Магистратура
2026/2027
Основы программирования в R и Python
Статус:
Курс обязательный (Аналитика данных и прикладная статистика / Data Analytics and Social Statistics)
Где читается:
Факультет социальных наук
Когда читается:
1-й курс, 1 модуль
Онлайн-часы:
40
Охват аудитории:
для своего кампуса
Язык:
английский
Кредиты:
3
Контактные часы:
24
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
- As a result of completing the course, the student will learn how to work in key environments for working with the R and Python languages (Rstudio, Jupyter Notebook, Google Colab), will acquire basic programming skills, and will gain skills in basic analytical procedures (calculating statistical metrics) and creating visualizations using these programming languages.
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 has a basic understanding of data structures in Python and knows which data types to use in which situations.
- The student knows how to create and modify variables, perform arithmetic operations, and create and use functions
- A student performs arithmetic and statistical operations using NumPy
- The student knows how to perform basic operations on tables in pandas
- The student knows how to open .ipynb files and work in the Jupyter Notebook environment, as well as how to create code cells and format Markdown cells.
- 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
- Python basics
Assessment Elements
- Final projectThe final project is designed to demonstrate basic programming skills in R and Python and consists of a small data analysis 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. Can be completed only 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.
- Quizzes4 sets of test questions (on the R language) and 4 sets code-solving assignments (Python) designed to be completed in SmartLMS
Interim Assessment
- 2026/2027 1st module0.4 * Quizzes + 0.2 * Homework assignments + 0.4 * Final project
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
- Grus, J. (2019). Data Science From Scratch : First Principles with Python (Vol. Second edition). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2102311
- R for data science : import, tidy, transform, visualize, and model data, Wickham, H., 2017
- 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
- Python in a nutshell : a desktop quick reference, , 2023