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Full Description
Do you want to create data analysis reports without writing a line of code? This book introduces SAS Studio, a free data science web browser-based product for educational and non-commercial purposes. The power of SAS Studio comes from its visual point-and-click user interface that generates SAS code. It is easier to learn SAS Studio than to learn R and Python to accomplish data cleaning, statistics, and visualization tasks.
The book includes a case study about analyzing the data required for predicting the results of presidential elections in the state of Maine for 2016 and 2020. In addition to the presidential elections, the book provides real-life examples including analyzing 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 Studio.
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 Studio graphical user interface. The book includes explanations of the code that SAS Studio generates automatically. You will learn how to edit this code to perform more complicated advanced tasks. The book introduces you to multiple SAS products such as SAS Viya, SAS Analytics, and SAS Visual Statistics.
What You Will Learn
Become familiar with SAS Studio 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
Get introduced to SAS-Viya, which is more potent than SAS studio
Who This Book Is For
A general audience of people who are new to data science, students, and data analysts and scientists who are experiencedbut new to SAS. No programming or in-depth statistics knowledge is needed.
Contents
Part 1: Basics.- Chapter 1: Data Science in Action.- Chapter 2: Getting Started.- Chapter 3: Data Visualization.- Part 2: More Programming.- Chapter 4: Statistical Analysis and Linear Models.- Chapter 5: Advanced Data Preprocessing.- Chapter 6: Preparing Data for Analysis.- Part 3: Advanced Topics.- Chapter 7: Regression.- Chapter 8: SAS Visual Statistics - Viya.- Chapter 9: What Is Next?.- Appendix: Resources.-
