Welcome to rstatisticsblog.com
This website is designed to serve as a self-help guide to those who seek to gain knowledge on topics related to statistical computing, machine learning, R programming concepts, predictive modeling, forecasting, and data visualization.
Here, you will find articles in chronological order, with reproducible R code and examples. The goal is to help you understand how to apply the data science concepts without the use of never-ending theory and fancy math. That is why all the articles are pretty straightforward, simple and include clear examples covering concepts of #rstats, #rstatistics, and #DataScience.
Structure of the website/book
The website is broken down into five sections.
- R Programming Tutorial- If you are entirely new to R, this section is a perfect place to start
- Data Science In Action - Under this section we will be covering concepts from the following two fields
- Statistics - These concepts will enable you to summarize, draw conclusions, and make a reliable forecast to improve business activities
- Machine Learning Algorithms - We will be covering a gamut of supervised and unsupervised algorithms
- Most Popular R Packages Tutorials - This section will enable you to learn some of the top most downloaded R package. Knowning these packages will further enable you to build powerful skills in terms of how to load, manipulate, visualize and model data.
- Other Useful Techniques - Techniques like variable selection, model tuning, pre-processing, and parallel processing are some example topics which we intend to cover in this section.
- Assignments for practice