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Predictive Analytics For Dummies®
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Predictive Analytics For Dummies®
by Dr. Tommy Jung, Dr. Mohamed Chaouchi, Dr. Anasse Bari
Predictive Analytics For Dummies, 2nd Edition
Cover
Cover
Introduction
About This Book
Foolish Assumptions
Icons Used in This Book
Beyond the Book
Where to Go from Here
Part 1: Getting Started with Predictive Analytics
Chapter 1: Entering the Arena
Exploring Predictive Analytics
Adding Business Value
Starting a Predictive Analytic Project
Ongoing Predictive Analytics
Forming Your Predictive Analytics Team
Surveying the Marketplace
Chapter 2: Predictive Analytics in the Wild
Online Marketing and Retail
Implementing a Recommender System
Target Marketing
Personalization
Content and Text Analytics
Chapter 3: Exploring Your Data Types and Associated Techniques
Recognizing Your Data Types
Identifying Data Categories
Generating Predictive Analytics
Connecting to Related Disciplines
Chapter 4: Complexities of Data
Finding Value in Your Data
Constantly Changing Data
Complexities in Searching Your Data
Differentiating Business Intelligence from Big-Data Analytics
Exploration of Raw Data
Part 2: Incorporating Algorithms in Your Models
Chapter 5: Applying Models
Modeling Data
Healthcare Analytics Case Studies
Social and Marketing Analytics Case Studies
Prognostics and its Relation to Predictive Analytics
The Rise of Open Data
Chapter 6: Identifying Similarities in Data
Explaining Data Clustering
Converting Raw Data into a Matrix
Identifying Groups in Your Data
Finding Associations in Data Items
Applying Biologically Inspired Clustering Techniques
Chapter 7: Predicting the Future Using Data Classification
Explaining Data Classification
Introducing Data Classification to Your Business
Exploring the Data-Classification Process
Using Data Classification to Predict the Future
Ensemble Methods to Boost Prediction Accuracy
Deep Learning
Part 3: Developing a Roadmap
Chapter 8: Convincing Your Management to Adopt Predictive Analytics
Making the Business Case
Gathering Support from Stakeholders
Presenting Your Proposal
Chapter 9: Preparing Data
Listing the Business Objectives
Processing Your Data
Working with Features
Structuring Your Data
Chapter 10: Building a Predictive Model
Getting Started
Developing and Testing the Model
Going Live with the Model
Chapter 11: Visualization of Analytical Results
Visualization as a Predictive Tool
Evaluating Your Visualization
Visualizing Your Model’s Analytical Results
Novel Visualization in Predictive Analytics
Big Data Visualization Tools
Part 4: Programming Predictive Analytics
Chapter 12: Creating Basic Prediction Examples
Installing the Software Packages
Preparing the Data
Making Predictions Using Classification Algorithms
Chapter 13: Creating Basic Examples of Unsupervised Predictions
Getting the Sample Dataset
Using Clustering Algorithms to Make Predictions
Chapter 14: Predictive Modeling with R
Programming in R
Making Predictions Using R
Chapter 15: Avoiding Analysis Traps
Data Challenges
Analysis Challenges
Part 5: Executing Big Data
Chapter 16: Targeting Big Data
Major Technological Trends in Predictive Analytics
Applying Open-Source Tools to Big Data
Chapter 17: Getting Ready for Enterprise Analytics
Analytics as a Service
Preparing for a Proof-of-Value of Predictive Analytics Prototype
Part 6: The Part of Tens
Chapter 18: Ten Reasons to Implement Predictive Analytics
Identifying Business Goals
Knowing Your Data
Organizing Your Data
Satisfying Your Customers
Reducing Operational Costs
Increasing Returns on Investments (ROI)
Gaining Rapid Access to Information
Making Informed Decisions
Gaining Competitive Edge
Improving the Business
Chapter 19: Ten Steps to Build a Predictive Analytic Model
Building a Predictive Analytics Team
Setting the Business Objectives
Preparing Your Data
Sampling Your Data
Avoiding “Garbage In, Garbage Out”
Creating Quick Victories
Fostering Change in Your Organization
Building Deployable Models
Evaluating Your Model
Updating Your Model
About the Authors
Connect with Dummies
End User License Agreement
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Predictive Analytics
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Introduction
Predictive Analytics For Dummies®
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Table of Contents
Cover
Introduction
About This Book
Foolish Assumptions
Icons Used in This Book
Beyond the Book
Where to Go from Here
Part 1: Getting Started with Predictive Analytics
Chapter 1: Entering the Arena
Exploring Predictive Analytics
Adding Business Value
Starting a Predictive Analytic Project
Ongoing Predictive Analytics
Forming Your Predictive Analytics Team
Surveying the Marketplace
Chapter 2: Predictive Analytics in the Wild
Online Marketing and Retail
Implementing a Recommender System
Target Marketing
Personalization
Content and Text Analytics
Chapter 3: Exploring Your Data Types and Associated Techniques
Recognizing Your Data Types
Identifying Data Categories
Generating Predictive Analytics
Connecting to Related Disciplines
Chapter 4: Complexities of Data
Finding Value in Your Data
Constantly Changing Data
Complexities in Searching Your Data
Differentiating Business Intelligence from Big-Data Analytics
Exploration of Raw Data
Part 2: Incorporating Algorithms in Your Models
Chapter 5: Applying Models
Modeling Data
Healthcare Analytics Case Studies
Social and Marketing Analytics Case Studies
Prognostics and its Relation to Predictive Analytics
The Rise of Open Data
Chapter 6: Identifying Similarities in Data
Explaining Data Clustering
Converting Raw Data into a Matrix
Identifying Groups in Your Data
Finding Associations in Data Items
Applying Biologically Inspired Clustering Techniques
Chapter 7: Predicting the Future Using Data Classification
Explaining Data Classification
Introducing Data Classification to Your Business
Exploring the Data-Classification Process
Using Data Classification to Predict the Future
Ensemble Methods to Boost Prediction Accuracy
Deep Learning
Part 3: Developing a Roadmap
Chapter 8: Convincing Your Management to Adopt Predictive Analytics
Making the Business Case
Gathering Support from Stakeholders
Presenting Your Proposal
Chapter 9: Preparing Data
Listing the Business Objectives
Processing Your Data
Working with Features
Structuring Your Data
Chapter 10: Building a Predictive Model
Getting Started
Developing and Testing the Model
Going Live with the Model
Chapter 11: Visualization of Analytical Results
Visualization as a Predictive Tool
Evaluating Your Visualization
Visualizing Your Model’s Analytical Results
Novel Visualization in Predictive Analytics
Big Data Visualization Tools
Part 4: Programming Predictive Analytics
Chapter 12: Creating Basic Prediction Examples
Installing the Software Packages
Preparing the Data
Making Predictions Using Classification Algorithms
Chapter 13: Creating Basic Examples of Unsupervised Predictions
Getting the Sample Dataset
Using Clustering Algorithms to Make Predictions
Chapter 14: Predictive Modeling with R
Programming in R
Making Predictions Using R
Chapter 15: Avoiding Analysis Traps
Data Challenges
Analysis Challenges
Part 5: Executing Big Data
Chapter 16: Targeting Big Data
Major Technological Trends in Predictive Analytics
Applying Open-Source Tools to Big Data
Chapter 17: Getting Ready for Enterprise Analytics
Analytics as a Service
Preparing for a Proof-of-Value of Predictive Analytics Prototype
Part 6: The Part of Tens
Chapter 18: Ten Reasons to Implement Predictive Analytics
Identifying Business Goals
Knowing Your Data
Organizing Your Data
Satisfying Your Customers
Reducing Operational Costs
Increasing Returns on Investments (ROI)
Gaining Rapid Access to Information
Making Informed Decisions
Gaining Competitive Edge
Improving the Business
Chapter 19: Ten Steps to Build a Predictive Analytic Model
Building a Predictive Analytics Team
Setting the Business Objectives
Preparing Your Data
Sampling Your Data
Avoiding “Garbage In, Garbage Out”
Creating Quick Victories
Fostering Change in Your Organization
Building Deployable Models
Evaluating Your Model
Updating Your Model
About the Authors
Connect with Dummies
End User License Agreement
Guide
Cover
Table of Contents
Begin Reading
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