Introduction to R for Data Analytics

Neformālās izglītības programma "Introduction to R for Data Analytics"

Informācija par kursu

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

Prerequisites

  • 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
Programmas ilgums: 08.03.2019. – 29.03.2019., on Fridays, from 18.15 till 21.15 (16 academic hours, 4 evening sessions 4 academic hours each)
Apguves valoda: English
Sākums: 08.03.2019
Pasniedzējs: Dmitry Pavlyuk, Dr.sc.ing

 

Kursu cenaMaksāt 220 EUR
TSI studentiem un AlumniMaksāt 200 EUR

[SVARĪGI] Pirms apmaksas veikšanas, pārliecinieties vai grupa ir nokomplektēta.

 

Kusa saturs

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

 

 

Reģistrācija ir obligāta!

 

 

 

 

kursi@tsi.lv, Transporta un sakaru institūts, Lomonosova iela 1 – 404.kab., Rīga, LV-1019, Latvija, tālrunis: (+371) 67100652

 

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