Python For Sas Users
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Python for SAS Users
Business users familiar with Base SAS programming can now learn Python by example. You will learn via examples that map SAS programming constructs and coding patterns into their Python equivalents. Your primary focus will be on pandas and data management issues related to analysis of data. It is estimated that there are three million or more SAS users worldwide today. As the data science landscape shifts from using SAS to open source software such as Python, many users will feel the need to update their skills. Most users are not formally trained in computer science and have likely acquired their skills programming SAS as part of their job. As a result, the current documentation and plethora of books and websites for learning Python are technical and not geared for most SAS users. Python for SAS Users provides the most comprehensive set of examples currently available. It contains over 200 Python scripts and approximately 75 SAS programs that are analogs to the Python scripts. The first chapters are more Python-centric, while the remaining chapters illustrate SAS and corresponding Python examples to solve common data analysis tasks such as reading multiple input sources, missing value detection, imputation, merging/combining data, and producing output. This book is an indispensable guide for integrating SAS and Python workflows. What You’ll Learn Quickly master Python for data analysis without using a trial-and-error approach Understand the similarities and differences between Base SAS and Python Better determine which language to use, depending on your needs Obtain quick results Who This Book Is For SAS users, SAS programmers, data scientists, data scientist leaders, and Python users who need to work with SAS
SAS for R Users
BRIDGES THE GAP BETWEEN SAS AND R, ALLOWING USERS TRAINED IN ONE LANGUAGE TO EASILY LEARN THE OTHER SAS and R are widely-used, very different software environments. Prized for its statistical and graphical tools, R is an open-source programming language that is popular with statisticians and data miners who develop statistical software and analyze data. SAS (Statistical Analysis System) is the leading corporate software in analytics thanks to its faster data handling and smaller learning curve. SAS for R Users enables entry-level data scientists to take advantage of the best aspects of both tools by providing a cross-functional framework for users who already know R but may need to work with SAS. Those with knowledge of both R and SAS are of far greater value to employers, particularly in corporate settings. Using a clear, step-by-step approach, this book presents an analytics workflow that mirrors that of the everyday data scientist. This up-to-date guide is compatible with the latest R packages as well as SAS University Edition. Useful for anyone seeking employment in data science, this book: Instructs both practitioners and students fluent in one language seeking to learn the other Provides command-by-command translations of R to SAS and SAS to R Offers examples and applications in both R and SAS Presents step-by-step guidance on workflows, color illustrations, sample code, chapter quizzes, and more Includes sections on advanced methods and applications Designed for professionals, researchers, and students, SAS for R Users is a valuable resource for those with some knowledge of coding and basic statistics who wish to enter the realm of data science and business analytics.
SAS Programming for R Users
SAS Programming for R Users, based on the free SAS Education course of the same name, is designed for experienced R users who want to transfer their programming skills to SAS. Emphasis is on programming and not statistical theory or interpretation. You will learn how to write programs in SAS that replicate familiar functions and capabilities in R. This book covers a wide range of topics including the basics of the SAS programming language, how to import data, how to create new variables, random number generation, linear modeling, Interactive Matrix Language (IML), and many other SAS procedures. This book also explains how to write R code directly in the SAS code editor for seamless integration between the two tools. Exercises are provided at the end of each chapter so that you can test your knowledge and practice your programming skills.