Introduction to R for Data Analytics
Non-formal Education Programme "Introduction to R for Data Analytics"
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
- 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: 15.11.2019.*|
* Course dates may change if the group is not completed
|Lecturer: Dmitry Pavlyuk, Dr.sc.ing|
|Price||Pay 220 EUR|
|TTI students and members of TTI alumni association||Pay 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
- Running and understanding basic models
- Visualizing models and their results
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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