Statistical system R ia a powerful and free tool. It is applied virtually in all fields of business and science. If you haven’t heard about R then learn why it is profitably to know R.

R is very often used in banks and other institutions to build predictive models, analyse data, test statistical hypotheses or automate data analysis workflows. Even is R is not the main tool in an institution then it is an excellent complement of it.

The attendants get practical skills. After the training they can analyse single-handedly their own data using R scripts got during the training.


What will you learn?

  • Learn basics of R and of powerful and free statistical system R.
  • How to read and write data in many formats.
  • How to programm in R language.
  • Basics of data analysis and attractive visual presentation of data.
  • You will work on these topics hands-on with a computer. It will take a half of the training.
  • You will get comprehensive materials and R scripts allowing you working single-handedly on your data.


For whom is this training?

Employees of departments working on data analysis and modelling (e.g. CRM, credit risk), controlling, audit, marketing, IT, and other departments who:

  • need to analyze data,
  • want to facilitate and automatize data analysis processes,
  • need to visualize data,
  • would benefit from using a flexible and powerful tool for any application of data analysis.


Shortened agenda

  • Short introduction to R
  • Data types and structures
  • Elements of programming in R
  • Introduction to data analysis in R
  • Graphical presentation of results
  • Input and output in R
  • Selected numerical methids in R


Full agenda

  1. Introduction to R environment
    • specificity of the tool and overview of features
    • installation and configuration
    • using R
    • help system
    • graphical user interface
    • R as a calculator
    • using built-in functions
  2. R basics: data types and structures
    • types of variables
    • objects and their basic properties (vectors, matrices, strings, lists, and data frames)
    • basic operations on objects
  3. Elements of programming in R
    • basics of R language
    • code flow controlling
    • writting own functions and scripts
  4. Introduction to using R in data analysis
    • reading data in selected formats
    • basic data operations
    • selected tools of descriptive statistics
  5. Graphical presentation of results
    • how to prepare readable and useful charts
    • high- and low-level graphical functions
    • exporting of graphics
    • how to increase attractiveness of your graphics: graphical parameters and their modification
    • elements of advanced graphics in R
  6. Communicating of R with outer world: input – output operations
    • using screen and keyboard
    • using files
  7. Selected advanced topics related to R programming
    • code optimization
    • writing good code: practical hints
    • resources control
  8. Calculations in R: selected numerical methods
    • numerical algebra
    • numerical optimization

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