Python for data analysis in economics and management

2022/2023

Academic Year

ENG

Instruction in English

3

ECTS credits

Delivered at:

Department of Mathematical Economics (Faculty of Economics)

Course type:

Compulsory course

When:

1 year, 4 module

Instructor

Давыдова Виолетта Дмитриевна

Course Syllabus

Abstract

In this course students are introduced to the rapidly growing field of data analytics with the specific focus on Python programming language. Students will learn concepts, techniques and tools they need to make meaningful inferences from data. Students will be exposed to a real- world data sets to gain practical skills in data manipulations. Each week will involve lectures and seminars. In the final project students will build a working code that can be readily applied for exploratory data analysis in their own (future) research domain.

Learning Objectives

• To provide a hands-on introduction to Python and its basic applications in the field of data analysis.

Expected Learning Outcomes

• Mastering basic Python libraries for data science: numpy, pandas, matplotlib
• Using Python to carry out basic statistical modeling and analysis
• Аpplication of efficient data wrangling algorithms
• Аpplication of basic tools (plots, graphs, summary statistics) to carry out exploratory data analysis

Course Contents

• Introduction
• The built-in data structures and operations with it: part 1
• The built-in data structures and operations with it: part 2
• Functions
• Exploratory data analysis using Python packages: part 1
• Exploratory data analysis using Python packages: part 2
• Data visualization using Python packages: part 1
• Data visualization using Python packages: part 2
• Web scraping & parsing
• Introduction to data science

Assessment Elements

• Активность
• Контрольная работа
• Экзамен

Interim Assessment

• 2022/2023 4th module
  0.25 * Контрольная работа + 0.35 * Активность + 0.4 * Экзамен

Bibliography

Recommended Core Bibliography

• Müller, A. C., & Guido, S. (2017). Introduction to Machine Learning with Python : A Guide for Data Scientists: Vol. First edition. Reilly - O’Reilly Media.

Recommended Additional Bibliography

• Cady, F. (2017). The Data Science Handbook. Hoboken, NJ: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1456617

Authors

• DAVYDOVA VIOLETTA DMITRIEVNA
• SILAEV ANDREY MIKHAYLOVICH