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Sarbanes-Oxley Compliance Using COBIT and Open Source Tools

This book illustrates the many Open Source cost savings opportunities available to companies seeking Sarbanes-Oxley compliance. It also provides examples of the Open Source infrastructure components that can and should be made compliant. In addition, the book clearly documents which Open Source tools you should consider using in the journey towards compliance. Although many books and reference material have been authored on the financial and business side of Sox compliance, very little material is available that directly address the information technology considerations, even less so on how Open Source fits into that discussion.Each chapter begins with an analysis of the business and technical ramifications of Sarbanes-Oxley as regards to topics covered before moving into the detailed instructions on the use of the various Open Source applications and tools relating to the compliance objectives.Shows companies how to use Open Source tools to achieve SOX compliance, which dramatically lowers the cost of using proprietary, commercial applicationsOnly SOX compliance book specifically detailing steps to achieve SOX compliance for IT Professionals

Sarbanes-Oxley IT Compliance Using Open Source Tools

The Sarbanes-Oxley Act (officially titled the Public Company Accounting Reform and Investor Protection Act of 2002), signed into law on 30 July 2002 by President Bush, is considered the most significant change to federal securities laws in the United States since the New Deal. It came in the wake of a series of corporate financial scandals, including those affecting Enron, Arthur Andersen, and WorldCom. The law is named after Senator Paul Sarbanes and Representative Michael G. Oxley. It was approved by the House by a vote of 423-3 and by the Senate 99-0. This book illustrates the many Open Source cost-saving opportunities that public companies can explore in their IT enterprise to meet mandatory compliance requirements of the Sarbanes-Oxley act. This book will also demonstrate by example and technical reference both the infrastructure components for Open Source that can be made compliant, and the Open Source tools that can aid in the journey of compliance. Although many books and reference material have been authored on the financial and business side of Sox compliance, very little material is available that directly address the information technology considerations, even less so on how Open Source fits into that discussion. The format of the book will begin each chapter with the IT business and executive considerations of Open Source and SOX compliance. The remaining chapter verbiage will include specific examinations of Open Source applications and tools which relate to the given subject matter.* Only book that shows companies how to use Open Source tools to achieve SOX compliance, which dramatically lowers the cost of using proprietary, commercial applications. * Only SOX compliance book specifically detailing steps to achieve SOX compliance for IT Professionals.

SAS — Eine anwendungs-orientierte Einführung: eine anwendungsorientierte Einführung

SAS — Eine anwendungsorientierte Einführung

SAS® Coding Primer and Reference Guide

Although the web and online SAS® communities can provide volumes of information for programmers, these resources are often overwhelming and lack a simple path to guide coding SAS. This reference, however, does provide such a path from a data user’s standpoint vs. seeing things as a code writer. Written by an experienced SAS programmer, this book lets SAS coders easily find explanations and clarification to typical programming problems. This book presents practical real-world data analysis steps encountered by analysts in the field. These steps include the following: Getting to know raw data Understanding variables Getting data into SAS Creating new data variables Performing data manipulations, including sorting, ranking, grouping, subtotal, total, and percentage Statistical testing under a broad range of logical and conditional settings Data visualization Throughout this book, statements and codes are accompanied by thorough annotation. Line-by-line explanations ensure that all terms are clearly explained. Code examples and sample codes have broad usages. All the examples are related to highway transportation where the use of big data is exploding and presenting new challenges and opportunities for growth. Clear and precise practical introductory material on statistics is integrated into the relevant SAS procedures to bolster users’ confidence in applying such methods to their own work. Comprehensive and foundational coverage, systematic introduction of programming topics, thoroughly annotated code examples, and real-world code samples combine to make SAS® Coding Primer and Reference Guide an indispensable reference for beginners and experienced programmers.

SAS® Coding Primer and Reference Guide

Although the web and online SAS® communities can provide volumes of information for programmers, these resources are often overwhelming and lack a simple path to guide coding SAS. This reference, however, does provide such a path from a data user’s standpoint vs. seeing things as a code writer. Written by an experienced SAS programmer, this book lets SAS coders easily find explanations and clarification to typical programming problems. This book presents practical real-world data analysis steps encountered by analysts in the field. These steps include the following: Getting to know raw data Understanding variables Getting data into SAS Creating new data variables Performing data manipulations, including sorting, ranking, grouping, subtotal, total, and percentage Statistical testing under a broad range of logical and conditional settings Data visualization Throughout this book, statements and codes are accompanied by thorough annotation. Line-by-line explanations ensure that all terms are clearly explained. Code examples and sample codes have broad usages. All the examples are related to highway transportation where the use of big data is exploding and presenting new challenges and opportunities for growth. Clear and precise practical introductory material on statistics is integrated into the relevant SAS procedures to bolster users’ confidence in applying such methods to their own work. Comprehensive and foundational coverage, systematic introduction of programming topics, thoroughly annotated code examples, and real-world code samples combine to make SAS® Coding Primer and Reference Guide an indispensable reference for beginners and experienced programmers.

SAS Data Analytic Development: Dimensions of Software Quality (Wiley and SAS Business Series)

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.

SAS Data Analytic Development: Dimensions of Software Quality (Wiley and SAS Business Series)

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.

SAS Essentials: Mastering SAS for Data Analytics

SAS ESSENTIALS Valuable 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 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 25 years of teaching introductory SAS courses. This book introduces data manipulation, statistical techniques, and the SAS programming language, including SAS macros, reporting results in tables, and factor analysis, as well as sections on character functions, variable manipulation, and merging, appending, and updating files. It features self-contained chapters to enhance the learning process and includes programming approaches for the latest version of the SAS platform. The Third Edition has been updated with expanded examples, a new chapter introducing PROC SQL as well as new end-of-chapter exercises. The Third Edition also includes a companion website with data sets, additional code, notes on SAS programming, functions, ODS, PROC SQL, and SAS Arrays, along with solutions for instructors. Specific sample topics covered in SAS® Essentials include: Getting data into SAS, reading, writing, and importing data, preparing data for analysis, preparing to use SAS procedures, and controlling output using ODS Techniques for creating, editing, and debugging SAS programs, cleaning up messy data sets, and manipulating data using character, time, and numeric functions Other essential computational skills that are utilized by business, government, and organizations alike, and DATA step for data management Using PROC to analyze data, including evaluating quantitative data, analyzing counts and tables, comparing means using T-Tests, correlation and regression, and analysis of variance, nonparametric analysis, logistic regression, factor analysis, and creating custom graphs and reports. SAS® Essentials is a fundamental study resource for professionals preparing for the SAS Base Certification Exam, as well as an ideal textbook for courses in statistics, data analytics, applied SAS programming, and statistical computer applications.

SAS Essentials: Mastering SAS for Data Analytics

SAS ESSENTIALS Valuable 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 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 25 years of teaching introductory SAS courses. This book introduces data manipulation, statistical techniques, and the SAS programming language, including SAS macros, reporting results in tables, and factor analysis, as well as sections on character functions, variable manipulation, and merging, appending, and updating files. It features self-contained chapters to enhance the learning process and includes programming approaches for the latest version of the SAS platform. The Third Edition has been updated with expanded examples, a new chapter introducing PROC SQL as well as new end-of-chapter exercises. The Third Edition also includes a companion website with data sets, additional code, notes on SAS programming, functions, ODS, PROC SQL, and SAS Arrays, along with solutions for instructors. Specific sample topics covered in SAS® Essentials include: Getting data into SAS, reading, writing, and importing data, preparing data for analysis, preparing to use SAS procedures, and controlling output using ODS Techniques for creating, editing, and debugging SAS programs, cleaning up messy data sets, and manipulating data using character, time, and numeric functions Other essential computational skills that are utilized by business, government, and organizations alike, and DATA step for data management Using PROC to analyze data, including evaluating quantitative data, analyzing counts and tables, comparing means using T-Tests, correlation and regression, and analysis of variance, nonparametric analysis, logistic regression, factor analysis, and creating custom graphs and reports. SAS® Essentials is a fundamental study resource for professionals preparing for the SAS Base Certification Exam, as well as an ideal textbook for courses in statistics, data analytics, applied SAS programming, and statistical computer applications.

SAS for Data Analysis: Intermediate Statistical Methods (Statistics and Computing)

This book is intended for use as the textbook in a second course in applied statistics that covers topics in multiple regression and analysis of variance at an intermediate level. Generally, students enrolled in such courses are p- marily graduate majors or advanced undergraduate students from a variety of disciplines. These students typically have taken an introductory-level s- tistical methods course that requires the use a software system such as SAS for performing statistical analysis. Thus students are expected to have an - derstanding of basic concepts of statistical inference such as estimation and hypothesis testing. Understandably, adequate time is not available in a ?rst course in stat- tical methods to cover the use of a software system adequately in the amount of time available for instruction. The aim of this book is to teach how to use the SAS system for data analysis. The SAS language is introduced at a level of sophistication not found in most introductory SAS books. Important features such as SAS data step programming, pointers, and line-hold spe- ?ers are described in detail. The powerful graphics support available in SAS is emphasized throughout, and many worked SAS program examples contain graphic components.

SAS For Dummies (For Dummies Computers Ser.)

Created in partnership with SAS, this book explores SAS, a business intelligence software that can be used in any business setting or enterprise for data delivery, reporting, data mining, forecasting, statistical analysis, and more SAS employee and technologist Stephen McDaniel combines real-world expertise and a friendly writing style to introduce readers to SAS basics Covers crucial topics such as getting various types of data into the software, producing reports, working with the data, basic SAS programming, macros, and working with SAS and databases

SAS For Dummies

The fun and easy way to learn to use this leading business intelligence tool Written by an author team who is directly involved with SAS, this easy-to-follow guide is fully updated for the latest release of SAS and covers just what you need to put this popular software to work in your business. SAS allows any business or enterprise to improve data delivery, analysis, reporting, movement across a company, data mining, forecasting, statistical analysis, and more. SAS For Dummies, 2nd Edition gives you the necessary background on what SAS can do for you and explains how to use the Enterprise Guide. SAS provides statistical and data analysis tools to help you deal with all kinds of data: operational, financial, performance, and more Places special emphasis on Enterprise Guide and other analytical tools, covering all commonly used features Covers all commonly used features and shows you the practical applications you can put to work in your business Explores how to get various types of data into the software and how to work with databases Covers producing reports and Web reporting tools, analytics, macros, and working with your data In the easy-to-follow, no-nonsense For Dummies format, SAS For Dummies gives you the knowledge and the confidence to get SAS working for your organization. Note: CD-ROM/DVD and other supplementary materials are not included as part of eBook file.

SAS For Dummies

The fun and easy way to learn to use this leading business intelligence tool Written by an author team who is directly involved with SAS, this easy-to-follow guide is fully updated for the latest release of SAS and covers just what you need to put this popular software to work in your business. SAS allows any business or enterprise to improve data delivery, analysis, reporting, movement across a company, data mining, forecasting, statistical analysis, and more. SAS For Dummies, 2nd Edition gives you the necessary background on what SAS can do for you and explains how to use the Enterprise Guide. SAS provides statistical and data analysis tools to help you deal with all kinds of data: operational, financial, performance, and more Places special emphasis on Enterprise Guide and other analytical tools, covering all commonly used features Covers all commonly used features and shows you the practical applications you can put to work in your business Explores how to get various types of data into the software and how to work with databases Covers producing reports and Web reporting tools, analytics, macros, and working with your data In the easy-to-follow, no-nonsense For Dummies format, SAS For Dummies gives you the knowledge and the confidence to get SAS working for your organization. Note: CD-ROM/DVD and other supplementary materials are not included as part of eBook file.

SAS for Finance: Forecasting and data analysis techniques with real-world examples to build powerful financial models

Leverage the analytical power of SAS to perform financial analysis efficientlyAbout This BookLeverage the power of SAS to analyze financial data with easeFind hidden patterns in your data, predict future trends, and optimize risk managementLearn why leading banks and financial institutions rely on SAS for financial analysisWho This Book Is ForFinancial data analysts and data scientists who want to use SAS to process and analyze financial data and find hidden patterns and trends from it will find this book useful. Prior exposure to SAS will be helpful but is not mandatory. Some basic understanding of the financial concepts is required.What You Will LearnUnderstand time series data and its relevance in the financial industryBuild a time series forecasting model in SAS using advanced modeling theoriesDevelop models in SAS and infer using regression and Markov chainsForecast in?ation by building an econometric model in SAS for your financial planningManage customer loyalty by creating a survival model in SAS using various groupingsUnderstand similarity analysis and clustering in SAS using time series dataIn DetailSAS is a groundbreaking tool for advanced predictive and statistical analytics used by top banks and financial corporations to establish insights from their financial data.SAS for Finance offers you the opportunity to leverage the power of SAS analytics in redefining your data. Packed with real-world examples from leading financial institutions, the author discusses statistical models using time series data to resolve business issues.This book shows you how to exploit the capabilities of this high-powered package to create clean, accurate financial models. You can easily assess the pros and cons of models to suit your unique business needs.By the end of this book, you will be able to leverage the true power of SAS to design and develop accurate analytical models to gain deeper insights into your financial data.Style and approachA comprehensive guide filled with use-cases will ensure that you have a very good conceptual and practical understanding of using SAS in the finance domain.

SAS for Finance: Forecasting and data analysis techniques with real-world examples to build powerful financial models

Leverage the analytical power of SAS to perform financial analysis efficientlyAbout This BookLeverage the power of SAS to analyze financial data with easeFind hidden patterns in your data, predict future trends, and optimize risk managementLearn why leading banks and financial institutions rely on SAS for financial analysisWho This Book Is ForFinancial data analysts and data scientists who want to use SAS to process and analyze financial data and find hidden patterns and trends from it will find this book useful. Prior exposure to SAS will be helpful but is not mandatory. Some basic understanding of the financial concepts is required.What You Will LearnUnderstand time series data and its relevance in the financial industryBuild a time series forecasting model in SAS using advanced modeling theoriesDevelop models in SAS and infer using regression and Markov chainsForecast in?ation by building an econometric model in SAS for your financial planningManage customer loyalty by creating a survival model in SAS using various groupingsUnderstand similarity analysis and clustering in SAS using time series dataIn DetailSAS is a groundbreaking tool for advanced predictive and statistical analytics used by top banks and financial corporations to establish insights from their financial data.SAS for Finance offers you the opportunity to leverage the power of SAS analytics in redefining your data. Packed with real-world examples from leading financial institutions, the author discusses statistical models using time series data to resolve business issues.This book shows you how to exploit the capabilities of this high-powered package to create clean, accurate financial models. You can easily assess the pros and cons of models to suit your unique business needs.By the end of this book, you will be able to leverage the true power of SAS to design and develop accurate analytical models to gain deeper insights into your financial data.Style and approachA comprehensive guide filled with use-cases will ensure that you have a very good conceptual and practical understanding of using SAS in the finance domain.

SAS for Finance: Forecasting And Data Analysis Techniques With Real-world Examples To Build Powerful Financial Models

SAS is the ground-breaking tool for advanced, predictive, and statistical analytics. Right from refining your data using power of SAS analytics, you will be able to exploit the capabilities of high-powered package to create accurate financial models. You can easily assess the pros and cons of models to suit unique business needs.

SAS for R Users: A Book for Data Scientists

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. AJAY OHRI is the founder of analytics startup Decisionstats.com. His research interests include spreading open source analytics, analyzing social media manipulation with mechanism design, simpler interfaces to cloud computing, investigating climate change, and knowledge flows. He currently advises startups in analytics off shoring, analytics services, and analytics. He is the author of Python for R Users: A Data Science Approach (Wiley), R for Business Analytics, and R for Cloud Computing.

SAS for R Users: A Book for Data Scientists

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. AJAY OHRI is the founder of analytics startup Decisionstats.com. His research interests include spreading open source analytics, analyzing social media manipulation with mechanism design, simpler interfaces to cloud computing, investigating climate change, and knowledge flows. He currently advises startups in analytics off shoring, analytics services, and analytics. He is the author of Python for R Users: A Data Science Approach (Wiley), R for Business Analytics, and R for Cloud Computing.

SAS für Anfänger: Einführung in das Programmsystem

A SAS/IML Companion for Linear Models (Statistics and Computing)

Linear models courses are often presented as either theoretical or applied. Consequently, students may find themselves either proving theorems or using high-level procedures like PROC GLM to analyze data. There exists a gap between the derivation of formulas and analyses that hide these formulas behind attractive user interfaces. This book bridges that gap, demonstrating theory put into practice. Concepts presented in a theoretical linear models course are often trivialized in applied linear models courses by the facility of high-level SAS procedures like PROC MIXED and PROC REG that require the user to provide a few options and statements and in return produce vast amounts of output. This book uses PROC IML to show how analytic linear models formulas can be typed directly into PROC IML, as they were presented in the linear models course, and solved using data. This helps students see the link between theory and application. This also assists researchers in developing new methodologies in the area of linear models. The book contains complete examples of SAS code for many of the computations relevant to a linear models course. However, the SAS code in these examples automates the analytic formulas. The code for high-level procedures like PROC MIXED is also included for side-by-side comparison. The book computes basic descriptive statistics, matrix algebra, matrix decomposition, likelihood maximization, non-linear optimization, etc. in a format conducive to a linear models or a special topics course. Also included in the book is an example of a basic analysis of a linear mixed model using restricted maximum likelihood estimation (REML). The example demonstrates tests for fixed effects, estimates of linear functions, and contrasts. The example starts by showing the steps for analyzing the data using PROC IML and then provides the analysis using PROC MIXED. This allows students to follow the process that lead to the output.

SAS Programming and Data Visualization Techniques: A Power User's Guide

SAS Programming and Data Visualization Techniques: A Power User’s Guide brings together a wealth of ideas about strategic and tactical solutions to everyday situations experienced when transferring, extracting, processing, analyzing, and reporting the valuable data you have at your fingertips. Best, you can achieve most of the solutions using the SAS components you already license, meaning that this book’s insights can keep you from throwing money at problems needlessly.Author Philip R. Holland advises a broad range of clients throughout Europe and the United States as an independent consultant and founder of Holland Numerics Ltd, a SAS technical consultancy. In this book he explains techniques—through code samples and example—that will enable you to increase your knowledge of all aspects of SAS programming, improve your coding productivity, and interface SAS with other programs. He also provides an expert’s overview of Graph Templates, which was recently moved into Base SAS. You will learn to create attractive, standardized, reusable, and platform-independent graphs—both statistical and non-statistical—to help you and your business users explore, visualize, and capitalize on your company’s data. In addition, you will find many examples and cases pertaining to healthcare, finance, retail, and other industries.Among other things, SAS Programming and Data Visualization Techniques will show you how to:Write efficient and reusable SAS codeCombine look-up data sets with larger data sets effectivelyRun R and Perl from SASRun SAS programs from SAS Studio and Enterprise GuideOutput data into insightful, valuable charts and graphsSAS Programming and Data Visualization Techniques prepares you to make better use of your existing SAS components by learning to use the newest features, improve your coding efficiency, help you develop applications that are easier to maintain, and make data analysis easier. In other words, it will save you time, money, and effort—and make you a more valuable member of the development team.What You'll LearnHow to write more efficient SAS code—either code that runs quicker, code that is easier to maintain, or bothHow to do more with the SAS components you already licenseHow to take advantage of the newest features in SASHow to interface external applications with SAS softwareHow to create graphs using SAS ODS GraphicsWho This Book Is For SAS programmers wanting to improve their existing programming skills, and programming managers wanting to make better use of the SAS software they already license.

SAS Stored Processes: A Practical Guide to Developing Web Applications

Customize the SAS Stored Process web application to create amazing tools for end users. This book shows you how to use stored processes—SAS programs stored on a server and executed as required by requesting applications.Never before have there been so many ways to turn data into information and build applications with SAS. This book teaches you how to use the web technologies that you frequently see used on impressive websites. By using SAS Stored Processes, you will be able to build applications that exploit CSS, JavaScript, and HTML libraries and enable you to build powerful and impressive web applications using SAS as the backend.While this approach is not common with SAS users, some have had amazing results. People who have SAS skills usually do not have web development skills, and those with web development skills usually do not have SAS skills. Some people have both skills but are unaware of how to connect them with the SAS Stored Process web application. This book shows you how to leverage your skills for success.What You Will LearnKnow the benefits of stored processesWrite your own tools in SASMake a stored process generate its own HTML menuPass data between stored processesUse stored processes to generate pure JavaScriptUtilize data generated by SASConvert a SAS program into a stored processWho This Book Is ForSAS programmers looking to improve their existing programming skills to develop web applications, and programming managers who want to make better use of the SAS software they already license

Sass and Compass for Designers

A step-by-step tutorial guide, taking you through how to build a responsive Sass and Compass powered website. If you understand HTML and CSS, this book is all you need to take your code to the next level with Sass and Compass. No prior understanding of CSS preprocessors or programming conventions is needed.

Sass Essentials

This book is primarily aimed at web designers who have a good understanding of CSS, jQuery, and HTML, but who are new to using CSS preprocessing. Some prior knowledge is assumed of WordPress, CSS grids, and Bootstrap, although these skills can be picked up reasonably quickly.