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Analytics using SAS

Analytics using SAS

SAS Analytics provide an integrated environment for predictive and descriptive modeling, data mining, text analytics, forecasting, optimization, simulation, experimental design and more. From dynamic visualization to predictive modeling, model deployment and process optimization, SAS provides a range of techniques and processes for the collection, classification, analysis and interpretation of data to reveal patterns, anomalies, key variables and relationships, leading ultimately to new insights and better answers faster.



Module-1 (Introduction to Business Analytics)
• Relevance in industry and need of the hour
• Types of analytics – Marketing, Risk, Operations, etc
• Future of analytics and critical requirement

Module-2 (Fundamental of Statistics)
• Basic statistics; descriptive and summary
• Inferential statistics
• Statistical tests

Module-3 (Data Prep & Reduction techniques )
• Need for data preparation
• Outlier treatment
• Flat-liners treatment
• Missing values treatment
• Factor Analysis

Module-4 (Basic Analytics)
• 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
• Hypothesis Testing Null/Alternative Hypothesis formulation
• One Sample, two sample (Paired and Independent) T/Z Tes
• P Value Interpretation
• Analysis of Variance (ANOVA)
• Chi Square Test
• Non Parametric Tests (Kruskal-Wallis, Mann-Whitney, KS)
• Correlation

Module -5 (Customer Segmentation)
• Basics clustering
• Deciles analysis
• Cluster analysis (K-means and Hierarchical)
• Cluster evaluation and profiling
• Interpretation of results

Module-6 (Regression Modeling)
• Basics of regression analysis
• Linear regression
• Logistic regression
• Interpretation of results
• Multivariate Regression modeling

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