Learn Data Science Using SAS Studio: From Clicks to Code - Tapa blanda

Acerca del autor

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.

De la contraportada

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.

What You Will Learn 

Become familiar with the SAS Studio IDE.
• How to create essential visualizations.
• Know the fundamental statistical analysis required in most data science and analytics reports.
• Clean the most common dataset problems
• Learn linear and logistic regression for data prediction and analysis.
• Write programs in SAS.
• How to analyze data and get insights from it for decision-making.
Learn character, numeric, date, time, and datetime functions and typecasting.