Lýsing:
Use Big Data and technology to uncover real-world insights You don't need a time machine to predict the future. All it takes is a little knowledge and know-how, and Predictive Analytics For Dummies gets you there fast. With the help of this friendly guide, you'll discover the core of predictive analytics and get started putting it to use with readily available tools to collect and analyze data.
In no time, you'll learn how to incorporate algorithms through data models, identify similarities and relationships in your data, and predict the future through data classification. Along the way, you'll develop a roadmap by preparing your data, creating goals, processing your data, and building a predictive model that will get you stakeholder buy-in. Big Data has taken the marketplace by storm, and companies are seeking qualified talent to quickly fill positions to analyze the massive amount of data that are being collected each day.
If you want to get in on the action and either learn or deepen your understanding of how to use predictive analytics to find real relationships between what you know and what you want to know, everything you need is a page away! Offers common use cases to help you get started Covers details on modeling, k-means clustering, and more Includes information on structuring your data Provides tips on outlining business goals and approaches The future starts today with the help of Predictive Analytics For Dummies.
Annað
- Höfundur: Anasse Bari
- Útgáfa:2
- Útgáfudagur: 2016-09-16
- Hægt að prenta út 10 bls.
- Hægt að afrita 2 bls.
- Format:ePub
- ISBN 13: 9781119267010
- Print ISBN: 9781119267003
- ISBN 10: 1119267013
Efnisyfirlit
- 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
- Chapter 1: Entering the Arena
- 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
- 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
- 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
- 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
- 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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- Gerð : 208
- Höfundur : 11011
- Útgáfuár : 2016
- Leyfi : 380