2023/2024
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
Охват аудитории:
для своего кампуса
Язык:
английский
Кредиты:
3
Контактные часы:
28
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 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
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
- 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.