Data Analytics in R-language
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Course code
IFI7360.DT
old course code
Course title in Estonian
Andmeanalüütika R-keeles
Course title in English
Data Analytics in R-language
ECTS credits
4.0
Assessment form
Examination
lecturer of 2022/2023 Autumn semester
Maria Kunevich (language of instruction:English)
lecturer of 2022/2023 Spring semester
Not opened for teaching. Click the study programme link below to see the nominal division schedule.
Course aims
To create an opportunity for students to get acquainted with statistical programming language and computing environment R. To support the development of practical skills for managing, describing, visualizing and modelling data in R. To create a preconditions for using R language and programming functionality on various research problems.
Brief description of the course
R is a language for statistical computing and graphics. It provides a rich environment for working with, visualizing, and analyzing data. The course will serve as a basic introduction to R language and its statistical and graphical capabilities. It covers data handling, data manipulation, graphics, functions, basic programming, and a range of elementary statistical techniques such as classical statistical tests, regression and analysis of variance. The main objective of the course is to familiarize students with the R environment and language, to enable them to develop practical skills for independent problem solving and to expand their knowledge of R on their own.

Course covers the following topics: What is R? R-Studio. Basics of R-language. Creating and accessing objects in R.

Managing data in R (Reading and writing data, Manipulating data).Managing data using dplyr package. Functions in R.Basic programming in R (Writing functions, Loops and conditional statements). Descriptive statistics in R. Graphics (Basic plotting; Building a graph; Customizing a graph; Advanced graphics; Plotting with ggplot2 package, Plotting with lattice package).

Statistical tests in R. Statistical models in R (Linear regression, Logistic regression, Analysis of variance).

Introduction to more advanced statistical methods. Topics to be determined by student interests/requirements.
Learning outcomes in the course
Upon completing the course the student:
- can use R for basic statistical programming;
- can create and modify R datasets;
- can create figures and plots in R;
- can perform and interpret basic statistical tests;
- can perform and interpret basic statistical models;
- can write basic functions and use them in their own research.
Teacher
Maria Kunevich
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