Master 2019/2020

Data Analysis in Python

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: 1 year, 3 module

Mode of studies: distance learning

Instructors: Alexander Krasilnikov

Master’s programme: Applied Economics and Mathematical Methods

Language: English

ECTS credits: 4

Contact hours: 4

Course Syllabus

Abstract

This course will introduce students to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library.

Learning Objectives

• introduce students to the basics of the python programming

Expected Learning Outcomes

• will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.

Course Contents

• Jupyter Notebook
• DataFrame structures
• Manipulating DataFrames
• Statistical techniques

Assessment Elements

• online
• exam

Interim Assessment

• Interim assessment (3 module)
  0.5 * exam + 0.5 * online

Bibliography

Recommended Core 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

Recommended Additional Bibliography

• CONTENTS 1 Blender/Python Documentation 3. (2011). Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.3109D75A