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Author: David Pope Publisher: ISBN: 9781788290906 Category : Computers Languages : en Pages : 266
Book Description
Leverage the capabilities of SAS to process and analyze Big DataAbout This Book* Combine SAS with platforms such as Hadoop, SAP HANA, and Cloud Foundry-based platforms for effecient Big Data analytics* Learn how to use the web browser-based SAS Studio and iPython Jupyter Notebook interfaces with SAS* Practical, real-world examples on predictive modeling, forecasting, optimizing and reporting your Big Data analysis with SASWho This Book Is ForSAS professionals and data analysts who wish to perform analytics on Big Data using SAS to gain actionable insights will find this book to be very useful. If you are a data science professional looking to perform large-scale analytics with SAS, this book will also help you. A basic understanding of SAS will be helpful, but is not mandatory.What You Will Learn* Configure a free version of SAS in order do hands-on exercises dealing with data management, analysis, and reporting.* Understand the basic concepts of the SAS language which consists of the data step (for data preparation) and procedures (or PROCs) for analysis.* Make use of the web browser based SAS Studio and iPython Jupyter Notebook interfaces for coding in the SAS, DS2, and FedSQL programming languages.* Understand how the DS2 programming language plays an important role in Big Data preparation and analysis using SAS* Integrate and work efficiently with Big Data platforms like Hadoop, SAP HANA, and cloud foundry based systems.In DetailSAS has been recognized by Money Magazine and Payscale as one of the top business skills to learn in order to advance one's career. Through innovative data management, analytics, and business intelligence software and services, SAS helps customers solve their business problems by allowing them to make better decisions faster. This book introduces the reader to the SAS and how they can use SAS to perform efficient analysis on any size data, including Big Data.The reader will learn how to prepare data for analysis, perform predictive, forecasting, and optimization analysis and then deploy or report on the results of these analyses. While performing the coding examples within this book the reader will learn how to use the web browser based SAS Studio and iPython Jupyter Notebook interfaces for working with SAS. Finally, the reader will learn how SAS's architecture is engineered and designed to scale up and/or out and be combined with the open source offerings such as Hadoop, Python, and R.By the end of this book, you will be able to clearly understand how you can efficiently analyze Big Data using SAS.Style and approachThe book starts off by introducing the reader to SAS and the SAS programming language which provides data management, analytical, and reporting capabilities. Most chapters include hands on examples which highlights how SAS provides The Power to Know�. The reader will learn that if they are looking to perform large-scale data analysis that SAS provides an open platform engineered and designed to scale both up and out which allows the power of SAS to combine with open source offerings such as Hadoop, Python, and R.
Author: Troy Martin Hughes Publisher: John Wiley & Sons ISBN: 1119255910 Category : Computers Languages : en Pages : 446
Book Description
Design quality SAS software and evaluate SAS software quality SAS Data Analytic Development is the developer’s compendium for writing better-performing software and the manager’s guide to building comprehensive software performance requirements. The text introduces and parallels the International Organization for Standardization (ISO) software product quality model, demonstrating 15 performance requirements that represent dimensions of software quality, including: reliability, recoverability, robustness, execution efficiency (i.e., speed), efficiency, scalability, portability, security, automation, maintainability, modularity, readability, testability, stability, and reusability. The text is intended to be read cover-to-cover or used as a reference tool to instruct, inspire, deliver, and evaluate software quality. A common fault in many software development environments is a focus on functional requirements—the what and how—to the detriment of performance requirements, which specify instead how well software should function (assessed through software execution) or how easily software should be maintained (assessed through code inspection). Without the definition and communication of performance requirements, developers risk either building software that lacks intended quality or wasting time delivering software that exceeds performance objectives—thus, either underperforming or gold-plating, both of which are undesirable. Managers, customers, and other decision makers should also understand the dimensions of software quality both to define performance requirements at project outset as well as to evaluate whether those objectives were met at software completion. As data analytic software, SAS transforms data into information and ultimately knowledge and data-driven decisions. Not surprisingly, data quality is a central focus and theme of SAS literature; however, code quality is far less commonly described and too often references only the speed or efficiency with which software should execute, omitting other critical dimensions of software quality. SAS® software project definitions and technical requirements often fall victim to this paradox, in which rigorous quality requirements exist for data and data products yet not for the software that undergirds them. By demonstrating the cost and benefits of software quality inclusion and the risk of software quality exclusion, stakeholders learn to value, prioritize, implement, and evaluate dimensions of software quality within risk management and project management frameworks of the software development life cycle (SDLC). Thus, SAS Data Analytic Development recalibrates business value, placing code quality on par with data quality, and performance requirements on par with functional requirements.
Author: Troy Martin Hughes Publisher: John Wiley & Sons ISBN: 111924076X Category : Computers Languages : en Pages : 624
Book Description
Design quality SAS software and evaluate SAS software quality SAS Data Analytic Development is the developer’s compendium for writing better-performing software and the manager’s guide to building comprehensive software performance requirements. The text introduces and parallels the International Organization for Standardization (ISO) software product quality model, demonstrating 15 performance requirements that represent dimensions of software quality, including: reliability, recoverability, robustness, execution efficiency (i.e., speed), efficiency, scalability, portability, security, automation, maintainability, modularity, readability, testability, stability, and reusability. The text is intended to be read cover-to-cover or used as a reference tool to instruct, inspire, deliver, and evaluate software quality. A common fault in many software development environments is a focus on functional requirements—the what and how—to the detriment of performance requirements, which specify instead how well software should function (assessed through software execution) or how easily software should be maintained (assessed through code inspection). Without the definition and communication of performance requirements, developers risk either building software that lacks intended quality or wasting time delivering software that exceeds performance objectives—thus, either underperforming or gold-plating, both of which are undesirable. Managers, customers, and other decision makers should also understand the dimensions of software quality both to define performance requirements at project outset as well as to evaluate whether those objectives were met at software completion. As data analytic software, SAS transforms data into information and ultimately knowledge and data-driven decisions. Not surprisingly, data quality is a central focus and theme of SAS literature; however, code quality is far less commonly described and too often references only the speed or efficiency with which software should execute, omitting other critical dimensions of software quality. SAS® software project definitions and technical requirements often fall victim to this paradox, in which rigorous quality requirements exist for data and data products yet not for the software that undergirds them. By demonstrating the cost and benefits of software quality inclusion and the risk of software quality exclusion, stakeholders learn to value, prioritize, implement, and evaluate dimensions of software quality within risk management and project management frameworks of the software development life cycle (SDLC). Thus, SAS Data Analytic Development recalibrates business value, placing code quality on par with data quality, and performance requirements on par with functional requirements.
Author: Gregory S. Nelson Publisher: John Wiley & Sons ISBN: 1119425093 Category : Business & Economics Languages : en Pages : 468
Book Description
An evidence-based organizational framework for exceptional analytics team results The Analytics Lifecycle Toolkit provides managers with a practical manual for integrating data management and analytic technologies into their organization. Author Gregory Nelson has encountered hundreds of unique perspectives on analytics optimization from across industries; over the years, successful strategies have proven to share certain practices, skillsets, expertise, and structural traits. In this book, he details the concepts, people and processes that contribute to exemplary results, and shares an organizational framework for analytics team functions and roles. By merging analytic culture with data and technology strategies, this framework creates understanding for analytics leaders and a toolbox for practitioners. Focused on team effectiveness and the design thinking surrounding product creation, the framework is illustrated by real-world case studies to show how effective analytics team leadership works on the ground. Tools and templates include best practices for process improvement, workforce enablement, and leadership support, while guidance includes both conceptual discussion of the analytics life cycle and detailed process descriptions. Readers will be equipped to: Master fundamental concepts and practices of the analytics life cycle Understand the knowledge domains and best practices for each stage Delve into the details of analytical team processes and process optimization Utilize a robust toolkit designed to support analytic team effectiveness The analytics life cycle includes a diverse set of considerations involving the people, processes, culture, data, and technology, and managers needing stellar analytics performance must understand their unique role in the process of winnowing the big picture down to meaningful action. The Analytics Lifecycle Toolkit provides expert perspective and much-needed insight to managers, while providing practitioners with a new set of tools for optimizing results.
Author: Ajay Ohri Publisher: John Wiley & Sons ISBN: 1119256410 Category : Computers Languages : en Pages : 210
Book Description
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.
Author: Tricia Aanderud Publisher: SAS Institute ISBN: 1635260442 Category : Computers Languages : en Pages : 294
Book Description
Focusing on the version of SAS Visual Analytics on SAS 9.4, this thorough guide will show you how to make sense of your complex data with the goal of leading you to smarter, data-driven decisions without having to write a single line of code ¿̐ư unless you want to. --
Author: Olivia Parr-Rud Publisher: SAS Institute ISBN: 1629593273 Category : Business & Economics Languages : en Pages : 182
Book Description
This tutorial for data analysts new to SAS Enterprise Guide and SAS Enterprise Miner provides valuable experience using powerful statistical software to complete the kinds of business analytics common to most industries. This beginnner's guide with clear, illustrated, step-by-step instructions will lead you through examples based on business case studies. You will formulate the business objective, manage the data, and perform analyses that you can use to optimize marketing, risk, and customer relationship management, as well as business processes and human resources. Topics include descriptive analysis, predictive modeling and analytics, customer segmentation, market analysis, share-of-wallet analysis, penetration analysis, and business intelligence. --
Author: Publisher: ISBN: 9781642954821 Category : Languages : en Pages : 108
Book Description
SAS provides many different solutions to investigate and analyze text and operationalize decisioning. Several impressive papers have been written to demonstrate how to use these techniques. We have carefully selected a handful of these from recent Global Forum contributions to introduce you to the topic and let you sample what each has to offer. Also available free as a PDF from sas.com/books.
Author: Alan C. Elliott Publisher: John Wiley & Sons ISBN: 111904216X Category : Education Languages : en Pages : 528
Book Description
A step-by-step introduction to using SAS® statistical software as a foundational approach to data analysis and interpretation Presenting a straightforward introduction from the ground up, SAS® Essentials: Mastering SAS for Data Analytics, Second Edition illustrates SAS using hands-on learning techniques and numerous real-world examples. Keeping different experience levels in mind, the highly-qualified author team has developed the book over 20 years of teaching introductory SAS courses. Divided into two sections, the first part of the book provides an introduction to data manipulation, statistical techniques, and the SAS programming language. The second section is designed to introduce users to statistical analysis using SAS Procedures. Featuring self-contained chapters to enhance the learning process, the Second Edition also includes: Programming approaches for the most up-to-date version of the SAS platform including information on how to use the SAS University Edition Discussions to illustrate the concepts and highlight key fundamental computational skills that are utilized by business, government, and organizations alike New chapters on reporting results in tables and factor analysis Additional information on the DATA step for data management with an emphasis on importing data from other sources, combining data sets, and data cleaning Updated ANOVA and regression examples as well as other data analysis techniques A companion website with the discussed data sets, additional code, and related PowerPoint® slides SAS Essentials: Mastering SAS for Data Analytics, Second Edition is an ideal textbook for upper-undergraduate and graduate-level courses in statistics, data analytics, applied SAS programming, and statistical computer applications as well as an excellent supplement for statistical methodology courses. The book is an appropriate reference for researchers and academicians who require a basic introduction to SAS for statistical analysis and for preparation for the Basic SAS Certification Exam.
Author: SAS Publisher: SAS Institute ISBN: 1952365112 Category : Computers Languages : en Pages : 362
Book Description
Discover your data and create reports in an elegant and intuitive web-based interface! SAS Visual Analytics for SAS Viya is a practical guide designed to get you started investigating your data and creating reports with SAS Visual Analytics, a web-based tool that enables you to explore huge volumes of data to identify patterns, trends, and opportunities. Learn how to report, share, and collaborate on insights from data with no SAS programming skills necessary – this book is accessible to all, including decision makers, business analysts, report creators, and citizen data scientists. SAS Visual Analytics for SAS Viya first introduces the basics needed to prepare and explore your data, make discoveries, and create a report in SAS Visual Analytics. Then, the second section describes more advanced topics, such as using automated explanation and creating advanced interactive reports with parameters. The book covers: Adding and manipulating data items within SAS Visual Analytics Analyzing data with SAS Visual Analytics Designing and sharing reports using SAS Visual Analytics Demonstrations and practices are included for you to follow to gain hands-on experience with SAS Visual Analytics, and the data sets used in the book are also provided to download.
Author: David Pope
Author: Troy Martin Hughes
Author: Troy Martin Hughes
Author: David Pope
Author: Gregory S. Nelson
Author: Ajay Ohri
Author: Tricia Aanderud
Author: Olivia Parr-Rud
Author: 
Author: Alan C. Elliott
Author: SAS