Introduction into Python

2021/2022

Academic Year

ENG

Instruction in English

4

ECTS credits

Delivered at:

Department of Management

Course type:

Compulsory course

When:

2 year, 2, 3 module

Instructors

Елизаров Павел Андреевич

Krasilnikov, Alexander

Course Syllabus

Abstract

Python is an interpreted high-level general-purpose programming language. It has a set of powerful libraries for data analysis. It is a simple language for beginners to learn, though it is powerful enough for writing large applications. This 2-module course is an introduction to the Python programming language and data science. The average time to complete this course depends on student background. To complete the course, students are supposed to have mathematical skills at the high school level. Students’ academic performance is evaluated using programming assignments: homework and classwork. Also there is one mid-semester exam and final exam. The examples and problems used in this course cover such areas as text processing, HTML and data analytics. This course does not provide lectures and students must finish corresponding week of recommended online course before seminar class.

Learning Objectives

• Teach students how to create basic scripts, understand data types, statements and logical expressions; create own functions and use libraries.
• Collect, store, process and analyze data automatically with the use of scripting languages.
• Can identify the data needed for addressing the financial and business objectives.

Expected Learning Outcomes

• Student can create scripts for data analysis
• Student can explain basic principles of Python programming language
• Student can read and understand simple scripts.

Course Contents

• Basic of Python programming
• Boolean data type and IF conditions
• WHILE loops
• Lists and FOR loops
• Dictionaries and Methods
• Dictionaries
• Nested data structures. Sorting
• Functions
• Additional chapters: pandas
• Text files, tables, JSON
• Scraping: collection of links from website
• Additional chapters: re
• Additional chapters: charts

Assessment Elements

• Mid-semester exam
• Homework
• Classwork
• Exam

Interim Assessment

• 2021/2022 3rd module
  0.15 * Classwork + 0.4 * Exam + 0.25 * Homework + 0.2 * Mid-semester exam

Bibliography

Recommended Core Bibliography

• 9781491912140 - Vanderplas, Jacob T. - Python Data Science Handbook : Essential Tools for Working with Data - 2016 - O'Reilly Media - https://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=1425081 - nlebk - 1425081

Recommended Additional Bibliography

• 9781785284571 - Romano, Fabrizio - Learning Python - 2015 - Packt Publishing - http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=1133614 - nlebk - 1133614