Data Analysis in Python

Bachelor 2023/2024

Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'

Category 'Best Course for New Knowledge and Skills'

Type: Compulsory course

Area of studies: Business Informatics

Delivered by: Department of Business Informatics

Where: Graduate School of Business

When: 2 year, 3 module

Mode of studies: offline

Open to: students of one campus

Instructors: Yury Sanochkin

Language: English

ECTS credits: 3

Contact hours: 36

Full 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
Activity
Assessing student attendance and activity at seminars, as well as activity at lectures
Exam
The exam is practical work performed by students based on the results of mastering the course.

Interim Assessment

2023/2024 3rd 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
GLADKOVA MARGARITA ANATOLEVNA

Course Syllabus