Бакалавриат
2026/2027
Python для анализа данных
Статус:
Курс обязательный (Международная программа по бизнесу и экономике)
Где читается:
Санкт-Петербургская школа экономики и менеджмента
Когда читается:
2-й курс, 1, 2 модуль
Охват аудитории:
для всех кампусов НИУ ВШЭ
Язык:
английский
Кредиты:
4
Контактные часы:
56
Course Syllabus
Abstract
Python is an interpreted, high-level, general-purpose programming language. It is suitable for beginners while also being powerful enough to develop complex scripts and applications. This semester course is an introduction to the Python programming language. The average time to complete this course depends on the student’s background. A prerequisite for the course is mathematics at the secondary (high school) level. Students' academic performance is assessed through programming assignments in the form of homework and seminar exercises, oral answers in colloquium, as well as written assessments including midterm test and a final exam. Course assignments practice basic syntax rules, file input and output, and user-defined functions. The course does not include lectures; all theoretical materials are provided to students during practical sessions.
Learning Objectives
- Understand basic syntax rules, data types, and built-in constructs
- Create user-defined functions and work with files
- Become familiar with Python data science libraries: pandas, requests
- Develop basic skills in using Python as an analytical tool
Expected Learning Outcomes
- Student can explain basic principles of Python programming language
- Student can create scripts for automating processes
- Students can read and understand simple scripts
- Student can explain basic principles of Python programming language.
- Student can write scripts for automating processe.
- Student can read and understand simple scripts
- Student can find syntax and logical errors in scripts
- Student can perform basic data analysis using Python scripts
Course Contents
- Topic 1. Basic of Python programming
- Topic 2. Boolean data type and IF conditions
- Topic 3. Strings and string methods
- Topic 4. Lists, Tuples and its methods
- Topic 5. WHILE loops
- Topic 6. FOR loops
- Topic 7. Text files
- Topic 8. Dictionaries, sets
- Topic 9. Nested data structures. Sorting
- Topic 10. Functions
- Topic 11. Functions 2
- Topic 12. Recap
- Topic 1. Introduction
- Topic 2. Conditions and Boolean Algebra
- Topic 3. Ordered collections — part 1
- Topic 4. Ordered collections — part 2
- Topic 5. The WHILE loop
- Topic 6. The FOR loop
- Topic 8. Unordered collections
- Topic 9. Functions — part 1
- Topic 10. Functions — part 2
- Topic 11. Working with files
- Topic 12. Pandas — part 1
- Topic 13. Pandas — part 2
- Topic 14. JSON, requests
- Topic 15. Recap
- Topic 7. Loops, nested data structures, sorting
Assessment Elements
- MidtermMidterm covers all topics from the Syllabus (the first module material). Midterm consists of several paper-based tasks. The midterm is open-book: any amount of paper-based materials is allowed (printed or hand-written). During the midterm cheating is strongly prohibited: no additional electronic resources/devices; no talking to peers. In case of the rules violation the student gets zero points for the midterm. Duration: 2 academic hours (1h 20m). The maximum grade is 10.
- Graded SeminarsGiven out during seminars. Students individually complete the assignment during the seminar, and submit it no later than the end of the class. The format of the control element is offline. If the student is not present at the class in person during the control element, but has completed an attempt to pass the control element, a score of "0" is given for the corresponding control element. Each seminar cannot be retaken regardless of the reason for absence. The maximum grade for each graded seminar is 10, including tasks that check an outstanding student performance. Realized through SmartLMS. The grade is published no later than 5 workdays after the deadline. The list of sources allowed for use: • Online translators and dictionaries (with the exception of using built-in image translation functions and built-in chatbot modules with generative artificial intelligence, large language models, etc.) • Searching for information through search engines and usage of specialized websites (including Python documentation and libraries studied in the discipline) • Printed and handwritten notes or copies of lectures and seminars • Lecture files in .ipynb format List of prohibited sources: • Opening and/or usage of messengers, regardless of device and purpose • Opening and/or usage of chatbots with generative artificial intelligence, deep thinking etc. • Presence and/or usage of smartphones (in accordance with clauses 3.5.4.1. and 3.5.4.6. of the Student Internal Regulations at National Research University Higher School of Economics) • Other sources not allowed above and leading to violations of student's duties under sub-paragraphs 3.5 of the Student Internal Regulations at National Research University Higher School of Economics.
- HomeworkGiven out after corresponding seminars. Students have one calendar week to complete the assignment. Each Homework cannot be retaken regardless of the reason for absence. The maximum grade for each Homework is 10, including tasks that check an outstanding student performance. Homeworks are implemented in the SmartLMS system. The grade is published no later than 5 workdays after the deadline.
- ExamExam covers all topics from the Syllabus. Exam consists of several paper-based tasks. The exam is open-book: any amount of paper-based materials is allowed (printed or hand-written). During the exam cheating is strongly prohibited: no additional electronic resources/devices; no talking to peers. In case of the rules violation the student gets zero points for the exam. Duration: 2 academic hours (1h 20m). Maximum grade is 10.
Interim Assessment
- 2026/2027 2nd module0.2 * Midterm + 0.5 * Exam + 0.19 * Graded Seminars + 0.11 * Homework
Bibliography
Recommended Core Bibliography
- Python for Everybody - CCBY4_072 - Chuck Severance - 2022 - Open Educational Resources: libretexts.org - https://ibooks.ru/products/390857 - 390857 - iBOOKS
Recommended Additional Bibliography
- Майтак Р.В., Пылов П.А., Протодьяконов А.В. - Python, Django, Data Science - 978-5-9729-2143-0 - Инфра-Инженерия - 2025 - https://znanium.ru/catalog/document?id=469326 - 469326 - ZNANIUM