[Dummies 01] • Predictive Analytics For Dummies · 2nd Edition by Bari, Anasse -- Read -- Imperial Library of Trantor

Index

Table of Contents

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

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