The following links describe a set of data science tutorials which are useful for learning basic, intermediate, advanced Statistics and machine learning algorithms. It covers some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, logistic regression, classification, market basket analysis, random forest, ensemble technique, clustering, and more. It covers predictive modeling with SAS and data science with R tutorials.

Statistics Tutorials

The following is a list of statistics 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.
- Basic Statistics: Types of Variables
- Descriptive Statistics
- Significance Testing: Independent T-Test
- Linear Regression with R
- Logistic Regression with R
- Logistic Regression with SAS
- 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
- Selecting the Best Linear Regression Model
- Ridge Regression with SAS
- Mixed Regression Simplified
- Time Series Forecasting : ARIMA
- Random Forest with R
- 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 for modeling, model development, model performance and model validation. It includes practical application of dealing assumptions of statistical techniques.
- Observation and Performance Window
- Bias-Variance Tradeoff
- Variable Selection / Reduction
- Decision Tree on Credit Data with R
- Random Forest Explained
- 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.

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'.
- How to get into Analytics Field
- Free Data Sources for Predictive Modeling and Text Mining
- Free Ebooks on R, Python and Data Science
- 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