Live Course Module: SAS Course for Data Science

Total Duration: 36 Hours (6 Weeks)

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Week 1: Introduction to SAS & Data Science Basics (6 hrs)

1. Introduction to Data Science and SAS (1 hr)

   • Role of SAS in data science ecosystem

   • Comparison with Python/R

   • SAS software interface & environment

2. SAS Environment & Tools (2 hrs)

   • SAS Studio & Enterprise Guide

   • Libraries & datasets

   • Understanding data types & variables

3. Basics of SAS Programming (3 hrs)

   • SAS syntax rules

   • Data step & Proc step

   • Import/export datasets (Excel, CSV, databases)

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Week 2: Data Handling & Manipulation (6 hrs)

1. Reading and Writing Data (2 hrs)

   • Infile & Input statements

   • Proc Import/Export

   • Dataset formats

2. Data Cleaning & Transformation (2 hrs)

   • Handling missing values

   • Conditional statements (IF-THEN-ELSE)

   • Functions for text, numeric & dates

3. Data Integration & Subsetting (2 hrs)

   • Merge & concatenate datasets

   • Sorting & filtering

   • Keep & Drop statements

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Week 3: Exploratory Data Analysis (EDA) with SAS (6 hrs)

1. Descriptive Statistics (2 hrs)

   • PROC MEANS, PROC FREQ, PROC UNIVARIATE

   • Summarizing data

2. Data Visualization in SAS (2 hrs)

   • PROC SGPLOT, PROC SGSCATTER

   • Histograms, scatterplots, boxplots

3. Data Summarization (2 hrs)

   • PROC TABULATE, PROC REPORT

   • Pivot table style reporting

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Week 4: Advanced Data Preparation (6 hrs)

1. Advanced Functions & Arrays (2 hrs)

   • Math & string functions

   • Arrays in SAS

2. Data Restructuring (2 hrs)

   • PROC TRANSPOSE

   • Reshaping data for ML

3. Macros & Automation (2 hrs)

   • Macro variables

   • Writing simple macros

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Week 5: Statistical Analysis & Predictive Modeling (6 hrs)

1. Basic Statistical Tests (2 hrs)

   • PROC TTEST, PROC ANOVA, PROC CORR

   • Hypothesis testing basics

2. Regression Analysis (2 hrs)

   • PROC REG for linear regression

   • PROC LOGISTIC for classification

3. Machine Learning Basics in SAS (2 hrs)

   • Decision Trees (PROC HPSPLIT)

   • Clustering (PROC FASTCLUS)

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Week 6: Case Studies & Project (6 hrs)

1. Case Study 1: Customer Segmentation (2 hrs)

   • Clustering with real-world dataset

2. Case Study 2: Predictive Modeling (2 hrs)

   • Logistic regression for classification problem

3. Capstone Project & Presentation (2 hrs)

   • End-to-end workflow: Data import → Cleaning → EDA → Modeling → Reporting

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Expected Outcomes

• Hands-on experience with SAS programming for data science

• Ability to clean, manipulate & visualize data

• Build predictive & statistical models in SAS

• Apply SAS in real-world case studies for data-driven decision making