Title: Data Mining for Business Analytics Pdf Concepts, Techniques, and Applications in R
Author: Galit Shmueli
Published Date: 2017-09-05
Page: 574
"This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject."Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in RData Mining for Business Analytics: Concepts, Techniques, and Applications in R presents an applied approach to data mining concepts and methods, using R software for illustration Readers will learn how to implement a variety of popular data mining algorithms in R (a free and open-source software) to tackle business problems and opportunities.This is the fifth version of this successful text, and the first using R. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes:Data Mining for Business Analytics: Concepts, Techniques, and Applications in R is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.
Data Mining for Business Analytics: Concepts, Techniques, and Applications in R presents an applied approach to data mining concepts and methods, using R software for illustration
Readers will learn how to implement a variety of popular data mining algorithms in R (a free and open-source software) to tackle business problems and opportunities.
This is the fifth version of this successful text, and the first using R. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes:
- Two new co-authors, Inbal Yahav and Casey Lichtendahl, who bring both expertise teaching business analytics courses using R, and data mining consulting experience in business and government
- Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students
- More than a dozen case studies demonstrating applications for the data mining techniques described
- End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented
- A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions www.dataminingbook.com
Data Mining for Business Analytics: Concepts, Techniques, and Applications in R is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.
Great Book This book is very accessible for business professionals who want to develop their skills in data mining. I like how the "R" codes are presented and the ease in how it can be reproduced. Furthermore, each chapter is a stand-alone topic and does not generally require a sequenced order of reading from start to finish.An excellent introduction and overview of models that underpin business analytics, with many good business oriented examples There are a lot of good things about this new book. First, this is a very good authoring team. They have a deep (both correct and very up-to-date) understanding that spans the technical aspects of data analytics, and how and why analytics is used in business settings to make business oriented decisions. Second, the book is well crafted. It is very well organized with very clear and to-the-point explanations. Of the five authors of this book, three of them (Shmueli, Bruce and Patel) have been together as a text writing team since 2007, and this is the 5th time they have updated and revised their business analytics text based on new developments in the field, and based on ongoing feedback from faculty and students who have used the text in both academic courses and professional training courses. Third, because this team has been deeply involved in data analytics for decades, they have a good sense of perspective. They understand the history and evolution of models from the statistics community, as well as the history and evolution of models from computing communities spanning both data mining and machine learning. The lead author, Galit Shmueli, has been publishing in both the statistics literature as well as the management science literature on the differences between explanation (and inference) versus prediction. This depth of perspective, and technically correct understanding of these important nuances, are reflected in how the material in this book is organized and presented. Fourth, they provide a unified and coherent approach to understanding these models. They have major sections on classification models and prediction models. They also have sections on time series forecasting, association models, and clustering. In each of these sections on model and application specifics, they draw on the appropriate models from across the communities of statistics, data mining and machine learning. In addition, the book contains sections on the front end (data prep, exploration, and dimension reduction), as well as on the backend of the modelling process (model evaluation). All examples are worked out in R.The specific chapters on each data analytics modeling method are relatively short and to-the-point, as there are numerous textbooks and professional books on every one of the individual methods covered in this text. Because these authors are now in their fifth iteration of the content, and because they get a lot of feedback on what users of this material do and do not clearly understand, this authoring team has a knack for adding special explanatory material for those things that people tend to not understand well, or often misunderstand. While the chapters on each method are by design brief and introductory, they are solidly sufficient and highly informative, even for people with prior background in these methods. They have a good way of knowing what is important to explain, and a good way of explaining what they present.In short, this is an excellent introductory text and also serves as a very good reference text for the most up-to-date thinking on the the modeling that underpins business analytics.Useful!! This is a very useful book for R beginner!Item arrived fast at the beginning of the semester, the package is also protected very wellAppreciate!
Business Data Science pdf
The Integrated Enterprise Excellence System pdf
Analytics pdf
Data Analytics pdf
Machine Learning for Beginners 2019 pdf
Data Analytics For Businesses 2019 pdf
Data Science for Business 2019 (2 BOOKS IN 1) pdf
Data Mining for Business Analytics pdf
Data Mining pdf
Become a Python Data Analyst pdf
Tags: 1118879368 pdf,Data Mining for Business Analytics pdf,Concepts, Techniques, and Applications in R pdf,Galit Shmueli, Peter C. Bruce, Inbal Yahav, Nitin R. Patel, Kenneth C. Lichtendahl Jr.,Data Mining for Business Analytics: Concepts, Techniques, and Applications in R,Wiley,1118879368,Econometrics,Enterprise Applications - Business Intelligence Tools,Probability & Statistics - General,Business - Data processing,Business mathematics - Computer programs,Business;Data processing.,Data mining,Data mining.,R (Computer program language),R (Computer program language).,BUSINESS & ECONOMICS / Econometrics,Business & Economics,Business & Economics/Econometrics,Business & Economics/Information Management,COMPUTERS / Enterprise Applications / Business Intelligence Tools,Computer Applications,Computers/Enterprise Applications - Business Intelligence Tools,DATA MINING,Data Mining Statistics,Data mining.,Database & Data Warehousing Technologies,Decision Sciences,Econometrics,Enterprise Applications - Business Intelligence Tools,General,HC,HC/Mathematik/Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik,MATHEMATICS,MATHEMATICS / Probability & Statistics / General,Mathematik,Non-Fiction,Probability & Statistics,Probability & Statistics - General,R (Computer program language),R (Computer program language).,R (PROGRAMMING LANGUAGE),STATISTICAL ANALYSIS,Scholarly/Undergraduate,Science/Math,Science/Mathematics,TEXT,Textbooks (Various Levels),United States,Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik,business; concepts; analytics; applied approach; applications; mining algorithms; popular; implement; data; variety; software; illustration readers; version; fifth; algorithms; statistical; prediction; machine; two new coauthors; expertise,BUSINESS & ECONOMICS / Econometrics,Business & Economics/Econometrics,Business & Economics/Information Management,COMPUTERS / Enterprise Applications / Business Intelligence Tools,Computers/Enterprise Applications - Business Intelligence Tools,MATHEMATICS / Probability & Statistics / General,Mathematics,Science/Mathematics,HC/Mathematik/Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik
Tidak ada komentar:
Posting Komentar
Catatan: Hanya anggota dari blog ini yang dapat mengirim komentar.