📍 Module 1: Introduction to SAS

• What is SAS?

• SAS for Data Science – Use Cases

• Understanding SAS Environment: Editor, Log, Output

• Libraries and Data Sets

• Data Science Workflow in SAS

📍 Module 2: SAS Programming Basics (Base SAS)

• Data Step and Proc Step

• Importing Data (CSV, Excel, Text)

• Variables, Data Types, and Labels

• Reading and Writing Datasets

• Creating and Using Formats

• Conditional Logic (IF-THEN, ELSE, SELECT)

• Loops and Arrays in SAS

📍 Module 3: Data Manipulation in SAS

• Sorting, Filtering, and Subsetting Data

• Combining Datasets: Merge & Append

• Using PROC SQL in SAS

• Data Aggregation with PROC MEANS, FREQ, and SUMMARY

• Data Cleaning Techniques

• Working with Date and Time Functions

📍 Module 4: Data Visualization with SAS

• Introduction to PROC SGPLOT, PROC GCHART

• Bar Charts, Pie Charts, Histograms

• Scatter Plots and Boxplots

• ODS (Output Delivery System) for Reporting

• Exporting Reports to PDF, Excel, HTML

📍 Module 5: Statistical Analysis

• Descriptive Statistics

• Probability Distributions

• Hypothesis Testing (t-test, Chi-Square, ANOVA)

• Correlation & Covariance

• Linear Regression with PROC REG

• Logistic Regression with PROC LOGISTIC

📍 Module 6: Predictive Modeling in SAS

• Building Predictive Models

• Feature Selection Techniques

• Model Training and Validation

• Decision Trees using PROC HPSPLIT

• Clustering with PROC FASTCLUS

• Model Performance Metrics (AUC, ROC, Confusion Matrix)

📍 Module 7: Advanced SAS Topics

• Macros in SAS

• SAS Functions and Automation

• Advanced PROC SQL Techniques

• Introduction to SAS Enterprise Miner (Optional)

• Time Series Forecasting Basics

• SAS Integration with Excel & SQL Databases

📍 Module 8: Real-Time Capstone Projects

• Credit Risk Analysis

• Sales Forecasting

• Patient Data Analytics

• Customer Segmentation

• Report Automation Project