Master 2022/2023

Intro to Programming in Python

Type: Elective course (Comparative Social Research)

Area of studies: Sociology

Delivered by: School of Sociology

Where: Faculty of Social Sciences

When: 2 year, 1 module

Mode of studies: distance learning

Online hours: 16

Open to: students of all HSE University campuses

Instructors: Ekaterina Mitrofanova

Master’s programme: Comparative Soсial Research

Language: English

ECTS credits: 4

Contact hours: 6

Course Syllabus

Abstract

The readings, quizzes, and coding challenges will contribute to the "Review Quizzes" part of the course. Website https://stepik.org/course/84164/promo

Learning Objectives

• To get familiar with Python

Expected Learning Outcomes

• Know the Basics of Numbers and Variables
• Can Use Functions
• Can Apply a Datatype for Text
• Can Work with Errors when Mixing Datatypes
• Can Work with Defining Functions and Floating-Point Numbers

Course Contents

• The Basics of Numbers and Variables
• Using Functions
• A Datatype for Text
• Errors when Mixing Datatypes
• Defining Functions. Floating-Point Numbers

Assessment Elements

• Test
• Quiz

Interim Assessment

• 2022/2023 1st module
  According to the information at the website

Bibliography

Recommended Core Bibliography

• A Tutorial on Machine Learning and Data Science Tools with Python. (2017). Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.E5F82B62
• Landau, R. H., Bordeianu, C. C., & Páez Mejía, M. J. (2007). Computational Physics : Problem Solving with Python (Vol. Second revised and enlarged edition). Weinheim: Wiley-VCH. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1016329
• Programming Python : [covers Python 2.5], Lutz, M., 2006
• Rubio, D. (2017). Beginning Django : Web Application Development and Deployment with Python. [Berkeley, CA]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1623501

Recommended Additional Bibliography

• Derivatives analytics with Python : data analysis, models, simulation, calibration and hedging, Hilpisch, Y. J., 2015
• H, S. (2013). A Byte of Python. Place of publication not identified: H, Swaroop. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsotl&AN=edsotl.OTLid0000581
• Learning Python : [covers Python 2.5], Lutz, M., 2008