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Course Syllabus

Course Syllabus


 

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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

Full SyllabusAsk Question

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

Learning Objectives

  • To get familiar with Python
Expected Learning Outcomes

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

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

Assessment Elements

  • non-blocking Test
  • non-blocking Quiz
Interim Assessment

Interim Assessment

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

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

Authors

  • MITROFANOVA EKATERINA SERGEEVNA