Master
2023/2024
Programming in R and Python
Category 'Best Course for Career Development'
Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
Type:
Compulsory course
Area of studies:
Applied Mathematics and Informatics
Delivered by:
International laboratory for Applied Network Research
Where:
Faculty of Social Sciences
When:
1 year, 1 module
Mode of studies:
offline
Open to:
students of one campus
Master’s programme:
Applied Statistics with Network Analysis
Language:
English
ECTS credits:
3
Contact hours:
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
- 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
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.