Welcome to RStatistics.Net !

An educational resource for all things related to R language and its applications in advanced statistical computing and machine learning.

Who is this Website For?

  1. If you are a college student working on a project using R and you want to learn machine learning techniques to solve problems
  2. If you are a statistician, but you don’t have prior programming experience, our plugin snippets of R Code will help you achieve several of your analysis outcomes in R
  3. If you are a programmer coming from other platform (such as python, SAS, SPSS) and you are looking to get your way around in R
  4. You have a software / DB background, and would like to expand your skills into data science and advanced analytics.
  5. You are a beginner with no stats background whatsoever, but have a critical analytical  mind and have a keen interest in analytical problem solving.

Whatever your motivations, RStatistics.Net can help you achieve your goal.

Don’t Know Where To Get Started?

If you are completely new to R, the Getting-Started-Guide will walk you through the essentials of the language. Read and practice the step-by-step code snippets completely so you will find it much easier to build advanced models and algorithms later on.

If you prefer video lecture type of learning, we have created a video course, particularly for beginners who have not laid hands on R, yet.

If you wish to get started with statistical methods, regression modelling would be a good place to start. Move on to advanced regression types to get a broader perspective on which technique can be applied for various problem types. You can start reading about decision trees, cluster analysis and time series analysis in parallel. These are some of the essential techniques you are going to need for basic analytical problem solving. Association mining is useful for ‘suggest recommendations’ type of problems.

What Will I Find Here ?

This website is a R programming reference for beginners and advanced statisticians alike. You will find data mining and machine learning techniques explained succinctly with workable R code. The methods deal with the practical and application oriented aspects, so that you know which method can be applied to what problem. At relevant places, context specific code and syntax along with the specific use cases are discussed. When used effectively, you can boost the prediction accuracy and derive rich insights out of your analyses.

In this website, you will find learning resources, tutorials and articles on techniques to learn and perform statistical analyses and problem solving in various areas.

    Beginners: Learning resources and tutorials

  1. What is R
  2. Getting started with R
  3. Essentials of making plots and graphs (under construction)
  4. Practice Exercises
  5. R Tips and Trivia
  6. R Video Tutorials
  7. Recommended R Books
  8. Statistics

    Topics are in the order of foundational topics to advanced.

  9. Regression Modelling
  10. Simple linear regression with numeric example
  11. How to test a regression model for heteroscedasticity and if present, how to correct it?
  12. Advanced regression modeling with full work flow
  13. Statistical tests in R
  14. How to find the most important variables that contribute most significantly to a response variable?
  15. Model selection strategies
  16. Advanced regression models
  17. Logistic regression
  18. Discriminant analysis
  19. Naive bayes classification
  20. Understanding Time Series Analysis
  21. How to forecast a time series.
  22. How to detect breakouts in a time series?
  23. Machine learning, Data Mining and Advanced R

  24. Strategies to improve R code
  25. How to do parallel computing with R
  26. Advanced regression models
  27. Cluster Analysis
  28. Cubist
  29. Basic text mining and work cloud
  30. Support vector machines
  31. Random forests
  32. Association mining to build recommendation systems
  33. Decision trees

Write Back To Us!

We constantly strive to improve and add valuable learning material to this site. If you find any topic is difficult to understand and needs more explanation, or if you find typos or code breaks, we are eagerly waiting to hear from you. We will try our best to promptly attend to your  support, feedback and suggestions. We tweet nice R tips and interesting articles, so follow and tweet at us on Twitter.

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