Магистратура
2025/2026
Основы программирования в R и Python
Лучший по критерию «Полезность курса для Вашей будущей карьеры»
Лучший по критерию «Полезность курса для расширения кругозора и разностороннего развития»
Статус:
Курс обязательный (Аналитика данных и прикладная статистика / Data Analytics and Social Statistics)
Когда читается:
1-й курс, 1 модуль
Онлайн-часы:
40
Охват аудитории:
для своего кампуса
Язык:
английский
Кредиты:
3
Контактные часы:
8
Course Syllabus
Abstract
Students who have never programmed are afraid that it is difficult. This course is designed to introduce them to the basics of programming languages such as R and Python. This course will discuss the difference between these languages, the strengths of each of them. Students will learn the basics of programming and working with these languages.
Learning Objectives
- to provide students with the basic R and Python skills that will be required in other courses in the programme
Expected Learning Outcomes
- be able to create and work with vectors, matrices and lists
- be able to upload files to R space
- be able to visualize data
- have skills on performing descriptive statistics, exploratory data analysis
- know how to build simple and basic models
- The student knows how to create and modify variables, perform arithmetic operations, and create and use functions
- The student has a basic understanding of data structures in R and knows which data types to use in which situations.
Course Contents
- Data formats
- Starting working with data
- Exploratory data analysis
- Visualization
- Basic linear regression
- R Basics
Assessment Elements
- Final project
- Homework assignmentsHomework R is designed for students to demonstrate their ability to use the R programming language effectively. It focuses on analytical thinking, basic statistical modeling, and data visualization. Students are encouraged to explore a topic of their choice, applying R tools creatively to real or simulated data. This homework must be completed individually. Homework Python allows students to present their skills in work with Python, and represents a creative homework where students can choose their own topic of Homework. Can be completed only individually.
- Quizzes
Interim Assessment
- 2025/2026 1st module0.4 * Quizzes + 0.4 * Final project + 0.2 * Homework assignments
Bibliography
Recommended Core Bibliography
- An introduction to R : a programming environment for data analysis and graphics, Venables, W. N., 2009
- Gillespie, C., & Lovelace, R. (2016). Efficient R Programming : A Practical Guide to Smarter Programming. Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1435808
- R for data science : import, tidy, transform, visualize, and model data, Wickham, H., 2017
- Ren, K. (2016). Learning R Programming. Birmingham: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1409189
- Trejo, O., & C. Figliozzi, P. (2017). R Programming By Example : Practical, Hands-on Projects to Help You Get Started with R. Birmingham: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1682395
- W. N. Venables, & D. M. Smith. (2012). D.M.: An Introduction to R. Notes on R: A Programming Environment for Data Analysis and Graphics Version 2.15.0. R-project.org.
Recommended Additional Bibliography
- Simon N. Wood. (2017). Generalized Additive Models : An Introduction with R, Second Edition: Vol. Second edition. Chapman and Hall/CRC.
- The art of R programming : a tour of statistical software design, Matloff, N., 2011