This page is a complete repository of statistics tutorials which are useful for learning basic, intermediate, advanced Statistics and machine learning algorithms with SAS, R and Python.;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. 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

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, R and Python. It would give you an idea how these algorithms works in background and how to perform these statistical techniques with statistical packages. It includes both theoretical as well as technical explanation.- Basic Statistics: Types of Variables
- Descriptive Statistics
- When and why to standardize variables
- Significance Testing: Independent T-Test
- Partial and Semipartial Correlation
- Linear Regression with R
- Linear Regression in Python
- Learn Python for Data Science from Scratch
- Logistic Regression with R
- Logistic Regression with SAS
- 15 Types of Regression
- Cluster Analysis using SAS
- Cluster Analysis with R
- Validate Cluster Analysis
- Market Basket Analysis with R
- Principal Component Analysis with SAS
- Variable Selection / Reduction with R
- Variable Selection with Boruta Package
- Selecting the Best Linear Regression Model
- Ridge Regression with SAS
- Mixed Regression Simplified
- Time Series Forecasting : ARIMA
- Support Vector Machine Simplified
- Variable Clustering (PROC VARCLUS)
- Detecting Multicollinearity in Categorical Variables
- Detecting Non-Linear and Non-Monotonic Relationship
- Model Performance in Logistic Regression
- Model Validation in Logistic Regression
- Model Monitoring in Logistic Regression
- Bootstrapping Logistic Regression
- Effect of Oversampling for Rare Events
- Weight of Evidence (WOE) and Information Value (IV)
- Difference between linear regression and logistic regression
- Checking Assumptions of Multiple Linear Regression
- Homoscedasticity Explained
- Detecting and correcting multicollinearity problem
- Detecting and solving outlier problem
- Difference between R-Squared and Adjusted R-Squared
- Standardized vs Unstandardized Coefficients
- Difference between CHAID and CART
- Relative Importance Analysis with SPSS
- Detecting Interaction in Regression Model
- Variable Selection - Wald Chi Square Analysis
- Understanding Concordant, Discordant and Tied Pairs
- Chi-Square : Variable Reduction Technique
- Modeling Myth: General linear model and generalized linear model mean the same thing

## 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.- Observation and Performance Window
- Bias-Variance Tradeoff
- Variable Selection / Reduction
- Decision Tree with R
- Random Forest in R
- K-Nearest Neighbor with R
- Support Vector Machine Simplified
- Ensemble Learning : Boosting and Bagging
- Random Forest on Imbalance Data
- Calculating Variable Importance with Random Forest
- Shortcomings in Random Forest Variable Importance
- Gradient Boosting Model (GBM)
- Market Basket Analysis with R
- Ways to Correct Class Imbalances / Rare Events
- Weighting in Conditional Tree and SVM
- Ensemble Learning - Stacking (Blending)
- Missing Imputation Techniques
- Cost Sensitive Learning For Churn Model
- Impute Missing Values with Decision Tree
- Treatment of Insignificant Levels of a Categorical Variable
- Calculating AUC of Validation Data with SAS

## 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.- Text Mining Basics
- Creating WordCloud with R
- Creating WordCloud by Demographic
- Twitter Analytics with R
- Named Entity Recognition with Python
- Sentiment Analysis with Python

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## 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.- How to get into Analytics Field
- Free Data Sources for Predictive Modeling and Text Mining
- Free Ebooks on R, Python and Data Science
- Companies using R
- Analytics Companies Using SAS in India
- Analytics Companies Using SPSS in India
- Data Analysis Tools : Excel, SPSS and SAS
- List of free statistical softwares
- List of free econometrics softwares
- Statistics Humor