ANALYTICS TRAINING / PREDICTIVE MODELING ON SAS

Module 1 - Basic Analytics

1. Statistics Basics

• Introduction to Data Analytics and Statistical Techniques        
• Types of Variables, measures of central tendency and dispersion
• Variable Distributions and Probability Distributions
• Normal Distribution and Properties
• Central Limit Theorem and Application

2. Hypothesis Testing

• Null/Alternative Hypothesis formulation       
• One Sample, two sample (Paired and Independent) T/Z Test
• P Value Interpretation
• Analysis of Variance (ANOVA)
• Chi Square Test
• Non Parametric Tests (Kruskal-Wallis, Mann-Whitney, KS)

3. Multivariate Regression

• Introduction to Correlation - Karl Pearson and Graphical Methods      
• Spearman Rank Correlation
• OLS Regression - Simple and Multiple

Module 2 - Advanced Analytics 

1. Logistic Regression

• Non Linear Regressions using Link functions         
• Logit Link Function
• Binomial Propensity Modeling
• Training-Validation approach
• ROC-AUC, Lift charts, Decile Analysis

2. Factor Analysis

• Introduction to Factor Analysis - PCA 
• KMO MSA tests, Eigen Value Interpretation
• Factor Rotation and Extraction

3. Cluster Analysis

• Introduction to Cluster Techniques    
• Distance Methodologies
• Hierarchical and Non-Hierarchical Procedures
• K-Means clustering
• Wards Method

Module 3 - Time Series Analysis    

    

1. Introduction and Exponential Smoothening

• Introduction to Time Series Data and Analysis      
• Decomposition of Time Series
• Trend and Seasonality detection and forecasting
• Exponential Smoothing (Single, double and triple)

2. Arima Modeling

• Box - Jenkins Methodology      
• Introduction to Auto Regression and Moving Averages, ACF, PACF
• Detecting order of ARIMA processes
• Seasonal ARIMA Models (P,D,Q)(p,d,q)
• Introduction to Multivariate ARIMA

Module 4 - Advanced Data Mining  

    

1. Introduction to R/Rattle Environment

• R-Rattle GUI Familiarization     
• Rattle Tabs
• Data Import and Variable role setting
• Data Exploration and Visualization, Hypothesis Testing
• Data Manipulation, Standardization, Missing value Treatment

2. Statistical Analysis & Data Mining/Machine Learning

• Cluster Analysis using R-Rattle
• Association Rule Mining
• Predictive Modeling using
• Decision Trees
• Random Forests
• Adaptive Boosting
• Logistic Regression

3. Evaluating & Deploying Models

• Evaluating performance of Model on Training and Validation data      
• ROC, Sensitivity, Specificity, Lift charts, Error Matrix
• Deploying models using Score options
• Opening and Saving models using Rattle