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Data Mining for Business Analytics

Concepts, Techniques and Applications in Python

Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities. This is the sixth version of this successful text, and the first using Python. 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: A new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process A new section on ethical issues in data mining 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 Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python 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. “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 R

This is the sixth version of this successful text, and the first using Python.

Data Mining for Business Analytics

Concepts, Techniques, and Applications with XLMiner

An applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies. Readers will work with all of the standard data mining methods using the Microsoft® Office Excel® add-in XLMiner® to develop predictive models and learn how to obtain business value from Big Data. Featuring updated topical coverage on text mining, social network analysis, collaborative filtering, ensemble methods, uplift modeling and more, the Third Edition also includes: Real-world examples to build a theoretical and practical understanding of key data mining methods End-of-chapter exercises that help readers better understand the presented material Data-rich case studies to illustrate various applications of data mining techniques Completely new chapters on social network analysis and text mining A companion site with additional data sets, instructors material that include solutions to exercises and case studies, and Microsoft PowerPoint® slides https://www.dataminingbook.com Free 140-day license to use XLMiner for Education software Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition is an ideal textbook for upper-undergraduate and graduate-level courses as well as professional programs on data mining, predictive modeling, and Big Data analytics. The new edition is also a unique reference for analysts, researchers, and practitioners working with predictive analytics in the fields of business, finance, marketing, computer science, and information technology. Praise for the Second Edition "…full of vivid and thought-provoking anecdotes... needs to be read by anyone with a serious interest in research and marketing."– Research Magazine "Shmueli et al. have done a wonderful job in presenting the field of data mining - a welcome addition to the literature." – ComputingReviews.com "Excellent choice for business analysts...The book is a perfect fit for its intended audience." – Keith McCormick, Consultant and Author of SPSS Statistics For Dummies, Third Edition and SPSS Statistics for Data Analysis and Visualization Galit Shmueli, PhD, is Distinguished Professor at National Tsing Hua University’s Institute of Service Science. She has designed and instructed data mining courses since 2004 at University of Maryland, Statistics.com, The Indian School of Business, and National Tsing Hua University, Taiwan. Professor Shmueli is known for her research and teaching in business analytics, with a focus on statistical and data mining methods in information systems and healthcare. She has authored over 70 journal articles, books, textbooks and book chapters. Peter C. Bruce is President and Founder of the Institute for Statistics Education at www.statistics.com. He has written multiple journal articles and is the developer of Resampling Stats software. He is the author of Introductory Statistics and Analytics: A Resampling Perspective, also published by Wiley. Nitin R. Patel, PhD, is Chairman and cofounder of Cytel, Inc., based in Cambridge, Massachusetts. A Fellow of the American Statistical Association, Dr. Patel has also served as a Visiting Professor at the Massachusetts Institute of Technology and at Harvard University. He is a Fellow of the Computer Society of India and was a professor at the Indian Institute of Management, Ahmedabad for 15 years.

The book is a perfect fit for its intended audience." – Keith McCormick, Consultant and Author of SPSS Statistics For Dummies, Third Edition and SPSS Statistics for Data Analysis and Visualization "…extremely well organized, clearly ...

Data Mining with Rattle and R

The Art of Excavating Data for Knowledge Discovery

Data mining is the art and science of intelligent data analysis. By building knowledge from information, data mining adds considerable value to the ever increasing stores of electronic data that abound today. In performing data mining many decisions need to be made regarding the choice of methodology, the choice of data, the choice of tools, and the choice of algorithms. Throughout this book the reader is introduced to the basic concepts and some of the more popular algorithms of data mining. With a focus on the hands-on end-to-end process for data mining, Williams guides the reader through various capabilities of the easy to use, free, and open source Rattle Data Mining Software built on the sophisticated R Statistical Software. The focus on doing data mining rather than just reading about data mining is refreshing. The book covers data understanding, data preparation, data refinement, model building, model evaluation, and practical deployment. The reader will learn to rapidly deliver a data mining project using software easily installed for free from the Internet. Coupling Rattle with R delivers a very sophisticated data mining environment with all the power, and more, of the many commercial offerings.

Throughout this book the reader is introduced to the basic concepts and some of the more popular algorithms of data mining.

Komputasi Statistika

Dalam buku ini para penulis memberikan bahasan tentang pengantar R, pengantar komputasi statistika, pendekatan analisis data, dan pembahasan teori secukupnya yang disertai dengan coding R dan contoh-contohnya. Isi buku ini meliputi manajemen data di R, fungsi-fungsi matematika dan statistika, sebaran diskret dan kontinu, hipotesis statistika, analisis varian, analisis regresi, pengujian hipotesis, regresi logistik, analisis time series, dan Looping kekonvergenan. Secara umum tidak ada syarat khusus untuk membaca buku ini, tetapi disarankan para pembaca telah memiliki pengetahuan matematika dan komputasi dasar serta bersemangat dalam mempelajari program open source R. Detail dari buku ini ditulis secara sistematis sehingga sangat membantu dan mempermudah para pengguna statistika untuk memahaminya, baik dari segi konsep, teori maupun aplikasi khususnya pada software R. Diharapkan buku ini dapat memberikan manfaat bagi para mahasiswa, dosen, peneliti dan siapa saja yang mempunyai perhatian terhadap perkembangan komputasi statistika dan analisis statistika.

Dalam buku ini para penulis memberikan bahasan tentang pengantar R, pengantar komputasi statistika, pendekatan analisis data, dan pembahasan teori secukupnya yang disertai dengan coding R dan contoh-contohnya.

Dasar-Dasar Statistik Sosial

Buku ini sangat tepat dipelajari oleh para akademisi dalam hal ini dosen dan mahasiswa yangg mengambil bidang kajian ilmu sosial dan berfokus pada jenis penelitian kuantitatif. Penelitian kuantitatif pada bidang kajian sosial menjadi semacam anak tiri, bukan karena dianggap tidak penting, akan tetapi karena dianggap menjemukkan dan memusingkan terlebih hal-hal yang berkaitan dengan perhitungan-perhitungan atas data angka (baca statistik) sehingga yang tertanam di benak mereka adalah “jauhi penelitian kuantitatif, statitistik memusingkan, mending memilih penelitian kualitatif saja!” Padahal perkembangan ilmu sosial menjadikan kedudukan penelitian kuantitatif dalam ilmu sosial adalah setara bahkan untuk beberapa kajian justru lebih dibutuhkan. Adapun gerbang untuk memasuki penelitian kuantitatif ini adalah pemahaman statistik, khususnya statistik sosial. Buku ini disusun secara sederhana namun sistematis dan sangat mudah untuk dipahami, dengan harapan memberikan pondasi awal bagi para pembaca untuk mengenali dan memahami statistik sosial. Buku ini juga memilih bahasan-bahasan yang nantinya akan sangat diperlukan dalam melakukan penelitian kuantitatif khususnya yang berkaitan dengan membaca dan memahami data statistik, pengukuran data statistik, penentuan sampel penelitian dan juga pengolahan data dan penarikan kesimpulan atas data kuantitatif dalam ilmu sosial. Diakhir setiap BAB pada buku ini disajikan contoh-contoh soal dan latihan sesuai dengan materi yang telah dipelajari agar semakin memudahkan pembaca untuk memahami setiap pembahasan. Dibagian akhir buku ini pula disajikan project statistik sosial yang bisa dilakukan guna memperdalam pemahaman dan penerapan statistik dalam ilmu sosial dengan cara pembelajaran luar ruangan. Project yang disajikan berupa survey-survey sosial yang tidak hanya bisa dijadikan rujukan untuk model penelitian kuantitatif tetapi juga bisa langsung mengaplikasikan semua pembahasan yang ada dalam buku ini dalam suatu kegiatan bahkan hasilnya bisa langsung dimanfaatkan untuk kepentingan publik.

Buku ini sangat tepat dipelajari oleh para akademisi dalam hal ini dosen dan mahasiswa yangg mengambil bidang kajian ilmu sosial dan berfokus pada jenis penelitian kuantitatif.

STATISTIK TEORI DAN APLIKASI

Statistik dalam aplikasi dibutuhkan sebuah alat yang memudahkan mahasiswa dalam membuat sebuah penelitian untuk tugas akhir skripsi, tesis dan disertasi. Penyajian buku ini terfokus pada teori dan aplikasi mengenai statistik dan penyelesaian persoalan-persoalan ekonomi yang dilengkapi dengan contoh-contoh soal hitungan, aplikasi dan studi kasus. Penyajiannya diawali dengan arti dan lingkup statistik, penelitian dan sebagainya. Kemudian dilanjutkan mengenai penyajian data statistik, distribusi frekuensi, ukuran pemusatan dan penyimpangan, angka indeks, regresi dan korelasi, analisis penelitian korelasi dan prediksi serta jenis-jenis uji statistik secara singkat. Dalam penyajian buku statistik teori dan aplikasi ini hanya dibahas mengenai dasar-dasar dalam statistik yang terdiri dari teori dan aplikasinya dalam ilmu ekonomi, buku statistik selanjutnya akan dibahas secara mendalam mengenai jenis-jenis uji statistik dan sebagainya secara lengkap beserta contoh-contoh soal dan aplikasinya dalam ilmu ekonomi.

Dalam penyajian buku statistik teori dan aplikasi ini hanya dibahas mengenai dasar-dasar dalam statistik yang terdiri dari teori dan aplikasinya dalam ilmu ekonomi, buku statistik selanjutnya akan dibahas secara mendalam mengenai jenis ...

Pengantar Matematika Aktuaria

Buku ini merupakan hasil kolaborasi penulisan yang membahas matematika aktuaria. Pembaca akan mudah memahami matematika aktuaria karena akan dibahas mulai dari sejarah dan profesi dari aktuaria serta pembahasan tentang berbagai aspek dan teori diungkapkan pada buku ini.

Buku ini merupakan hasil kolaborasi penulisan yang membahas matematika aktuaria.

DATA MINING

Teori dan Aplikasi Weka

buku ini dijelaskan secara rinci mulai dari persiapaan ataupan perancangan, pendefenisian secara teori, contoh kasus dan penyelesiaan masalah serta pengujian dengan aplikasi weka. Buku ini juga merupakan jilid 1, yang nantinya akan ada perbaikan – perbaikan dan pengujian dengan aplikasi – aplikasi pendukung yang telah tersedia sehingga dapat digunakan secara berkelanjutan.

buku ini dijelaskan secara rinci mulai dari persiapaan ataupan perancangan, pendefenisian secara teori, contoh kasus dan penyelesiaan masalah serta pengujian dengan aplikasi weka.

Matrix-Based Introduction to Multivariate Data Analysis

This book enables readers who may not be familiar with matrices to understand a variety of multivariate analysis procedures in matrix forms. Another feature of the book is that it emphasizes what model underlies a procedure and what objective function is optimized for fitting the model to data. The author believes that the matrix-based learning of such models and objective functions is the fastest way to comprehend multivariate data analysis. The text is arranged so that readers can intuitively capture the purposes for which multivariate analysis procedures are utilized: plain explanations of the purposes with numerical examples precede mathematical descriptions in almost every chapter. This volume is appropriate for undergraduate students who already have studied introductory statistics. Graduate students and researchers who are not familiar with matrix-intensive formulations of multivariate data analysis will also find the book useful, as it is based on modern matrix formulations with a special emphasis on singular value decomposition among theorems in matrix algebra. The book begins with an explanation of fundamental matrix operations and the matrix expressions of elementary statistics, followed by the introduction of popular multivariate procedures with advancing levels of matrix algebra chapter by chapter. This organization of the book allows readers without knowledge of matrices to deepen their understanding of multivariate data analysis.

This book enables readers who may not be familiar with matrices to understand a variety of multivariate analysis procedures in matrix forms.

Applied Multivariate Data Analysis

Multivariate analysis plays an important role in the understanding of complex data sets requiring simultaneous examination of all variables. Breaking through the apparent disorder of the information, it provides the means for both describing and exploring data, aiming to extract the underlying patterns and structure. This intermediate-level textbook introduces the reader to the variety of methods by which multivariate statistical analysis may be undertaken. Now in its 2nd edition, 'Applied Multivariate Data Analysis' has been fully expanded and updated, including major chapter revisions as well as new sections on neural networks and random effects models for longitudinal data. Maintaining the easy-going style of the first edition, the authors provide clear explanations of each technique, as well as supporting figures and examples, and minimal technical jargon. With extensive exercises following every chapter, 'Applied Multivariate Data Analysis' is a valuable resource for students on applied statistics courses and applied researchers in many disciplines.

With extensive exercises following every chapter, the book is a valuable resource for students on applied statistics courses and for applied researchers in many disciplines.