2025/2026

Программирование на Python для бизнес-аналитики

Статус: Маго-лего

Кто читает: Департамент бизнес-информатики

Где читается: Высшая школа бизнеса

Когда читается: 1 модуль

Охват аудитории: для всех кампусов НИУ ВШЭ

Преподаватели: Блинков Арсений Викторович, Саночкин Юрий Ильич, Эрденко Александр Сергеевич

Язык: английский

Кредиты: 3

Контактные часы: 24

Course Syllabus

Abstract

The Python programming language is one of the most popular programming languages. It can be used in almost all IT fields, from data analysis and automation of routine processes to game development. This course will provide students with advanced skills in Python programming and the use of Python for data processing tasks. Students will be able to apply the knowledge gained in their professional activities and, if desired, can delve deeper into a specific field through further courses.

Learning Objectives

• Application of Python programming language skills to solve practical tasks.
• Confident command of the Python programming language syntax.
• Ability to work with Python using object-oriented and functional programming paradigms.
• Ability to work with popular libraries used in data processing tasks.

Expected Learning Outcomes

• Develop and Debug Python Programs
• Evaluate and Compare Algorithmic Complexity
• Identify and Select Appropriate Data Structures
• Implement and Optimize Algorithms Using Selected Data Structures
• Manipulate and Analyze Data Using Python Libraries
• Implement Data Preprocessing and Integration Techniques
• Create Diverse Data Visualizations Using Python Libraries
• Apply Principles of Effective Data Storytelling and Design

Course Contents

• Python & Algorithmic Complexity Basics
• Data Structure Selection for Common Algorithms
• Python for Data Analysis Tasks
• Visualisations in Data Analysis

Assessment Elements

• HA
  Average grade for all practical homework assignments provided in the course
• Exam
  Exam is a practical work performed by students based on the results of mastering the course.
• Activity
  Assessing student activity at seminars, as well as activity at lectures

Interim Assessment

• 2025/2026 1st module
  0.35 * Activity + 0.4 * Exam + 0.25 * HA

Bibliography

Recommended Core Bibliography

• Algorithms and complexity, Wilf, H. S., 2002
• Grus, J. (2019). Data Science From Scratch : First Principles with Python (Vol. Second edition). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2102311
• Knaflic C.N. Storytelling with data: a data visualization guide for business professionals. New Jersey: Wiley, 2015.
• McKinney, W. (2018). Python for Data Analysis : Data Wrangling with Pandas, NumPy, and IPython (Vol. Second edition). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1605925
• Robert Sedgewick, & Kevin Wayne. (2014). Algorithms : Part I. [N.p.]: Addison-Wesley Professional. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1600534

Recommended Additional Bibliography

• Introduction to the design and analysis of algorithms, Levitin, A., 2012
• McKinney, W. (2012). Python for Data Analysis : Data Wrangling with Pandas, NumPy, and IPython. Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=495822
• Robert Sedgewick, & Kevin Wayne. (2014). Algorithms, Part II. Addison-Wesley Professional.
• 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.

Authors

• Sanochkin Yuriy Ilich