Bachelor 2024/2025

Data Analysis in Python

Type: Compulsory course (Digital Product Management)

Area of studies: Business Informatics

Delivered by: Department of Business Informatics

Where: Graduate School of Business

When: 2 year, 4 module

Mode of studies: offline

Open to: students of one campus

Instructors: Yury Sanochkin

Language: English

ECTS credits: 3

Course Syllabus

Abstract

From the course one can learn programming skills in python. The course aims on beginners (people who have never written code) and covers a variety of different topics: from basic (syntax, data types, operators) to more complex and specific ones (OOP, async).

Learning Objectives

• The course is aimed to provide students with necessary knowledge and tools to write programs in python and understand code written by others.
• During the learning process, students will gain the ability to develop and deploy real python projects in teams of 3-4 people.

Expected Learning Outcomes

• Be able to set up python environment under any operating system;
• Understand key concepts of programming (procedural, functional paradigms, OOP, data types, algorithm complexity, TDD, etc.) and apply them;
• Be able to write scripts in python for the variety of different tasks;
• Be able to cover and support code with unit tests, linters, and formatters;
• Be able to find and read documentation on python libraries not covered by the course.

Course Contents

• 1. Introduction to programming and the Python language
• 2. Python script mode. Conditional execution
• 3. Loops
• 4. Bitwise operations. Error handling techniques
• 5. Functions and modules. Name scopes. Recursive algorithms
• 6. Python data model. Lists, sets, tuples
• 7. Introduction to algorithmic complexity. Sorting algorithms
• 8. Strings. Advances techniques of text processing, regular expressions
• 9. Special Python syntactic features for linear collections
• 10. Associative containers
• 11. File input-output
• 12. Elements of functional programming. Lambda functions, iterables, generators
• 13. Introduction to object-oriented programming
• 14. Object-oriented programming in depth
• 15. Decorators
• 16. Threads and processes in Python
• 17. Development & Deployment
• 18. Unittesting in Python
• 19. Advanced techniques

Assessment Elements

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

Interim Assessment

• 2024/2025 4th module
  0.21 * Activity + 0.4 * Exam + 0.39 * HA

Bibliography

Recommended Core Bibliography

• Downey, A. (2015). Think Python : How to Think Like a Computer Scientist (Vol. Second edition). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1105725
• Eric Matthes. (2019). Python Crash Course, 2nd Edition : A Hands-On, Project-Based Introduction to Programming: Vol. 2nd edition. No Starch Press.
• Learning Python : [covers Python 2.5], Lutz, M., 2008
• Lutz, M. (2011). Programming Python : Powerful Object-Oriented Programming (Vol. 4th ed). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=415412
• Sweigart, Al. Automate the boring stuff with Python: practical programming for total beginners. – No Starch Press, 2015. – 505 pp.
• Программируем на Python, Доусон, М., 2015

Recommended Additional Bibliography

• Baka, B. (2017). Python Data Structures and Algorithms. Birmingham, U.K.: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1528144

Authors

• Sanochkin Yuriy Ilich