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SAS Fundamentals
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Getting Started
🟢 Course Slides
🟢 Overview: XSAS01: SAS Fundamentals
🟢 How This Course Works
🟢 Schedule, Key Terms and Resources
🟢 Download Your Course Files
Module 1: Data Science in Action
1.1 Data Science in Action
🟢 Overview
🟢 1.1.1 Where data science shows up
🔴 1.1.1b Reading a Table, In Depth
1.2 Data Science Process
🟢 Overview
🟢 1.2.1 The five stages, chapter by chapter
🔴 1.2.1b The Process Loops, In Depth
1.3 Case Study: Presidential Elections in Maine
🟢 Overview
🟢 1.3.1 Population, gender, race and age
🔴 1.3.1b Demographic Variables, In Depth
🔴 1.3.2 Voter Turnout
🔴 1.3.2b Turnout and Population, In Depth
🔴 1.3.3 Winning Candidates in 2012
🔴 1.3.3b Outcome Variables, In Depth
🔴 1.3.4 Categories/Issues
🔴 1.3.4b Choosing Explanatory Variables, In Depth
🟢 1.3.5 Modeling
🔴 1.3.5b Fitting and Scoring, In Depth
🟢 1.3.6 My 2016 Predictions
🔴 1.3.6b Scoring a Forecast, In Depth
🟡 1.3.7 The predictions checked against 2024
🔴 1.3.8 Factors Affecting Maine's Economy
🔴 1.3.9 My 2020 Predictions
Lab 1: Sign In and Open the Case
🟢 Overview
🔴 Task 1: Create your SAS OnDemand account and sign in to SAS Studio
🟢 Task 2: Run your first program on SASHELP and save it
🔴 Task 3: Map the data science process onto the Maine case study
🟢 Recap
🔴 Knowledge Check
Module 2: Getting Started
2.1 What Is SAS and SAS Studio?
🟢 Overview
🟢 2.1.1 SAS, SAS Studio and where they run
🔴 2.1.1b Where SAS Runs, In Depth
🔴 2.1.2 How Do You Install SAS Studio?
🔴 2.1.2b Your Free Account, In Depth
🔴 2.1.3 Your SAS OnDemand for Academics Account
2.2 Tour
🟢 Overview
🟢 2.2.1 The SAS Studio workspace
🔴 2.2.1b The Workspace, In Depth
🟢 2.2.2 Tasks
🔴 2.2.2b Tasks, In Depth
🔴 2.2.3 Snippets
🔴 2.2.3b Snippets, In Depth
🔴 2.2.4 Reports
🔴 2.2.5 Graphs
2.3 Main Components of a SAS Program
🟢 Overview
🟢 2.3.1 Data Step
🔴 2.3.1b The DATA Step, In Depth
🟢 2.3.2 Variable Types
🔴 2.3.2b Variable Types, In Depth
🟢 2.3.3 Proc Step
🔴 2.3.3b The PROC Step, In Depth
🔴 2.3.4 Reading the log: notes, warnings and errors
2.4 Libraries
🟢 Overview
🟢 2.4.1 Accessing Your Existing Local Files
🔴 2.4.1b Files and Paths, In Depth
🟢 2.4.2 Accessing Data in SAS Libraries
🔴 2.4.2b Libraries, In Depth
🟢 2.4.3 Create a New Library
🔴 2.4.3b New Library, In Depth
🟢 2.4.4 INFILE technique
🔴 2.4.4b INFILE Options, In Depth
🔴 2.4.5 PROC IMPORT, PROC EXPORT and Excel files
Lab 2: SAS Studio, Steps and Libraries
🟢 Overview
🟢 Task 1: Tour SAS Studio, run a task and write your own DATA and PROC steps
🟢 Task 2: Upload the kit, create a library and read a raw file with INFILE
🔴 Task 3: Import and export Excel, and fix three log errors on purpose
🟢 Recap
🔴 Knowledge Check
Module 3: Data Visualization
3.1 Scatter Plot
🟢 Overview
🟢 3.1.1 Scatter Plot Code
🔴 3.1.1b Scatter Options, In Depth
🟢 3.1.2 Scatter Plot Relationships
🔴 3.1.2b Reading Relationships, In Depth
🟢 3.1.3 Plotting More Than One Scatter Plot in the Same Image
🔴 3.1.3b Worked Example: Two Overlays and a Panel
3.2 Histogram
🟢 Overview
🟢 3.2.1 Shape of a distribution
🔴 3.2.1b Bins, Shape and Skew, In Depth
🟢 3.2.2 Appearance Tab
🔴 3.2.2b Titles, Labels and Colors in Code, In Depth
3.3 Series Plot
🟢 Overview
🟢 3.3.1 Values over time
🔴 3.3.1b Series Plots, In Depth
3.4 Bar Chart
🟢 Overview
🟢 3.4.1 Counts and sums by category
🔴 3.4.1b Bar Chart Options, In Depth
🟢 3.4.2 How Do You Sort A Bar Chart?
🔴 3.4.2b Sorting and Ranking Bars, In Depth
🔴 3.4.3 Create a Histogram Using a Bar Chart
3.5 Bubble Chart
🔴 Overview
🔴 3.5.1 A third variable as size
🔴 3.5.1b Bubble Chart Options, In Depth
3.6 Maps
🟢 Overview
🟢 3.6.1 Coloring regions by a value
🔴 3.6.1b Choropleth Maps, In Depth
🔴 3.6.2 Bubble Map
🟡 3.6.2a Pick the Right Chart
🔴 3.6.2b Bubble Maps and Basemaps, In Depth
3.7 Cluster Analysis
🔴 Overview
🔴 3.7.1 Grouping similar rows
Lab 3: Draw the Data
🟢 Overview
🟢 Task 1: Scatter plots, overlays and a histogram
🔴 Task 2: A series plot, a sorted bar chart, a bubble chart and a map
🔴 Task 3: Cluster analysis, and a histogram built from a bar chart
🟢 Recap
🔴 Knowledge Check
Module 4: Statistical Analysis and Linear Models
4.1 Statistical Analysis
🟢 Overview
🟢 4.1.1 One-Way Frequency
🔴 4.1.1b Frequency Options, In Depth
🟢 4.1.2 Summary Statistics
🔴 4.1.2b Summary Statistics, In Depth
🟢 4.1.3 Correlation Analysis
🔴 4.1.3b Correlation Options, In Depth
4.2 T-Tests
🟢 Overview
🟢 4.2.1 One-Sample T-tests
🔴 4.2.1b Reading One-Sample Output, In Depth
🟢 4.2.2 Paired-sample T-test
🔴 4.2.2b Why Pairing Matters, In Depth
🟢 4.2.3 Two-Sample T-tests
🟡 4.2.3a Which T-Test?
🔴 4.2.3b Equal Variances, In Depth
4.3 Linear Models
🟢 Overview
🟢 4.3.1 One-Way ANOVA
🔴 4.3.1b One-Way ANOVA, In Depth
🔴 4.3.2 N-Way ANOVA
Lab 4: Test the Numbers
🟢 Overview
🟢 Task 1: Frequencies, summary statistics and correlation
🟢 Task 2: Three t-tests and a one-way ANOVA
🔴 Task 3: An N-way ANOVA
🟢 Recap
🔴 Knowledge Check
Module 5: Advanced Data Preprocessing and Feature Engineering
5.1 Operators and missing values
🟢 Overview
🟢 5.1.1 Comment Statement
🔴 5.1.1b Comment Habits, In Depth
🟢 5.1.2 Arithmetic Operators
🔴 5.1.2b Operator Order and Missing Values, In Depth
🟢 5.1.3 How to Represent Missing Values in Raw Data
🔴 5.1.3b Missing Values and Raw Files, In Depth
🟢 5.1.4 Comparison Operators
🔴 5.1.4b Comparison Operators, In Depth
5.2 PROC SQL Statement
🟢 Overview
🟢 5.2.1 SELECT-WHERE Statement
🔴 5.2.1b SELECT and WHERE, In Depth
🟢 5.2.2 WHERE Clause
🔴 5.2.2b WHERE Versus IF, In Depth
🔴 5.2.3 Joins and summaries in PROC SQL
5.3 SELECT-WHEN-OTHERWISE Statement
🟢 Overview
🟢 5.3.1 Recoding with SELECT-WHEN
🔴 5.3.1b SELECT-WHEN In Depth
🔴 5.3.2 DO Loops
🔴 5.3.2b DO WHILE and DO UNTIL, In Depth
Lab 5: Clean and Query
🟢 Overview
🟢 Task 1: Comments, operators and missing values
🔴 Task 2: Query with PROC SQL and recode with SELECT-WHEN
🔴 Task 3: Joins and GROUP BY in PROC SQL
🟢 Recap
🔴 Knowledge Check
Module 6: Preparing Data for Analysis
6.1 Labels, formats and new variables
🟢 Overview
🟢 6.1.1 Label
🔴 6.1.1b Labels in Practice, In Depth
🟢 6.1.2 Format
🔴 6.1.2b Built-In Formats and PROC FORMAT
🟢 6.1.3 Create New Variables
🔴 6.1.3b New Variables, In Depth
🟢 6.1.4 Rearrange the Dataset Variables
🔴 6.1.4b Column Order, In Depth
6.2 IF Statement
🟢 Overview
🟢 6.2.1 IF (Condition) Without THEN statement
🔴 6.2.1b The Subsetting IF, In Depth
🟢 6.2.2 IF-THEN Statement
🔴 6.2.2b IF-THEN Conditions, In Depth
🟢 6.2.3 IF-THEN-ELSE Statement
🔴 6.2.3b Worked Example: BMI Groups
6.3 Reading, stacking and trimming tables
🟢 Overview
🟢 6.3.1 SET Statement
🔴 6.3.1b SET Options and Stacking Traps
🟢 6.3.2 DROP Statement
🔴 6.3.2b DROP, KEEP and Their Options
🔴 6.3.3 Combining tables with MERGE and BY
Lab 6: Shape a Table for Analysis
🟢 Overview
🟢 Task 1: Labels, formats, new variables and column order
🔴 Task 2: The three forms of IF, and SET with DROP
🔴 Task 3: MERGE the visits with the members
🟢 Recap
🔴 Knowledge Check
Module 7: Date, Time, and Datetime
7.1 Dates and times in SAS
🟢 Overview
🟢 7.1.1 How SAS stores dates, times and datetimes
🔴 7.1.1b Day Zero and Mislabeled Numbers, In Depth
🟢 7.1.2 Typecasting dates and times
🔴 7.1.2b Informats and Formats, In Depth
🟢 7.1.3 Extracting components
🔴 7.1.3b Datetimes and Date Parts, In Depth
🟢 7.1.4 Calculating durations
🔴 7.1.4b Intervals and Ages, In Depth
🔴 7.1.5 More date, time and datetime functions
Lab 7: Dates and Times
🟢 Overview
🟢 Task 1: Read and format dates and times
🔴 Task 2: Extract components and compute durations
🔴 Task 3: More date, time and datetime functions
🟢 Recap
🔴 Knowledge Check
Module 8: Strings
8.1 Cleaning character data
🟢 Overview
🟢 8.1.1 Parsing strings
🔴 8.1.1b SCAN, SUBSTR and CATX, In Depth
🟢 8.1.2 Searching strings
🔴 8.1.2b FIND, INDEX and Flags, In Depth
🟢 8.1.3 Unifying case and spacing
🔴 8.1.3b Cleaning Names, In Depth
🔴 8.1.4 More string functions
Lab 8: Clean the Names
🟢 Overview
🟢 Task 1: Parse and search the member names
🔴 Task 2: Unify case and spacing
🔴 Task 3: More string functions
🟢 Recap
🔴 Knowledge Check
Module 9: Regression
9.1 Simple Linear Regression
🟢 Overview
🟢 9.1.1 One predictor, one outcome
🟡 9.1.1a Regression Line What-If
🔴 9.1.1b Predictions and Residuals, In Depth
9.2 Multiple Linear Regression
🟢 Overview
🟢 9.2.1 Several predictors at once
🔴 9.2.1b Adjusted R-Square and Categorical Predictors, In Depth
🔴 9.2.2 Variable selection and multicollinearity
9.3 Logistic Regression
🟢 Overview
🟢 9.3.1 Predicting yes or no
🔴 9.3.1b Odds, Probabilities and the Cutoff, In Depth
🔴 9.3.2 Back to Maine: modeling the winner
Lab 9: Predict
🟢 Overview
🟢 Task 1: Simple and multiple linear regression
🟢 Task 2: Logistic regression
🔴 Task 3: Choose predictors and check multicollinearity
🟢 Recap
🔴 Knowledge Check
Module 10: What Is Next?
10.1 The whole process, once more
🟢 Overview
🟢 10.1.1 From question to communicated result
🔴 10.1.1b Worked Example: Question to PDF
🔴 10.1.2 Getting Help: Documentation, Communities and AI Assistants
🔴 10.1.3 The SAS certification path
🟢 Recap
🔴 Knowledge Check
Capstone
🔴 Capstone: Used-Car Prices from Raw Data
Instructor Only
Start Here
Setup and Delivery
Instructor Notes and Build Sheet
Pacing Card
Run Sheet
Questions You’ll Get and Page Map
Demo Talk-Tracks and Orientations
Knowledge Check Answer Key
Designation Review
Course Slides (PowerPoint)
Reference programs (Labs 1 to 9 and the Capstone)
QA Report
QA Report: Open SME Items
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AI Learning Hub
AI Essentials
Level 1: Generative AI Foundation
Level 2: Grounding AI with your Data
Level 3: Automating AI Workflows
Copilot First-Step Kit
Claude
ChatGPT
Microsoft Copilot
M365
Office
Excel
Word
Outlook
PowerPoint
Power Platform
Power BI
Power Apps
Power Automate
SharePoint
Microsoft Official
Adobe
CompTIA
Holding
Webinars
Programming
WordPress
Accounting
Google
Upskilling Series
Home
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SAS Fundamentals
Skill Level
:
Beginner
