# Statistics Tutorials : Beginner to Advanced

This page is a complete repository of statistics tutorials which are useful for learning basic, intermediate, advanced Statistics and machine learning algorithms with SAS and R. It covers some of the most important modeling and prediction techniques, along with relevant applications. Topics include hypothesis testing, linear regression, logistic regression, classification, market basket analysis, random forest, ensemble techniques, clustering, and many more. It covers predictive modeling with SAS and data science with R tutorials. In these days, knowledge of statistics and machine learning is one of the most sought-after skills. People who possess hands-on experience of these techniques are paid well in job market. In the world of automation, it's important to gain experience of machine learning algorithms to survive in the market.
 Statistics Tutorial : Step by Step Guide

Statistics / Analytics Tutorials with SAS and R
The following is a list of tutorials which are ideal for both beginners and advanced analytics professionals. It's a step by step guide to learn statistics with popular statistical tools such as SAS and R. It would give you an idea how these algorithms works in background and how to perform these statistical techniques with SAS and R. It includes both theoretical as well as technical explanation.

Predictive Modeling using SAS & R Training
This training covers both mathematical/statistical and real-world application of predictive modeling with case studies and live projects. You will also be guided through domain or business knowledge required while building predictive models. It includes case studies of marketing analytics, credit risk and HR analytics.
Predictive Modeling using SAS & R- Case Studies

Data Mining and Machine Learning Tutorials
The following tutorials would provide explanation of popular predictive modeling and machine learning algorithms. It covers steps of data preparation, variable selection / dimensionality reduction, model development, model performance and model validation.  Also it includes practical application of dealing assumptions of statistical techniques and how to treat them if they get violated.  You would also learn how to improve accuracy of a predictive model.

Text Mining with R
It includes fundamentals of text mining with practical case studies. It also covers how to visualize results of text mining. The popular techniques of text mining are also described in the following articles. These tutorials would help you to get started with text analytics and how to perform social media mining with R.

Other Resources
The links below would assist you to excel into analytics field. It includes tutorials ranging from 'How to enter into analytics' to 'What are the career prospects in analytics'. It would answer a lot of your questions - scope of SAS and R - if you are novice in analytics field. These resources would train you to work on a real world data science project.
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