I'm a studentI'm an educator

Predictive Analytics: Data Mining, Machine Learning and Data Science for Practitioners, 2nd edition

Published by Pearson FT Press (December 21, 2020) © 2021

  • Dursun Delen Oklahoma State University
Products list
$39.99

Price Reduced From: $49.99

Currently unavailable

Details

  • A print text
  • Free shipping
  • Also available for purchase as an ebook from all major ebook resellers, including InformIT.com
Products list
$140.00

Access Details

  • Access courses online from any computer (PC or Mac) or tablet (Android or iOS)
  • Native app available for mobile use; use online, or download and work offline; data syncs automatically 
  • Purchase print or digital codes from your college bookstore, or printed access code cards here

Features

  • Interactive learning elements throughout, including exercises, quizzes, flashcards, and video tutorials
In Predictive Analytics: Data Mining, Machine Learning and Data Science for Practitioners, Dr. Dursun Delen illuminates state-of-the-art best practices for predictive analytics for students. Using predictive analytics techniques, students can uncover hidden patterns and correlations in their data, and leverage this insight to improve a wide range of business decisions. Delen’s holistic approach covers all this, and more:
  • Data mining processes, methods, and techniques
  • The role and management of data
  • Predictive analytics tools and metrics
  • Techniques for text and web mining, and for sentiment analysis
  • Integration with cutting-edge Big Data approaches
Throughout, Delen promotes understanding by presenting numerous conceptual illustrations, motivational success stories, failed projects that teach important lessons, and simple, hands-on tutorials that set this guide apart from competitors.
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


Need help? Get in touch