Do you want to create data analysis reports without writing a line of code? This course introduces SAS programming using SAS Studio. The power of SAS Studio comes from its visual point-and-click user interface that generates SAS code. All that you will learn in this course, you can apply seamlessly using SAS Enterprise Guide or SAS Viya as all platforms use the same programming language and same syntax.
It is easier to learn SAS than to learn R and Python to accomplish data cleaning, statistics, and visualization tasks. If you use any other SAS product, this course will cover the SAS programming syntax and fundamentals needed to do your data reports.
The course provides real-life examples including analyzing election predictions, stocks, oil and gold prices, crime, marketing, and healthcare. You will see data science in action and how easy it is to perform complicated tasks and visualizations in SAS.
You will learn, step-by-step, how to do visualizations, including maps. In most cases, you will not need a line of code as you work with the SAS graphical user interface. The course includes explanations of the code that SAS IDE generates automatically. You will learn how to edit this code to perform more complicated advanced tasks.
You will:
- Become familiar with SAS IDE
- Understand essential visualizations
- Know the fundamental statistical analysis required in most data science and analytics reports
- Clean the most common data set problems
- Use linear progression for data prediction
- Write programs in SAS
Intended Audience
This class is most appropriate for data scientists and those wanting data analytics. Individuals mastering the content of this class will be qualified to pass the SAS certification exams for SAS 9.4 Base Programming (A00-231) and SAS 9.4 Programming Fundamentals (A00-215). Also, class participants will be on their way toward being qualified for the SAS 9.4 Advanced Programming (A00-212). Speak with the instructor or to a customer service representative to get additional supplemental material to prepare for the Advanced Programming certification.
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/Issues
- Factors Affecting Maine’s Economy
- Modeling
- My 2016 Predictions
- My 2020 Predictions
Getting Started:
- How Do You 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 More Than One Scatter Plot in the Same Image
- Histogram
- Appearance Tab
- Series Plot
- Bar Chart
- How Do You 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 (Condition) Without THEN statement
- IF-THEN Statement
- IF-THEN-ELSE Statement
- DROP Statement
- SET Statement
Regression:
- Simple Linear Regression
- Multiple Linear Regression
- Logistic Regression
We will work on another data set to practice by hand everything we learned throughout the course. We will clean the data column by column including character, numeric, date and time variables and how to do typecasting. We will find the correlation between the outcome and the variables.
