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  • SAS Institute  (12)
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  • 1
    Online Resource
    Online Resource
    SAS Viya : the Python perspective (2017)
    Cary, N.C. : SAS Institute
    Details
    ISBN: 9781629608839 , 1629608831
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: SAS (Computer file) ; Python (Computer program language) ; Cloud computing ; Web services ; Big data ; Data mining ; Electronic books ; Electronic books ; local
    Abstract: Learn how to access analytics from SAS Cloud Analytic Services (CAS) using Python and the SAS Viya platform. SAS Viya : The Python Perspective is an introduction to using the Python client on the SAS Viya platform. SAS Viya is a high-performance, fault-tolerant analytics architecture that can be deployed on both public and private cloud infrastructures. While SAS Viya can be used by various SAS applications, it also enables you to access analytic methods from SAS, Python, Lua, and Java, as well as through a REST interface using HTTP or HTTPS. This book focuses on the perspective of SAS Viya from Python. SAS Viya is made up of multiple components. The central piece of this ecosystem is SAS Cloud Analytic Services (CAS). CAS is the cloud-based server that all clients communicate with to run analytical methods. The Python client is used to drive the CAS component directly using objects and constructs that are familiar to Python programmers. Some knowledge of Python would be helpful before using this book; however, there is an appendix that covers the features of Python that are used in the CAS Python client. Knowledge of CAS is not required to use this book. However, you will need to have a CAS server set up and running to execute the examples in this book. With this book, you will learn how to: Install the required components for accessing CAS from Python Connect to CAS, load data, and run simple analyses Work with CAS using APIs familiar to Python users Grasp general CAS workflows and advanced features of the CAS Python client SAS Viya : The Python Perspective covers topics that will be useful to beginners as well as experienced CAS users. It includes examples from creating connections to CAS all the way to simple statistics and machine learning, but it is also useful as a desktop reference.
    Note: Includes index. - Description based on online resource; title from title page (Safari, viewed March 3, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781629608839/?ar
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    Online Resource
    Online Resource
    Predictive modeling with SAS Enterprise Miner : practical solutions for business applications (2017)
    Cary, NC : SAS Institute
    Details
    ISBN: 9781635260380 , 1635260388
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Third edition.
    Keywords: Enterprise miner ; SAS (Computer file) ; Business ; Data processing ; Data mining ; Regression analysis ; Computer programs ; Statistics ; Data processing ; Electronic books ; Electronic books ; local
    Abstract: A step-by-step guide to predictive modeling! Kattamuri Sarma's Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications, Third Edition, will show you how to develop and test predictive models quickly using SAS Enterprise Miner. Using realistic data, the book explains complex methods in a simple and practical way to readers from different backgrounds and industries. Incorporating the latest version of Enterprise Miner, this third edition also expands the section on time series. Written for business analysts, data scientists, statisticians, students, predictive modelers, and data miners, this comprehensive text provides examples that will strengthen your understanding of the essential concepts and methods of predictive modeling. Topics covered include logistic regression, regression, decision trees, neural networks, variable clustering, observation clustering, data imputation, binning, data exploration, variable selection, variable transformation, and much more, including analysis of textual data. Develop predictive models quickly, learn how to test numerous models and compare the results, gain an in-depth understanding of predictive models and multivariate methods, and discover how to do in-depth analysis. Do it all with Predictive Modeling with SAS Enterprise Miner!
    Note: Previous edition published: 2013. - Includes bibliographical references and index. - Description based on online resource; title from title page (Safari, viewed September 8, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781635260380/?ar
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  • 3
    Online Resource
    Online Resource
    Fundamentals of predictive analytics with JMP (2016)
    Cary, NC : SAS Institute
    Details
    ISBN: 9781629608013 , 1629608017
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Second edition.
    Keywords: JMP (Computer file) ; Statistics ; Graphic methods ; Mathematical statistics ; Data processing ; Data mining ; Electronic books ; Electronic books ; local
    Abstract: Written for students in undergraduate and graduate statistics courses, as well as for the practitioner who wants to make better decisions from data and models, this updated and expanded second edition of Fundamentals of Predictive Analytics with JMP(R) bridges the gap between courses on basic statistics, which focus on univariate and bivariate analysis, and courses on data mining and predictive analytics. Going beyond the theoretical foundation, this book gives you the technical knowledge and problem-solving skills that you need to perform real-world multivariate data analysis. First, this book teaches you to recognize when it is appropriate to use a tool, what variables and data are required, and what the results might be. Second, it teaches you how to interpret the results and then, step-by-step, how and where to perform and evaluate the analysis in JMP . Using JMP 13 and JMP 13 Pro, this book offers the following new and enhanced features in an example-driven format: an add-in for Microsoft Excel Graph Builder dirty data visualization regression ANOVA logistic regression principal component analysis LASSO elastic net cluster analysis decision trees k -nearest neighbors neural networks bootstrap forests boosted trees text mining association rules model comparison With today's emphasis on business intelligence, business analytics, and predictive analytics, this second edition is invaluable to anyone who needs to expand his or her knowledge of statistics and to apply real-world, problem-solving analysis. This book is part of the SAS Press program.
    Note: Includes bibliographical references and index. - Description based on online resource; title from title page (viewed January 16, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781629608013/?ar
    Library Location Call Number Volume/Issue/Year Availability
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  • 4
    Online Resource
    Online Resource
    Using JMP 11 (2014)
    [Erscheinungsort nicht ermittelbar] : SAS Institute
    Details
    ISBN: 9781629591131 , 1629591130
    Language: English
    Pages: 1 online resource (1 v.) , ill.
    Edition: 2nd ed.
    Keywords: JMP (Computer file) ; Mathematical statistics ; Data processing ; Data mining ; Electronic books ; Electronic books ; local
    Abstract: Using JMP 11 shows you how to perform common tasks such as importing data, setting column properties, exporting analyses as graphics or HTML, and modifying JMP preferences. Details about connecting to SAS and working in the Formula Editor are also provided.
    Note: "Statistical discovery. From SAS"--Cover. - Includes index. - Description based on online resource; title from resource description page (Safari, viewed Mar. 18, 2014)
    URL: https://learning.oreilly.com/library/view/-/9781612909356/?ar
    Library Location Call Number Volume/Issue/Year Availability
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  • 5
    Online Resource
    Online Resource
    Segmentation and lifetime value models using SAS (2013)
    Cary, N.C. : SAS Institute
    Details
    Language: English
    Pages: 1 online resource (xii, 168 p.) , ill.
    Parallel Title: Erscheint auch als
    Keywords: SAS (Computer file) ; Business ; Data processing ; Customer relations ; Management ; Data processing ; Relationship marketing ; Data processing ; Data mining ; Electronic books ; Electronic books ; local
    Abstract: Help your organization determine the value of its customer relationships with Segmentation and Lifetime Value Models Using SAS. This book contains a wealth of information that will help you perform analyses to identify your customers and make informed marketing investments. It answers core questions on customer relationship management (CRM), provides an overall framework for thinking about CRM, and offers real-world examples across a variety of industries. Edward C. Malthouse introduces you to a number of useful models, ranging from simple to more complicated examples, and discusses their applications. You'll learn about segmentation models for identifying groups of customers and about lifetime value models for estimating the future value of the segments. You'll learn how to prepare data and estimate models using Base SAS, SAS/STAT, SAS/IML, and SQL. Marketing analysts, CRM analysts, database managers, and anyone looking to address the challenges of allocating marketing resources to different customer groups will benefit from the concepts and exercises in this book. Analysts will learn how to approach unique business problems. Managers will gain a sense of what's possible and what to ask of their analytics departments. This book is part of the SAS Press program.
    Note: Includes bibliographical references and index. - Description based on print version record
    URL: https://learning.oreilly.com/library/view/-/9781612907062/?ar
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  • 6
    Online Resource
    Online Resource
    Fundamentals of predictive analytics with JMP (2013)
    Cary, NC : SAS Institute
    Details
    ISBN: 9781612906232 , 1612906230
    Language: English
    Pages: 1 online resource (1 v.) , ill.
    Parallel Title: Erscheint auch als
    Keywords: JMP (Computer file) ; Statistics ; Graphic methods ; Mathematical statistics ; Data processing ; Data mining ; Electronic books ; Electronic books ; local
    Abstract: With the new emphasis on business intelligence, business analytics and predictive analytics, Fundamentals of Predictive Analytics with JMP is invaluable to everyone who needs to expand their knowledge of statistics and apply real problem-solving analysis.
    Note: Includes bibliographical references and index. - Description based on print version record
    URL: https://learning.oreilly.com/library/view/-/9781612906232/?ar
    Library Location Call Number Volume/Issue/Year Availability
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  • 7
    Online Resource
    Online Resource
    Predictive modeling with SAS Enterprise Miner : practical solutions for business applications (2013)
    Cary, NC : SAS Institute
    Details
    ISBN: 9781607648185 , 1607648180
    Language: English
    Pages: 1 online resource (1 v.) , ill.
    Edition: 2nd ed.
    Keywords: Enterprise miner ; SAS (Computer file) ; Business ; Data processing ; Data mining ; Regression analysis ; Computer programs ; Statistics ; Data processing ; Electronic books ; Electronic books ; local
    Abstract: Learn the theory behind and methods for predictive modeling using SAS Enterprise Miner. Learn how to produce predictive models and prepare presentation-quality graphics in record time with Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications, Second Edition. If you are a graduate student, researcher, or statistician interested in predictive modeling; a data mining expert who wants to learn SAS Enterprise Miner; or a business analyst looking for an introduction to predictive modeling using SAS Enterprise Miner, you'll be able to develop predictive models quickly and effectively using the theory and examples presented in this book. Author Kattamuri Sarma offers the theory behind, programming steps for, and examples of predictive modeling with SAS Enterprise Miner, along with exercises at the end of each chapter. You'll gain a comprehensive awareness of how to find solutions for your business needs. This second edition features expanded coverage of the SAS Enterprise Miner nodes, now including File Import, Time Series, Variable Clustering, Cluster, Interactive Binning, Principal Components, AutoNeural, DMNeural, Dmine Regression, Gradient Boosting, Ensemble, and Text Mining. Develop predictive models quickly, learn how to test numerous models and compare the results, gain an in-depth understanding of predictive models and multivariate methods, and discover how to do in-depth analysis. Do it all with Predictive Modeling with SAS Enterprise Miner. This book is part of the SAS Press program.
    Note: Previous ed.: 2007. - Includes bibliographical references and index. - Description based on online resource; title from title page (Safari, viewed June 24, 2014)
    URL: https://learning.oreilly.com/library/view/-/9781607648185/?ar
    Library Location Call Number Volume/Issue/Year Availability
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  • 8
    Online Resource
    Online Resource
    Using JMP 11 (2013)
    [Erscheinungsort nicht ermittelbar] : SAS Institute
    Details
    ISBN: 9781612906812 , 1612906818
    Language: English
    Pages: 1 online resource (1 v.) , ill.
    Keywords: JMP (Computer file) ; Mathematical statistics ; Data processing ; Data mining ; Electronic books ; Electronic books ; local
    Abstract: Using JMP 11 shows you how to perform common tasks such as importing data, setting column properties, exporting analyses as graphics or HTML, and modifying JMP preferences. Details about connecting to SAS and working in the Formula Editor are also provided.
    Note: "Statistical discovery. From SAS"--Cover. - Includes index. - Description based on online resource; title from resource description page (Safari, viewed Dec. 10, 2013)
    URL: https://learning.oreilly.com/library/view/-/9781612906812/?ar
    Library Location Call Number Volume/Issue/Year Availability
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  • 9
    Online Resource
    Online Resource
    Building business intelligence using SAS : content development examples (2012)
    Cary, N.C. : SAS Institute
    Details
    ISBN: 9781607649946 , 1607649942
    Language: English
    Pages: 1 online resource (1 v.) , ill.
    Parallel Title: Erscheint auch als
    Keywords: SAS (Computer file) ; Database management ; Data mining ; Knowledge management ; Electronic books ; Electronic books ; local
    Abstract: Business intelligence (BI) software provides an interface for multiple audiences to dissect, discover, and decide what the data means. These reporting tools make dynamic information available to all users, giving everyone the ability to manipulate results and further understand the business. There is significant power in reducing the data gatekeeper role in your organization so that each person can quickly interact with data and uncover additional value. SAS offers a BI solution that provides mechanisms to reach every level of the organization. Each tool in this solution provides a different amount of complexity and functionality to aid a broad deployment. Building Business Intelligence Using SAS: Content Development Examples, by Tricia Aanderud and Angela Hall, clarifies how you can fully leverage each SAS BI solution component to ensure a successful implementation. Focusing on the SAS BI Clients, the authors provide a quick-start guide loaded with examples and tips that will help users move quickly from using only one of the SAS BI Clients to using a significant portion of the system. So if you are a SAS BI or SAS Enterprise BI user, but you aren't yet using all the components of the solution, this book is the resource that you need. In addition, the tips and techniques provided in this book will prove invaluable for advanced SAS BI and SAS Enterprise BI users who are studying for SAS Certified BI Content Developer certification. This book is part of the SAS Press program.
    Note: Includes index. - Description based on print version record
    URL: https://learning.oreilly.com/library/view/-/9781607649946/?ar
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  • 10
    Online Resource
    Online Resource
    Applied data mining for forecasting using SAS (2012)
    Cary, N.C. : SAS Institute
    Details
    ISBN: 9781612900933 , 1612900933
    Language: English
    Pages: 1 online resource (1 v.) , ill.
    Parallel Title: Erscheint auch als
    Keywords: SAS (Computer file) ; Data mining ; Forecasting ; Electronic books ; Electronic books ; local
    Abstract: Applied Data Mining for Forecasting Using SAS, by Tim Rey, Arthur Kordon, and Chip Wells, introduces and describes approaches for mining large time series data sets. Written for forecasting practitioners, engineers, statisticians, and economists, the book details how to select useful candidate input variables for time series regression models in environments when the number of candidates is large, and identifies the correlation structure between selected candidate inputs and the forecast variable.
    Note: Includes bibliographical references and index. - Description based on print version record
    URL: https://learning.oreilly.com/library/view/-/9781607646624/?ar
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  • 11
    Online Resource
    Online Resource
    Analyzing receiver operating characteristic curves with SAS (2007)
    Cary, NC : SAS Pub.
    Details
    ISBN: 9781599946344 , 1599946343
    Language: English
    Pages: 1 online resource (1 v.) , ill.
    Series Statement: SAS Press series
    Parallel Title: Erscheint auch als
    Keywords: SAS (Computer file) ; Receiver operating characteristic curves ; Computer programs ; SAS (Computer program language) ; Data mining ; Diagnosis ; Data processing ; Medical statistics ; Computer programs ; Electronic books ; Electronic books ; local
    Abstract: As a diagnostic decision-making tool, receiver operating characteristic (ROC) curves provide a comprehensive and visually attractive way to summarize the accuracy of predictions. They are used extensively in medical diagnosis and increasingly in fields such as data mining, credit scoring, weather forecasting, and psychometry. In Analyzing Receiver Operating Characteristic Curves with SAS, author Mithat Gonen illustrates the many existing SAS procedures that can be tailored to produce ROC curves and expands upon further analyses using other SAS procedures and macros. Both parametric and nonparametric methods for analyzing ROC curves are covered in detail. Topics addressed include: Appropriate methods for binary, ordinal, and continuous measures Computations using PROC FREQ, PROC LOGISTIC, PROC NLMIXED, and macros Comparing the ROC curves of several markers and adjusting them for covariates ROC curves with censored data Using the ROC curve for evaluating multivariable prediction models via bootstrap and cross-validation ROC curves in SAS Enterprise Miner And more! Written for any statistician interested in learning more about ROC curve methodology, the book assumes readers have a basic understanding of regression procedures and moderate familiarity with Base SAS and SAS/STAT. Some familiarity with SAS/GRAPH is helpful but not essential. This book is part of the SAS Press program.
    Note: Includes bibliographical references and index. - Description based on print version record
    URL: https://learning.oreilly.com/library/view/-/9781599942988/?ar
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  • 12
    Online Resource
    Online Resource
    Data preparation for analytics using SAS (2006)
    Cary, N.C. : SAS Institute | Boston, Mass. :Safari Books Online,
    Details
    ISBN: 9781599940472 , 1599940477
    Language: English
    Pages: xxii, 408 p , ill. , 28 cm
    Series Statement: SAS Press series
    Keywords: SAS (Computer file) ; Enterprise Miner ; Business ; Data processing ; Electronic data processing ; Commercial analysis ; Data marts ; Data mining ; Time-series analysis ; Electronic books ; local
    Abstract: Written for anyone involved in the data preparation process for analytics, Gerhard Svolba's Data Preparation for Analytics Using SAS offers practical advice in the form of SAS coding tips and tricks, and provides the reader with a conceptual background on data structures and considerations from a business point of view. The tasks addressed include viewing analytic data preparation in the context of its business environment, identifying the specifics of predictive modeling for data mart creation, understanding the concepts and considerations of data preparation for time series analysis, using various SAS procedures and SAS Enterprise Miner for scoring, creating meaningful derived variables for all data mart types, using powerful SAS macros to make changes among the various data mart structures, and more!
    Note: Includes index
    URL: https://learning.oreilly.com/library/view/-/9781599940472/?ar
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