Do you want to create data analysis reports without writing a line of code? This book introduces SAS Studio, a free, web-based data science product for educational and non-commercial purposes. The power of SAS Studio lies in its visual, point-and-click user interface, which 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 analyzing the data required to predict 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 analyses of stock, oil, and gold prices, crime, marketing, and healthcare. You will see data science in action and how easily it can be performed using complicated tasks and visualizations in SAS Studio.

You will learn, step by step, how to perform visualizations, including creating 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.

What You Will Learn

• Become familiar with the SAS Studio IDE.

  Autorentext

Engy Fouda is an Adjunct Lecturer at SUNY New Paltz University, teaching Intro to Data Science Using SAS Studio and Introduction to Machine Learning using Python. She is an author for Apress and Packt Publishing, having published nine books, and this book is her tenth. Many of them are best sellers. Currently, she teaches SAS Fundamentals, SAS Programming Intermediate (Test-Prep for the SAS Associate and SAS Specialist Certificates), SAS SQL, Docker Fundamentals, Docker for Enterprise Developers, Kubernetes Introduction, Kubernetes Intermediate, and Docker Certified Associate (DCA) Test Prep at ONLC (Microsoft Partner), Plurasight Inc., and other venues as a freelance instructor. She holds two master's degrees. The most recent one is in journalism from Harvard University, Extension School, and a computer engineering degree from Cairo University. Moreover, she earned a Data Science Graduate Professional Certificate from Harvard University.

Inhalt

Part I: Basics.- Chapter 1: Data Science in Action.- Chapter 2: Getting Started.- Chapter 3: Data Visualization.- Part II: More Programming.- Chapter 4: Statistical Analysis and Linear Models.- Chapter 5: Advanced Data Preprocessing.- Chapter 6: Preparing Data for Analysis.- Part III: Advanced Topics.- Chapter 7: Date, Time, Datetime - Chapter 8: Strings Chapter 9: Regression..- Chapter 10: What Is Next?.- Appendix: Resources.