Predictive Analytics
Data Mining, Machine Learning and Data Science for Practitioners, 2nd Edition
2nd Edition
Published on 31. March 2021
448 pages
978-0-13-594655-8 (ISBN)
Description
Use Predictive Analytics to Uncover Hidden Patterns and Correlations and Improve Decision-Making
Using predictive analytics techniques, decision-makers can uncover hidden patterns and correlations in their data and leverage these insights to improve many key business decisions. In this thoroughly updated guide, Dr. Dursun Delen illuminates state-of-the-art best practices for predictive analytics for both business professionals and students.
Delen's holistic approach covers key data mining processes and methods, relevant data management techniques, tools and metrics, advanced text and web mining, big data integration, and much more. Balancing theory and practice, Delen presents intuitive conceptual illustrations, realistic example problems, and real-world case studies-including lessons from failed projects. It's all designed to help you gain a practical understanding you can apply for profit.
* Leverage knowledge extracted via data mining to make smarter decisions
* Use standardized processes and workflows to make more trustworthy predictions
* Predict discrete outcomes (via classification), numeric values (via regression), and changes over time (via time-series forecasting)
* Understand predictive algorithms drawn from traditional statistics and advanced machine learning
* Discover cutting-edge techniques, and explore advanced applications ranging from sentiment analysis to fraud detection
Using predictive analytics techniques, decision-makers can uncover hidden patterns and correlations in their data and leverage these insights to improve many key business decisions. In this thoroughly updated guide, Dr. Dursun Delen illuminates state-of-the-art best practices for predictive analytics for both business professionals and students.
Delen's holistic approach covers key data mining processes and methods, relevant data management techniques, tools and metrics, advanced text and web mining, big data integration, and much more. Balancing theory and practice, Delen presents intuitive conceptual illustrations, realistic example problems, and real-world case studies-including lessons from failed projects. It's all designed to help you gain a practical understanding you can apply for profit.
* Leverage knowledge extracted via data mining to make smarter decisions
* Use standardized processes and workflows to make more trustworthy predictions
* Predict discrete outcomes (via classification), numeric values (via regression), and changes over time (via time-series forecasting)
* Understand predictive algorithms drawn from traditional statistics and advanced machine learning
* Discover cutting-edge techniques, and explore advanced applications ranging from sentiment analysis to fraud detection
More details
Edition
2nd edition
Language
English
Place of publication
NJ
United States
Publishing group
Pearson Education (US)
Target group
Professional and scholarly
File size
9,00 MB
ISBN-13
978-0-13-594655-8 (9780135946558)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Book
01/2021
2nd Edition
Pearson FT Press
€47.00
Shipment within 15-20 days
Person
Dr. Dursun Delen is an internationally renowned expert in business analytics, data science, and machine learning. He is often invited to national and international conferences to deliver keynote presentations on topics related to data/text mining, business intelligence, decision support systems, business analytics, data science, and knowledge management. Prior to his appointment as a professor at Oklahoma State University in 2001, Dr. Delen worked for industry for more than 10 years, developing and delivering business analytics solutions to companies. His most recent industrial work was at a privately owned applied research and consulting company, Knowledge Based Systems, Inc. (KBSI), in College Station, Texas, as a research scientist. During his five years at KBSI, Dr. Delen led a number of projects related to decision support systems, enterprise engineering, information systems development, and advanced business analytics that were funded by private industry and federal agencies, including several branches of the Department of Defense, NASA, National Science Foundation, National Institute for Standards and Technology, and the Department of Energy. Today, in addition to his academic endeavors, Dr. Delen provides professional education and consulting services to businesses in assessing their analytics, data science, and information system needs and helping them develop state-of-the-art computerized decision support systems.
In his current academic position, Dr. Delen holds the William S. Spears Endowed Chair in Business Administration and the Patterson Family Endowed Chair in Business Analytics, and he is the director of research for the Center for Health Systems Innovation and regents' professor of management science and information systems in the Spears School of Business at Oklahoma State University. He has published more than 150 peer-reviewed research articles that have appeared in major journals, including Journal of Business Research, Journal of Business Analytics, Decision Sciences Journal, Decision Support Systems, Communications of the ACM, Computers & Operations Research, Annals of Operations Research, Computers in Industry, Journal of Production Operations Management, Artificial Intelligence in Medicine, Journal of the American Medical Informatics Association, Expert Systems with Applications, Renewable and Sustainable Energy Reviews, Energy, and Renewable Energy, among others. He has also authored and coauthored 11 books and textbooks in the broad area of business analytics, data science, and business intelligence.
Dr. Delen regularly chairs tracks and minitracks at various business analytics and information systems conferences. Currently, he is the editor-in-chief for the Journal of Business Analytics and AI in Business (in Frontiers in Artificial Intelligence), senior editor for the Journal of Decision Support Systems, Decision Sciences, and Journal of Business Research, associate editor for Decision Analytics, International Journal of Information and Knowledge Management, and International Journal of RF Technologies, and is on the editorial boards of several other academic journals. He has been the recipient of several research and teaching awards, including the prestigious Fulbright scholar, regents' distinguished teacher and researcher, president's outstanding researcher, and Big Data mentor awards.
In his current academic position, Dr. Delen holds the William S. Spears Endowed Chair in Business Administration and the Patterson Family Endowed Chair in Business Analytics, and he is the director of research for the Center for Health Systems Innovation and regents' professor of management science and information systems in the Spears School of Business at Oklahoma State University. He has published more than 150 peer-reviewed research articles that have appeared in major journals, including Journal of Business Research, Journal of Business Analytics, Decision Sciences Journal, Decision Support Systems, Communications of the ACM, Computers & Operations Research, Annals of Operations Research, Computers in Industry, Journal of Production Operations Management, Artificial Intelligence in Medicine, Journal of the American Medical Informatics Association, Expert Systems with Applications, Renewable and Sustainable Energy Reviews, Energy, and Renewable Energy, among others. He has also authored and coauthored 11 books and textbooks in the broad area of business analytics, data science, and business intelligence.
Dr. Delen regularly chairs tracks and minitracks at various business analytics and information systems conferences. Currently, he is the editor-in-chief for the Journal of Business Analytics and AI in Business (in Frontiers in Artificial Intelligence), senior editor for the Journal of Decision Support Systems, Decision Sciences, and Journal of Business Research, associate editor for Decision Analytics, International Journal of Information and Knowledge Management, and International Journal of RF Technologies, and is on the editorial boards of several other academic journals. He has been the recipient of several research and teaching awards, including the prestigious Fulbright scholar, regents' distinguished teacher and researcher, president's outstanding researcher, and Big Data mentor awards.
Content
Foreword
Chapter 1 Introduction to Analytics
What's in a Name?
Why the Sudden Popularity of Analytics and Data Science?
The Application Areas of Analytics
The Main Challenges of Analytics
A Longitudinal View of Analytics
A Simple Taxonomy for Analytics
The Cutting Edge of Analytics: IBM Watson
Summary
References
Chapter 2 Introduction to Predictive Analytics and Data Mining
What Is Data Mining?
What Data Mining Is Not
The Most Common Data Mining Applications
What Kinds of Patterns Can Data Mining Discover?
Popular Data Mining Tools
The Dark Side of Data Mining: Privacy Concerns
Summary
References
Chapter 3 Standardized Processes for Predictive Analytics
The Knowledge Discovery in Databases (KDD) Process
Cross-Industry Standard Process for Data Mining (CRISP-DM)
SEMMA
SEMMA Versus CRISP-DM
Six Sigma for Data Mining
Which Methodology Is Best?
Summary
References
Chapter 4 Data and Methods for Predictive Analytics
The Nature of Data in Data Analytics
Preprocessing of Data for Analytics
Data Mining Methods
Prediction
Classification
Decision Trees
Cluster Analysis for Data Mining
k-Means Clustering Algorithm
Association
Apriori Algorithm
Data Mining and Predictive Analytics Misconceptions and Realities
Summary
References
Chapter 5 Algorithms for Predictive Analytics
Naive Bayes
Nearest Neighbor
Similarity Measure: The Distance Metric
Artificial Neural Networks
Support Vector Machines
Linear Regression
Logistic Regression
Time-Series Forecasting
Summary
References
Chapter 6 Advanced Topics in Predictive Modeling
Model Ensembles
Bias-Variance Trade-off in Predictive Analytics
Imbalanced Data Problems in Predictive Analytics
Explainability of Machine Learning Models for
Predictive Analytics
Summary
References
Chapter 7 Text Analytics, Topic Modeling, and Sentiment Analysis
Natural Language Processing
Text Mining Applications
The Text Mining Process
Text Mining Tools
Topic Modeling
Sentiment Analysis
Summary
References
Chapter 8 Big Data for Predictive Analytics
Where Does Big Data Come From?
The Vs That Define Big Data
Fundamental Concepts of Big Data
The Business Problems That Big Data Analytics
Addresses
Big Data Technologies
Data Scientists
Big Data and Stream Analytics
Data Stream Mining
Summary
References
Chapter 9 Deep Learning and Cognitive Computing
Introduction to Deep Learning
Basics of "Shallow" Neural Networks
Elements of an Artificial Neural Network
Deep Neural Networks
Convolutional Neural Networks
Recurrent Networks and Long Short-Term Memory Networks
Computer Frameworks for Implementation of Deep Learning
Cognitive Computing
Summary
References
Appendix A KNIME and the Landscape of Tools for Business Analytics and Data Science
9780136738510 TOC 11/12/2020
Chapter 1 Introduction to Analytics
What's in a Name?
Why the Sudden Popularity of Analytics and Data Science?
The Application Areas of Analytics
The Main Challenges of Analytics
A Longitudinal View of Analytics
A Simple Taxonomy for Analytics
The Cutting Edge of Analytics: IBM Watson
Summary
References
Chapter 2 Introduction to Predictive Analytics and Data Mining
What Is Data Mining?
What Data Mining Is Not
The Most Common Data Mining Applications
What Kinds of Patterns Can Data Mining Discover?
Popular Data Mining Tools
The Dark Side of Data Mining: Privacy Concerns
Summary
References
Chapter 3 Standardized Processes for Predictive Analytics
The Knowledge Discovery in Databases (KDD) Process
Cross-Industry Standard Process for Data Mining (CRISP-DM)
SEMMA
SEMMA Versus CRISP-DM
Six Sigma for Data Mining
Which Methodology Is Best?
Summary
References
Chapter 4 Data and Methods for Predictive Analytics
The Nature of Data in Data Analytics
Preprocessing of Data for Analytics
Data Mining Methods
Prediction
Classification
Decision Trees
Cluster Analysis for Data Mining
k-Means Clustering Algorithm
Association
Apriori Algorithm
Data Mining and Predictive Analytics Misconceptions and Realities
Summary
References
Chapter 5 Algorithms for Predictive Analytics
Naive Bayes
Nearest Neighbor
Similarity Measure: The Distance Metric
Artificial Neural Networks
Support Vector Machines
Linear Regression
Logistic Regression
Time-Series Forecasting
Summary
References
Chapter 6 Advanced Topics in Predictive Modeling
Model Ensembles
Bias-Variance Trade-off in Predictive Analytics
Imbalanced Data Problems in Predictive Analytics
Explainability of Machine Learning Models for
Predictive Analytics
Summary
References
Chapter 7 Text Analytics, Topic Modeling, and Sentiment Analysis
Natural Language Processing
Text Mining Applications
The Text Mining Process
Text Mining Tools
Topic Modeling
Sentiment Analysis
Summary
References
Chapter 8 Big Data for Predictive Analytics
Where Does Big Data Come From?
The Vs That Define Big Data
Fundamental Concepts of Big Data
The Business Problems That Big Data Analytics
Addresses
Big Data Technologies
Data Scientists
Big Data and Stream Analytics
Data Stream Mining
Summary
References
Chapter 9 Deep Learning and Cognitive Computing
Introduction to Deep Learning
Basics of "Shallow" Neural Networks
Elements of an Artificial Neural Network
Deep Neural Networks
Convolutional Neural Networks
Recurrent Networks and Long Short-Term Memory Networks
Computer Frameworks for Implementation of Deep Learning
Cognitive Computing
Summary
References
Appendix A KNIME and the Landscape of Tools for Business Analytics and Data Science
9780136738510 TOC 11/12/2020
