Introduction to R for Data Analytics | TSI

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Introduction to R for Data Analytics

Non-formal Education Programme "Introduction to R for Data Analytics"

Course Information

R is established as one of leading programming languages for data analytics. Today R is a tool of choice for many professionals in different industries. This introduction to R course will help you to start working in R environment and obtain a solid background for further progress in data analytics. The course starts from the basic syntax of R commands and lead you to your first data analysis in R.

The course is organized as a continuous practicing of your newly acquired skills.

Aim of the course

The course is designed to provide a quick and efficient introduction to R environment and develop participants' practical R programming skills. The course is best suited for:

  • Software developers, who want to dive into the data science
  • Statisticians and data analytics, who want to get acquainted with R


  • Computer proficiency
  • Programming experience (any language) is highly recommended
  • Background in statistics is beneficial, but not required

Learning outcomes

  • Obtain practical skills of R programming
  • Apply core techniques of data processing
  • Use R for data visualization
  • Run classical models and present the results
Duration of the program: on Fridays, from 18:15 till 21:15 (16 academic hours, 4 evening sessions 4 academic hours each)
Language of the Course: English
Start of the Course: 18 February 2020.*
* Course dates may change if the group is not completed
Lecturer: Dmitry Pavlyuk,


PricePay 220 EUR
TTI students and members of TTI alumni associationPay 200 EUR

[Important] Before you make a payment, make sure the group is completed.



Session 1. Basics of R programming

  • How it works (introduction to R console and RStudio)
  • Coding basics and variable types in R
  • Installing third-party packages
  • Vectors and lists
  • Basic programming structures (conditions, loops, functions)

Session 2. Handling data

  • Data frames
  • Importing data
  • Data pipes (tidyverse library)
  • Managing data (arranging, filtering, summarizing)
  • Descriptive statistics and grouped summaries

Session 3. R visualization tools

  • Basic plots (charts, scatters, histograms)
  • ggplot2 library
  • Aesthetic mappings
  • 2D and 3D plots
  • Plotting spatial data

Session 4. Basic data analytics in R

  • Model basics
  • Model building
  • Predictions
  • Running and understanding basic models
  • Visualizing models and their results


Dmitry Pavlyuk

Dmitry Pavlyuk

Postdoctoral researcher; assistant professor; head of TSI mathematical methods and modelling department. Dmitry holds two doctoral degrees – in economics and in engineering and has17 years of high school teaching experience.

Dmitry is an active researcher in the sphere of data analytics and a highly experienced R developer, the author of two R packages, accepted in the official Comprehensive R Archive Network (CRAN).



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