Do you want to create sophisticated data analysis reports without writing code? This course introduces you to SAS programming using SAS Studio. The strength of SAS Studio lies in its visual point-and-click user interface that automatically generates SAS code behind the scenes. Everything you learn in this course can be seamlessly applied using SAS Enterprise Guide or SAS Viya, since all platforms utilize the same programming language and syntax.
Learning SAS offers advantages over learning R and Python for accomplishing data cleaning, statistical analysis, and visualization tasks. Whether you are new to SAS or using other SAS products, this course provides the SAS programming syntax and foundational knowledge needed for creating robust data reports.
The course features real-world examples, including analysis of election predictions, stock performance, oil and gold price trends, crime statistics, marketing metrics, and healthcare data. You will observe data science in action and discover how efficiently you can perform complex tasks and create sophisticated visualizations using SAS.
You will learn systematically how to create visualizations, including geographic maps. In most cases, you will accomplish this without writing any code as you work within the SAS graphical user interface. The course includes detailed explanations of the code that SAS IDE generates automatically, and you will learn how to modify this code to accomplish more advanced and specialized tasks.
By completing this course, you will:
- Become proficient with the SAS IDE environment
- Understand essential data visualization techniques
- Comprehend fundamental statistical analysis methods required in data science and analytics reports
- Resolve common data set quality issues
- Apply linear regression models for data prediction
- Write and execute SAS programs
Intended Audience:
This course is most suitable for data scientists and those pursuing careers in data analytics. Students who master this course content will be well prepared to pass the SAS certification exams for SAS 9.4 Base Programming (A00-231) and SAS 9.4 Programming Fundamentals (A00-215). Participants will also progress toward eligibility for the SAS 9.4 Advanced Programming (A00-212) certification. Consult with the instructor or customer service representative for supplemental materials to prepare for the Advanced Programming certification exam.
Course Outline:
Part I: Basics
- Data Science in Action
- Data Science Process
Case Study: Presidential Elections in Maine
- Population
- Gender
- Race
- Age
- Voter Turnout
- Winning Candidates in 2012
- Categories and Issues
- Factors Affecting Maine's Economy
- Modeling
- 2016 Predictions
- 2020 Predictions
Getting Started
- How to Install SAS Studio
- What Is SAS and SAS Studio
- Tour
- Tasks
- Reports
- Graphs
- Snippets
- Main Components of a SAS Program
- Data Step
- Variable Types
- Proc Step
- Libraries
- Accessing Your Existing Local Files
- Accessing Data in SAS Libraries
- Create a New Library
- Add a New Table to the Library
- INFILE Technique
Data Visualization
- Scatter Plot
- Scatter Plot Code
- Scatter Plot Relationships
- Plotting Multiple Scatter Plots in the Same Image
- Histogram
- Appearance Tab
- Series Plot
- Bar Chart
- How to Sort a Bar Chart
- Create a Histogram Using a Bar Chart
- Bubble Chart
- Maps
- Bubble Map
- Cluster Analysis
Statistical Analysis and Linear Models
- Statistical Analysis
- One-Way Frequency
- Summary Statistics
- Correlation Analysis
- T-Tests
- One-Sample T-tests
- Paired-Sample T-test
- Two-Sample T-tests
- Linear Models
- One-Way ANOVA
- N-Way ANOVA
Advanced Data Preprocessing and Feature Engineering
- Comment Statement
- Arithmetic Operators
- How to Represent Missing Values in Raw Data
- Comparison Operators
- PROC SQL Statement
- SELECT-WHERE Statement
- WHERE Clause
- SELECT-WHEN-OTHERWISE Statement
- DO Loops
Preparing Data for Analysis
- Label
- Format
- Create New Variables
- Rearrange the Dataset Variables
- IF Statement
- IF Statement Without THEN Statement
- IF-THEN Statement
- IF-THEN-ELSE Statement
- DROP Statement
- SET Statement
Regression
- Simple Linear Regression
- Multiple Linear Regression
- Logistic Regression
Project/Case Study
You will work with another dataset to practice by hand everything learned throughout the course. You will clean the data column by column, handling character, numeric, date, and time variables, and performing typecasting. You will calculate the correlation between the outcome variable and the predictor variables.
