Decision Trees for Analytics Using SAS Enterprise Miner

Author: Barry de Ville
Publisher: SAS Institute
ISBN: 9781629591001
Release Date: 2013-07-10
Genre: Mathematics

Decision Trees for Analytics Using SAS Enterprise Miner is the most comprehensive treatment of decision tree theory, use, and applications available in one easy-to-access place. This book illustrates the application and operation of decision trees in business intelligence, data mining, business analytics, prediction, and knowledge discovery. It explains in detail the use of decision trees as a data mining technique and how this technique complements and supplements data mining approaches such as regression, as well as other business intelligence applications that incorporate tabular reports, OLAP, or multidimensional cubes. An expanded and enhanced release of Decision Trees for Business Intelligence and Data Mining Using SAS Enterprise Miner, this book adds up-to-date treatments of boosting and high-performance forest approaches and rule induction. There is a dedicated section on the most recent findings related to bias reduction in variable selection. It provides an exhaustive treatment of the end-to-end process of decision tree construction and the respective considerations and algorithms, and it includes discussions of key issues in decision tree practice. Analysts who have an introductory understanding of data mining and who are looking for a more advanced, in-depth look at the theory and methods of a decision tree approach to business intelligence and data mining will benefit from this book. This book is part of the SAS Press program.

Decision Trees for Business Intelligence and Data Mining

Author: Barry de Ville
Publisher: SAS Institute
ISBN: 9781599943107
Release Date: 2006-10
Genre: Computers

Using SAS Enterprise Miner, Barry de Ville's Decision Trees for Business Intelligence and Data Mining illustrates the application and operation of decision trees in business intelligence, data mining, business analytics, prediction, and knowledge discovery. It explains in detail the use of decision trees as a data mining technique and how this technique complements and supplements data mining approaches such as regression, as well as other business intelligence applications that incorporate tabular reports, OLAP, or multidimensional cubes. Examples show how various aspects of decision trees are constructed, how they operate, how to interpret them, and how to use them in a range of predictive and descriptive applications. The examples are drawn from the areas of purchase behavior, risk assessment, and business-to-business marketing. This book also describes the various disciplines that contributed to the development of decision trees and how, even today, decision trees can be used as a form of machine intelligence. Examples of using and interpreting graphic decision trees as executable rules are provided. The target audience includes analysts who have an introductory understanding of data mining and who want to benefit from a more advanced, in-depth look at the theory and methods of a decision tree approach to business intelligence and data mining.

Business Analytics Using SAS Enterprise Guide and SAS Enterprise Miner

Author: Olivia Parr-Rud
Publisher: SAS Institute
ISBN: 9781629593289
Release Date: 2014-10-01
Genre: Mathematics

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. Today’s businesses increasingly use data to drive decisions that keep them competitive. Especially with the influx of big data, the importance of data analysis to improve every dimension of business cannot be overstated. Data analysts are therefore in demand; however, many hires and prospective hires, although talented with respect to business and statistics, lack the know-how to perform business analytics with advanced statistical software. Business Analytics Using SAS Enterprise Guide and SAS Enterprise Miner is a beginner’s guide with clear, illustrated, step-by-step instructions that 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. This book is part of the SAS Press program.

SAS Enterprise Miner Exercise and Assignment Handbook for Higher Education

Author: Varol Onur Kayhan
Publisher: Varol Onur Kayhan
ISBN:
Release Date:
Genre: Education

This handbook is written for students in higher education. Instructors teaching predictive analytics courses can assign this handbook to their students to expose them to predictive analytics techniques using SAS Enterprise Miner. The handbook is developed using SAS Enterprise Miner version 12.1, but it should apply to other versions with little to no changes. This handbook does not require students to have any previous knowledge of SAS Enterprise Miner. It walks students through different predictive analytics techniques using step-by-step by instructions. Even though the contents of this handbook can be completed by anyone who has access to SAS Enterprise Miner, knowledge of predictive analytics concepts is essential for this handbook to be helpful. Also, this handbook is not a substitute for any lecture or textbook. It is best if this handbook is used in parallel to lectures.

SAS Enterprise Miner Exercise and Assignment Workbook

Author: Varol Onur Kayhan
Publisher: Varol Onur Kayhan
ISBN:
Release Date:
Genre: Computers

Visit http://sas-book.com to download the data sets used in this workbook. This workbook is written for students in higher education. Instructors teaching predictive analytics courses can assign this workbook to their students to expose them to predictive analytics techniques using SAS Enterprise Miner. The workbook is developed using SAS Enterprise Miner 14.3, but it should apply to other versions with little to no changes. This workbook does not require students to have any previous knowledge of SAS Enterprise Miner. It walks students through the predictive analytics process using step-by-step by instructions. Even though the contents of this workbook can be completed by anyone who has access to SAS Enterprise Miner, knowledge of predictive analytics concepts is essential. Also, this workbook is not a substitute for any lecture or textbook. It is best if this workbook is used in parallel to lectures.

Data Quality for Analytics Using SAS

Author: Gerhard Svolba
Publisher: SAS Institute
ISBN: 9781629598024
Release Date: 2015-05-05
Genre: Mathematics

Analytics offers many capabilities and options to measure and improve data quality, and SAS is perfectly suited to these tasks. Gerhard Svolba's Data Quality for Analytics Using SAS focuses on selecting the right data sources and ensuring data quantity, relevancy, and completeness. The book is made up of three parts. The first part, which is conceptual, defines data quality and contains text, definitions, explanations, and examples. The second part shows how the data quality status can be profiled and the ways that data quality can be improved with analytical methods. The final part details the consequences of poor data quality for predictive modeling and time series forecasting.

Data mining

Author: Ian H. Witten
Publisher:
ISBN: 3446215336
Release Date: 2001
Genre:


Data Preparation for Analytics Using SAS

Author: Gerhard Svolba
Publisher: SAS Institute
ISBN: 9781629597904
Release Date: 2006-11-27
Genre: Mathematics

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!

Datenanalyse mit Python

Author: Wes McKinney
Publisher: O'Reilly
ISBN: 9783960102144
Release Date: 2018-10-29
Genre: Computers

Erfahren Sie alles über das Manipulieren, Bereinigen, Verarbeiten und Aufbereiten von Datensätzen mit Python: Aktualisiert auf Python 3.6, zeigt Ihnen dieses konsequent praxisbezogene Buch anhand konkreter Fallbeispiele, wie Sie eine Vielzahl von typischen Datenanalyse-Problemen effektiv lösen. Gleichzeitig lernen Sie die neuesten Versionen von pandas, NumPy, IPython und Jupyter kennen.Geschrieben von Wes McKinney, dem Begründer des pandas-Projekts, bietet Datenanalyse mit Python einen praktischen Einstieg in die Data-Science-Tools von Python. Das Buch eignet sich sowohl für Datenanalysten, für die Python Neuland ist, als auch für Python-Programmierer, die sich in Data Science und Scientific Computing einarbeiten wollen. Daten und zugehöriges Material des Buchs sind auf GitHub verfügbar.Aus dem Inhalt:Nutzen Sie die IPython-Shell und Jupyter Notebook für das explorative ComputingLernen Sie Grundfunktionen und fortgeschrittene Features von NumPy kennenSetzen Sie die Datenanalyse-Tools der pandasBibliothek einVerwenden Sie flexible Werkzeuge zum Laden, Bereinigen, Transformieren, Zusammenführen und Umformen von DatenErstellen Sie interformative Visualisierungen mit matplotlibWenden Sie die GroupBy-Mechanismen von pandas an, um Datensätzen zurechtzuschneiden, umzugestalten und zusammenzufassenAnalysieren und manipulieren Sie verschiedenste Zeitreihen-DatenFür diese aktualisierte 2. Auflage wurde der gesamte Code an Python 3.6 und die neuesten Versionen der pandas-Bibliothek angepasst. Neu in dieser Auflage: Informationen zu fortgeschrittenen pandas-Tools sowie eine kurze Einführung in statsmodels und scikit-learn.

Einf hrung in die angewandte Wirtschaftsmathematik

Author: Jürgen Tietze
Publisher: Springer-Verlag
ISBN: 9783834892232
Release Date: 2007-12-18
Genre: Mathematics

Hunderte von Abbildungen, Beispielen und Übungsaufgaben ermöglichen ein solides Verständnis und die sichere Beherrschung des wirtschaftwissenschaftlichen Instrumentariums und seiner ökonomischen Anwendung. Die vorliegende 13. Auflage wurde erneut sorgfältig durchgesehen und in vielen Details verbessert.

SAS Data Mining

Author: Cesar Perez Lopez
Publisher: Apress
ISBN: 148420302X
Release Date: 2015-03-16
Genre: Computers

Data mining is used today in many different fields including banking, financial analysis of markets, insurance and private health sectors, education, industrial processes, medicine, biology, bioengineering and telecommunication. But regardless of the field in which it is applied, the core concepts and tasks of data mining do not require nor domain-specific knowledge, nor advanced mathematical treatments. SAS Data Mining presents the most common techniques used in SAS data mining in a simple and easy to understand way using SAS Enterprise Miner, regardless of the specific field you're working in, and without needing to draw on complicated mathematical algorithms. SAS Data Mining therefore describes data mining techniques to you in accessible language and clear, practical, hands-on examples and exercises. Each chapter presents a data mining case study, including the results of the case study, you've built and an interpretation of its results, which is so vital of course to your data mining work. SAS Data Mining begins with an introduction to data mining data and its distinct phases. You'll then learn how to develop the initial phases which include the selection of information, data exploration, data cleansing, transformation of data, and related issues. After these initial data mining phases, this book goes into practical, hands-on detail on both predictive and descriptive data mining techniques. The predictive techniques you'll learn about cover regression, discriminant analysis, decision trees, neural networks, and other model-based techniques. The descriptive techniques then work with variable dimension reduction techniques, techniques of classification and segmentation (clustering), and exploratory data analysis techniques. What you’ll learn Core concepts of data mining, which can apply to whichever domain of data mining you're working in Practical, hands-on examples and exercises of data mining techniques using SAS Data Miner How to conduct initial phase information selection, data exploration, and data cleansing Transforming your initial data selection as appropriate for your data situation About both predictive and descriptive data mining techniques How to use models of regression, discriminant analysis, decision trees, neutral networks and other model-based techniques Variable dimension reduction techniques, and techniques of classification and segmentation (clustering) How to use exploratory data analysis techniques Who this book is for This book is suitable for IT, MIS and data analysis professionals who want to learn to use SAS Enterprise Miner. Database professionals and other technologists who are looking to either get into the area, are new to Enterprise Miner or are making decisions on data analysis technology adoption will also benefit from this book.

Die Zahl die aus der K lte kam

Author: Rudolf Taschner
Publisher: Carl Hanser Verlag GmbH Co KG
ISBN: 9783446436497
Release Date: 2013-07-29
Genre: Science

Wer Zahlen beherrscht, der hat Macht. Schon Archimedes besiegte die römische Flotte mit Mathematik, und Rechenmaschinen schlagen den Menschen im Schach und beim Jeopardy. Rudolf Taschner nimmt uns mit auf einen Streifzug durch die Kulturgeschichte der Zahlen. Er erzählt, wie Blaise Pascal schon im 17. Jahrhundert den Computer erfand, wie Isaac Newton mit der Unendlichkeit rechnen lernte, warum Kurt Gödel zugleich an die Allmacht der Zahlen und an Gespenster glaubte – und sich der britische Geheimdienst an der Zahl 007 die Zähne ausbiss. Taschner lüftet dabei die Geheimnisse der Mathematik und Kryptologie so spannend, leichtfüßig und unterhaltsam, dass auch Nichteingeweihte ihrem Zauber erliegen müssen.

Die Macht der Gewohnheit Warum wir tun was wir tun

Author: Charles Duhigg
Publisher: ebook Berlin Verlag
ISBN: 9783827070746
Release Date: 2012-09-10
Genre: History

Seit kurzem versuchen Hirnforscher, Verhaltenspsychologen und Soziologen gemeinsam neue Antworten auf eine uralte Frage zu finden: Warum tun wir eigentlich, was wir tun? Was genau prägt unsere Gewohnheiten? Anhand zahlreicher Beispiele aus der Forschung wie dem Alltag erzählt Charles Duhigg von der Macht der Routine und kommt dem Mechanismus, aber auch den dunklen Seiten der Gewohnheit auf die Spur. Er erklärt, warum einige Menschen es schaffen, über Nacht mit dem Rauchen aufzuhören (und andere nicht), weshalb das Geheimnis sportlicher Höchstleistung in antrainierten Automatismen liegt und wie sich die Anonymen Alkoholiker die Macht der Gewohnheit zunutze machen. Nicht zuletzt schildert er, wie Konzerne Millionen ausgeben, um unsere Angewohnheiten für ihre Zwecke zu manipulieren. Am Ende wird eines klar: Die Macht von Gewohnheiten prägt unser Leben weit mehr, als wir es ahnen.