2024/2025
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:
Маго-лего
Когда читается:
1 модуль
Онлайн-часы:
40
Охват аудитории:
для своего кампуса
Язык:
русский
Кредиты:
3
Программа дисциплины
Аннотация
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.
Цель освоения дисциплины
- to provide students with the basic R and Python skills that will be required in other courses in the programme
Планируемые результаты обучения
- 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.
Содержание учебной дисциплины
- Data formats
- Starting working with data
- Exploratory data analysis
- Visualization
- Basic linear regression
- R Basics
Промежуточная аттестация
- 2024/2025 1st module0.3 * Quizzes + 0.2 * Homework assignments + 0.5 * Final project
Список литературы
Рекомендуемая основная литература
- 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.
Рекомендуемая дополнительная литература
- 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