Peter Gedeck's Books

Machine Learning for Business Analytics

Concepts, Techniques, and Applications in R

By: Galit Shmueli,Peter C. Bruce,Peter Gedeck,Inbal Yahav,Nitin R. Patel

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Machine Learning for Business Analytics

Concepts, Techniques, and Applications in R

By: Galit Shmueli,Peter C. Bruce,Peter Gedeck,Inbal Yahav,Nitin R. Patel

MACHINE LEARNING FOR BUSINESS ANALYTICS Machine learning —also known as data mining or data analytics— is a fundamental part of data science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information. Machine Learning for Business Analytics: Concepts, Techniques, and Applications in R provides a comprehensive introduction and an overview of this methodology. This best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, and network analytics. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques. This is the second R edition of Machine Learning for Business Analytics. This edition also includes: A new co-author, Peter Gedeck, who brings over 20 years of experience in machine learning using R An expanded chapter focused on discussion of deep learning techniques A new chapter on experimental feedback techniques including A/B testing, uplift modeling, and reinforcement learning A new chapter on responsible data science Updates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their students A full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniques 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, slides, and case solutions This textbook is an ideal resource for upper-level undergraduate and graduate level courses in data science, predictive analytics, and business analytics. It is also an excellent reference for analysts, researchers, and data science practitioners working with quantitative data in management, finance, marketing, operations management, information systems, computer science, and information technology.

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693

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John Wiley & Sons

Published Date

ISBN 10

1119835194

ISBN 13

9781119835196

Statistics for Data Science and Analytics

By: Peter C. Bruce,Peter Gedeck,Janet Dobbins

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Statistics for Data Science and Analytics

By: Peter C. Bruce,Peter Gedeck,Janet Dobbins

Introductory statistics textbook with a focus on data science topics such as prediction, correlation, and data exploration Statistics for Data Science and Analytics is a comprehensive guide to statistical analysis using Python, presenting important topics useful for data science such as prediction, correlation, and data exploration. The authors provide an introduction to statistical science and big data, as well as an overview of Python data structures and operations. A range of statistical techniques are presented with their implementation in Python, including hypothesis testing, probability, exploratory data analysis, categorical variables, surveys and sampling, A/B testing, and correlation. The text introduces binary classification, a foundational element of machine learning, validation of statistical models by applying them to holdout data, and probability and inference via the easy-to-understand method of resampling and the bootstrap instead of using a myriad of “kitchen sink” formulas. Regression is taught both as a tool for explanation and for prediction. This book is informed by the authors’ experience designing and teaching both introductory statistics and machine learning at Statistics.com. Each chapter includes practical examples, explanations of the underlying concepts, and Python code snippets to help readers apply the techniques themselves. Statistics for Data Science and Analytics includes information on sample topics such as: Int, float, and string data types, numerical operations, manipulating strings, converting data types, and advanced data structures like lists, dictionaries, and sets Experiment design via randomizing, blinding, and before-after pairing, as well as proportions and percents when handling binary data Specialized Python packages like numpy, scipy, pandas, scikit-learn and statsmodels—the workhorses of data science—and how to get the most value from them Statistical versus practical significance, random number generators, functions for code reuse, and binomial and normal probability distributions Written by and for data science instructors, Statistics for Data Science and Analytics is an excellent learning resource for data science instructors prescribing a required intro stats course for their programs, as well as other students and professionals seeking to transition to the data science field.

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390

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John Wiley & Sons

Published Date

ISBN 10

1394253818

ISBN 13

9781394253814

Machine Learning for Business Analytics

Concepts, Techniques, and Applications in R

By: Galit Shmueli,Peter C. Bruce,Peter Gedeck,Inbal Yahav,Nitin R. Patel

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Machine Learning for Business Analytics

Concepts, Techniques, and Applications in R

By: Galit Shmueli,Peter C. Bruce,Peter Gedeck,Inbal Yahav,Nitin R. Patel

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693

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John Wiley & Sons

Published Date

ISBN 10

1119835194

ISBN 13

9781119835196

The Films That Made Me...

By: Peter Bradshaw

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The Films That Made Me...

By: Peter Bradshaw

Like a pizza delivery driver who travels everywhere by moped, or a volcanologist who keeps turning the central heating up, I'm a film critic who loves going to the cinema.' - Peter Bradshaw. Peter Bradshaw is the film reviewer for intelligent, curious cinemagoers; he has worked at the Guardian for twenty years. The Films That Made Me collates his finest reviews from the last two decades, which carry with them his deep experience, knowledge and understanding of film. Introducing each section with a brief introductory article in his light, humorous tone, and ranging from The Cat in the Hat and the Twilight Saga to Synecdoche: New York, Bradshaw shares the films that he loved, the films that he hated, the films that made him laugh, cry, swoon and scared. Bradshaw's reviews range from the insightful and introspective to the savage and funny. The Films That Made Me is a must read for all film fanatics.

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Page Count

561

Publisher

Bloomsbury Publishing

Published Date

ISBN 10

1448217563

ISBN 13

9781448217564

Wie, bitte?

Introductory German for Proficiency

By: William B. Fischer,Peter N. Richardson

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Wie, bitte?

Introductory German for Proficiency

By: William B. Fischer,Peter N. Richardson

A brief, proficiency-based introductory program stressing communicative abilities, and organized according to ACTFL (American Council on the Teaching of Foreign Languages). Use of authentic listening and reading materials (e.g., radio broadcasts, transportation schedules, ads, magazine excerpts) promotes practical fluency. Cultural material is integrated into every aspect of the program. Class text has situational dialogues, brief grammar notes, cultural sections, class activities, and realia. Workbook contains chapter vocabularies, grammar and conversational exercises, lab manual, and reference grammar. Text includes many illustrations.

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Page Count

408

Publisher

Wiley

Published Date

ISBN 10

0471848441

ISBN 13

9780471848448

Machine Learning for Business Analytics

Concepts, Techniques, and Applications in R

By: Galit Shmueli,Peter C. Bruce,Peter Gedeck,Inbal Yahav,Nitin R. Patel

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Machine Learning for Business Analytics

Concepts, Techniques, and Applications in R

By: Galit Shmueli,Peter C. Bruce,Peter Gedeck,Inbal Yahav,Nitin R. Patel

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693

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John Wiley & Sons

Published Date

ISBN 10

1119835194

ISBN 13

9781119835196

Practical Statistics for Data Scientists

50+ Essential Concepts Using R and Python

By: Peter Bruce,Andrew Bruce,Peter Gedeck

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Practical Statistics for Data Scientists

50+ Essential Concepts Using R and Python

By: Peter Bruce,Andrew Bruce,Peter Gedeck

Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher-quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that "learn" from data Unsupervised learning methods for extracting meaning from unlabeled data

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Page Count

368

Publisher

"O'Reilly Media, Inc."

Published Date

ISBN 10

1492072893

ISBN 13

9781492072898

Statistics for Data Science and Analytics

By: Peter C. Bruce,Peter Gedeck,Janet Dobbins

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Statistics for Data Science and Analytics

By: Peter C. Bruce,Peter Gedeck,Janet Dobbins

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390

Publisher

John Wiley & Sons

Published Date

ISBN 10

1394253818

ISBN 13

9781394253814

Data Mining for Business Analytics

Concepts, Techniques and Applications in Python

By: Galit Shmueli,Peter C. Bruce,Peter Gedeck,Nitin R. Patel

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Data Mining for Business Analytics

Concepts, Techniques and Applications in Python

By: Galit Shmueli,Peter C. Bruce,Peter Gedeck,Nitin R. Patel

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

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608

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John Wiley & Sons

Published Date

ISBN 10

111954985X

ISBN 13

9781119549857

Modern Statistics

A Computer-Based Approach with Python

By: Ron S. Kenett,Shelemyahu Zacks,Peter Gedeck

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Modern Statistics

A Computer-Based Approach with Python

By: Ron S. Kenett,Shelemyahu Zacks,Peter Gedeck

This innovative textbook presents material for a course on modern statistics that incorporates Python as a pedagogical and practical resource. Drawing on many years of teaching and conducting research in various applied and industrial settings, the authors have carefully tailored the text to provide an ideal balance of theory and practical applications. Numerous examples and case studies are incorporated throughout, and comprehensive Python applications are illustrated in detail. A custom Python package is available for download, allowing students to reproduce these examples and explore others. The first chapters of the text focus on analyzing variability, probability models, and distribution functions. Next, the authors introduce statistical inference and bootstrapping, and variability in several dimensions and regression models. The text then goes on to cover sampling for estimation of finite population quantities and time series analysis and prediction, concluding with two chapters on modern data analytic methods. Each chapter includes exercises, data sets, and applications to supplement learning. Modern Statistics: A Computer-Based Approach with Python is intended for a one- or two-semester advanced undergraduate or graduate course. Because of the foundational nature of the text, it can be combined with any program requiring data analysis in its curriculum, such as courses on data science, industrial statistics, physical and social sciences, and engineering. Researchers, practitioners, and data scientists will also find it to be a useful resource with the numerous applications and case studies that are included. A second, closely related textbook is titled Industrial Statistics: A Computer-Based Approach with Python. It covers topics such as statistical process control, including multivariate methods, the design of experiments, including computer experiments and reliability methods, including Bayesian reliability. These texts can be used independently or for consecutive courses. The mistat Python package can be accessed at https://gedeck.github.io/mistat-code-solutions/ModernStatistics/ "In this book on Modern Statistics, the last two chapters on modern analytic methods contain what is very popular at the moment, especially in Machine Learning, such as classifiers, clustering methods and text analytics. But I also appreciate the previous chapters since I believe that people using machine learning methods should be aware that they rely heavily on statistical ones. I very much appreciate the many worked out cases, based on the longstanding experience of the authors. They are very useful to better understand, and then apply, the methods presented in the book. The use of Python corresponds to the best programming experience nowadays. For all these reasons, I think the book has also a brilliant and impactful future and I commend the authors for that." Professor Fabrizio RuggeriResearch Director at the National Research Council, ItalyPresident of the International Society for Business and Industrial Statistics (ISBIS)Editor-in-Chief of Applied Stochastic Models in Business and Industry (ASMBI)

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453

Publisher

Springer Nature

Published Date

ISBN 10

3031075668

ISBN 13

9783031075667

Industrial Statistics

A Computer-Based Approach with Python

By: Ron S. Kenett,Shelemyahu Zacks,Peter Gedeck

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Industrial Statistics

A Computer-Based Approach with Python

By: Ron S. Kenett,Shelemyahu Zacks,Peter Gedeck

This innovative textbook presents material for a course on industrial statistics that incorporates Python as a pedagogical and practical resource. Drawing on many years of teaching and conducting research in various applied and industrial settings, the authors have carefully tailored the text to provide an ideal balance of theory and practical applications. Numerous examples and case studies are incorporated throughout, and comprehensive Python applications are illustrated in detail. A custom Python package is available for download, allowing students to reproduce these examples and explore others. The first chapters of the text focus on the basic tools and principles of process control, methods of statistical process control (SPC), and multivariate SPC. Next, the authors explore the design and analysis of experiments, quality control and the Quality by Design approach, computer experiments, and cyber manufacturing and digital twins. The text then goes on to cover reliability analysis, accelerated life testing, and Bayesian reliability estimation and prediction. A final chapter considers sampling techniques and measures of inspection effectiveness. Each chapter includes exercises, data sets, and applications to supplement learning. Industrial Statistics: A Computer-Based Approach with Python is intended for a one- or two-semester advanced undergraduate or graduate course. In addition, it can be used in focused workshops combining theory, applications, and Python implementations. Researchers, practitioners, and data scientists will also find it to be a useful resource with the numerous applications and case studies that are included. A second, closely related textbook is titled Modern Statistics: A Computer-Based Approach with Python. It covers topics such as probability models and distribution functions, statistical inference and bootstrapping, time series analysis and predictions, and supervised and unsupervised learning. These texts can be used independently or for consecutive courses. The mistat Python package can be accessed at https://gedeck.github.io/mistat-code-solutions/IndustrialStatistics/. "This book is part of an impressive and extensive write up enterprise (roughly 1,000 pages!) which led to two books published by Birkhäuser. This book is on Industrial Statistics, an area in which the authors are recognized as major experts. The book combines classical methods (never to be forgotten!) and "hot topics" like cyber manufacturing, digital twins, A/B testing and Bayesian reliability. It is written in a very accessible style, focusing not only on HOW the methods are used, but also on WHY. In particular, the use of Python, throughout the book is highly appreciated. Python is probably the most important programming language used in modern analytics. The authors are warmly thanked for providing such a state-of-the-art book. It provides a comprehensive illustration of methods and examples based on the authors longstanding experience, and accessible code for learning and reusing in classrooms and on-site applications." Professor Fabrizio RuggeriResearch Director at the National Research Council, ItalyPresident of the International Society for Business and Industrial Statistics (ISBIS)Editor-in-Chief of Applied Stochastic Models in Business and Industry (ASMBI)

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Page Count

486

Publisher

Springer Nature

Published Date

ISBN 10

3031284828

ISBN 13

9783031284823

Book's by Peter Gedeck

Machine Learning for Business Analytics

Machine Learning for Business Analytics

Statistics for Data Science and Analytics

Statistics for Data Science and Analytics

Machine Learning for Business Analytics

Machine Learning for Business Analytics

The Films That Made Me...

The Films That Made Me...

Wie, bitte?

Wie, bitte?

Machine Learning for Business Analytics

Machine Learning for Business Analytics

Practical Statistics for Data Scientists

Practical Statistics for Data Scientists

Statistics for Data Science and Analytics

Statistics for Data Science and Analytics

Data Mining for Business Analytics

Data Mining for Business Analytics

Modern Statistics

Modern Statistics

Industrial Statistics

Industrial Statistics

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