2025/2026
Programming in R and Python
Type:
Mago-Lego
Delivered by:
International Laboratory for Applied Network Research
When:
1 module
Online hours:
40
Open to:
students of one campus
Instructors:
Lika Kapustina
Language:
English
Contact hours:
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
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 * Final project + 0.2 * Homework assignments + 0.4 * Quizzes
Bibliography
Recommended Core Bibliography
- 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.