19-20. Final practice + Exam (optional: team project or lab work)

Skills and Competence

Students will develop basic programming and data analysis skills as well as will be able to create simple programs, solve practical problems, and analyze data: they will know how to load and process data, clean it, create graphs, and draw conclusions from it. The course develops fundamental skills in logical thinking, data analysis, and drawing simple analytical conclusions - the foundation for further study in programming and data science.

Teaching Methods

Practical training.

Prerequisites

The course has no formal prerequisites.

Final Assessment 

Exam (optional: team project or lab work).

Course is taught by

Anastasia Murach; Vasily Khodakovsky.