Synopses & Reviews
Learn to:- Analyze structured and unstructured data
- Use algorithms and data analysis techniques
- Build clustering, classification and statistical models
- Apply predictive analytics to your website and marketing efforts
A practical guide to using Big Data and technology to discover real-world insights
Predict the future! Data is growing exponentially and predictive analytics is your organization’s key to making use of it to create a competitive advantage. This comprehensive resource will help you define real-world projects and takes a step-by-step approach to the technical aspects of predictive analytics so you can get up and running right away.
- Enter the arena — jump into predictive analytics by discovering how data can translate to a competitive advantage
- Incorporating algorithms — discover data models, how to identify similarities and relationships, and how to predict the future through data classification
- Developing a roadmap — prepare your data, create goals, structure and process your data, and build a predictive model that will get stakeholder buy-in
- Programming predictive analytics — use in-depth tips to install software, modules, and libraries to get going with prediction models
- Making predictive analytics work — gain an understanding of the typical pushback on predictive analytics adoption and how to overcome it
Open the book and find:
- Real-world tips for creating business value
- Common use cases to help you get started
- Details on modeling, k-means clustering, and more
- How you can predict the future with classification
- Information on structuring your data
- Methods for testing models
- Hands-on guides to software installation
- Tips on outlining business goals and approaches
Synopsis
Predictive Analytics For Dummies will help the reader understand the core of predictive analytics and get them started quickly as possible with readily available tools to collect and analyze data, and then make predictions. This book will not bog the reader down with advanced mathematical pre-requisites, but will cover just enough concepts to make meaningful decisions on which algorithms to use and how to create effective predictive models. The author will also address “soft” issues, including handling people, setting realistic goals, protecting budgets, making useful presentations, and more, to help the reader prepare for shepherding predictive analysis projects through their companies.
Coverage will include:
- The basics of predictive analytics
- Using structured and unstructured data
- Data mining to collect data
- Algorithms and techniques for analyzing data
- Building clustering, association, and statistical models
- Creating your predictive analysis roadmap
- Applying your predictive analytics knowledge to the web, marketing, finance, health, and content
- Overcoming predictive analysis challenges
Synopsis
Combine business sense, statistics, and computers in a new and intuitive way, thanks to Big DataPredictive analytics is a branch of data mining that helps predict probabilities and trends. Predictive Analytics For Dummies explores the power of predictive analytics and how you can use it to make valuable predictions for your business, or in fields such as advertising, fraud detection, politics, and others. This practical book does not bog you down with loads of mathematical or scientific theory, but instead helps you quickly see how to use the right algorithms and tools to collect and analyze data and apply it to make predictions.
Topics include using structured and unstructured data, building models, creating a predictive analysis roadmap, setting realistic goals, budgeting, and much more.
- Shows readers how to use Big Data and data mining to discover patterns and make predictions for tech-savvy businesses
- Helps readers see how to shepherd predictive analytics projects through their companies
- Explains just enough of the science and math, but also focuses on practical issues such as protecting project budgets, making good presentations, and more
- Covers nuts-and-bolts topics including predictive analytics basics, using structured and unstructured data, data mining, and algorithms and techniques for analyzing data
- Also covers clustering, association, and statistical models; creating a predictive analytics roadmap; and applying predictions to the web, marketing, finance, health care, and elsewhere
Propose, produce, and protect predictive analytics projects through your company with Predictive Analytics For Dummies.
About the Author
Dr. Anasse Bari is a Fulbright scholar, a software engineer, and a data mining expert. Mohamed Chaouchi has conducted extensive research using data mining methods in both health and financial domains. Tommy Jung has worked extensively on natural language processing and algorithmic trading using machine learning.
Table of Contents
Introduction 1
Part I: Getting Started with Predictive Analytics 5
Chapter 1: Entering the Arena 7
Chapter 2: Predictive Analytics in the Wild 19
Chapter 3: Exploring Your Data Types and Associated Techniques 43
Chapter 4: Complexities of Data 57
Part II: Incorporating Algorithms in Your Models 73
Chapter 5: Applying Models 75
Chapter 6: Identifying Similarities in Data 89
Chapter 7: Predicting the Future Using Data Classification 115
Part III: Developing a Roadmap 145
Chapter 8: Convincing Your Management to Adopt Predictive Analytics 147
Chapter 9: Preparing Data 167
Chapter 10: Building a Predictive Model 177
Chapter 11: Visualization of Analytical Results 189
Part IV: Programming Predictive Analytics 205
Chapter 12: Creating Basic Prediction Examples 207
Chapter 13: Creating Basic Examples of Unsupervised Predictions 233
Chapter 14: Predictive Modeling with R 249
Chapter 15: Avoiding Analysis Traps 275
Chapter 16: Targeting Big Data 295
Part V: The Part of Tens 307
Chapter 17: Ten Reasons to Implement Predictive Analytics 309
Chapter 18: Ten Steps to Build a Predictive Analytic Model 319
Index 331