Data Analysis in Python

Master 2022/2023

Type: Elective course (Applied Economics and Mathematical Methods)

Area of studies: Economics

Delivered by: Department of Economics

Where: St.Petersburg School of Economics and Management

When: 2 year, 1 module

Mode of studies: distance learning

Online hours: 30

Open to: students of all HSE University campuses

Instructors: Andrei Ternikov

Master’s programme: Applied Economics and Mathematical Methods

Language: English

ECTS credits: 6

Contact hours: 24

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Abstract

The course is aimed to introduce data analysis using Python. The first part of the course is dedicated to the basics of Python where the topics related to the basics of this programming language are covered. The second part of the course introduces the work with real-life data within social sciences and international relations. The course is specifically designed for people with no prior experience in programming.

Learning Objectives

Developing programming skills in Python in desktop and web-based interfaces

Expected Learning Outcomes

Collect, store, process and analyze data automatically with the use of scripting languages.
Develop and apply new research methods of basic machine learning algorithms and ways to collect information using data mining techniques.
Solve economic, financial and managerial problems using best practices of data analysis using modern computational tools.
Can identify the data needed for addressing the financial and business objectives.

Course Contents

Introduction to Python
Beginner Data Analysis in Python
Intermediate Data Analysis in Python

Assessment Elements

MOOC
Coursera MOOC: “Introduction to Data Science in Python” available here: https://www.coursera.org/learn/python-data-analysis [hereinafter “MOOC”]. Final progress [hereinafter “progress”] of the MOOC is based on several Programming Assignments. So, the student after finishing the course can get the progress in the interval from 0% to 100% including. Hint: The MOOC lasts for 4 weeks. Each student should register in the MOOC strictly within his/her corporate e-mail address (ending on @edu.hse.ru or @hse.ru) and your real First & Last names. The MOOC should be finished, and the progress should be submitted 7 days before the first day of the exam week (or earlier). The progress check and submission procedure are organized in LMS.
Individual Project in Python (Labs in Python)
Individual project consists of 18 computer exercises (in Python). Student should submit one file in Jupyter Notebook format (*.ipynb) with solutions. Each task has its own points. The final grade is calculated on the basis of the points’ sum. The maximum sum is 60. The student gets an integer grade for each task of a Project. If the answer on the particular question in the Project task is not full (not all requirements of the task are done), then the student gets 0 (zero) points for such a task/question. Moreover, the cheating is strongly prohibited. In case of cheating - the student gets 0 (zero) points for the whole Project.

Interim Assessment

2022/2023 1st module
0.5 * Individual Project in Python (Labs in Python) + 0.5 * MOOC

Bibliography

Recommended Core Bibliography

Vanderplas, J.T. (2016). Python data science handbook: Essential tools for working with data. Sebastopol, CA: O’Reilly Media, Inc. https://proxylibrary.hse.ru:2119/login.aspx?direct=true&db=nlebk&AN=1425081.

Recommended Additional Bibliography

Nelli, F. (2015). Python Data Analytics : Data Analysis and Science Using Pandas, Matplotlib and the Python Programming Language. [Berkeley, CA]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1056488

Authors

TERNIKOV ANDREY ALEKSANDROVICH

Course Syllabus