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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.
- Sales Rank: #43802 in Books
- Published on: 2016-10-31
- Original language: English
- Number of items: 1
- Dimensions: 9.30" h x .90" w x 7.40" l, .0 pounds
- Binding: Paperback
- 456 pages
From the Back Cover
Real-world tips for creating business value
Details on modeling, data clustering, and more
Enterprise use cases to help you get started
Learn to predict the future!
Business today relies on effectively using data to predict trends and sales. Predictive analytics is the tool that can make it happen, and this book eliminates the tricks and shows you how to use it. You'll learn to prepare and process your data, create goals, build a predictive model, get your organization's stakeholders on board, and more.
Inside...
- How to start a project
- Identifying data types
- Modeling tips
- Working with algorithms
- How data clustering works
- How data classification works
- How deep learning works
- Advice on presentations
- Step-by-step predictive modeling
About the Author
Anasse Bari, Ph.D. is data science expert and a university professor who has many years of predictive modeling and data analytics experience.
Mohamed Chaouchi is a veteran software engineer who has conducted extensive research using data mining methods.
Tommy Jung is a software engineer with expertise in enterprise web applications and analytics.
Most helpful customer reviews
3 of 3 people found the following review helpful.
Over 400 pages of great information, the authors explain all aspects of predictive ...
By Amazon Customer
First impression: this is a major upgrade from the first edition. Over 400 pages of great information, the authors explain all aspects of predictive analytics and data modeling. This is one of the rare books that cover everything you need to know about this fascinating field. The book covers types of data and various aspects of analytics, data preparation, data modeling, algorithms, technologies, and it dives into the realm of big data. It provides solid insights on how to and what to expect. This is a must read by all practitioners and would-be data scientists.
2 of 2 people found the following review helpful.
Highly recommended.
By db
This is an all encompassing book about Predictive Analytics, covering all the topics you run into in this field. As a practitioner, I found this book very satisfying; it strikes a balance between in-depth analysis, and broad-ground covering of the relevant topics. The tips dispensed throughout the book were especially helpful. Also, the authors were able to communicate very complex topics relating to modeling aspects, and data preparation with clarity and precision rarely found in specialized books. Highly recommended.
3 of 3 people found the following review helpful.
Excellent Introduction to The Techniques of Predictive Analytics, Data Mining and Machine Learning
By Ira Laefsky
Predictive Analytics is defined by Wikipedia to be "a variety of statistical techniques from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events." This excellent book intended for the intelligent layman with basic computer and mathematical skills is more of a comprehensive review of the variety of techniques including statistics, data mining and machine learning that are used in business and other endeavors to solve a wide variety of problems including forecasting, classification, clustering and other sorts of analytical modeling. The concepts are explained at a level that anyone with an early college background in math and computer usage (not necessarily programming can understand and these are applied to a variety of problems ranging from marketing, to bird flocking and championship performance at the game of Go. Speaking of an area where I have some prior knowledge I found the explanation of "Neural Nets" and "Deep Learning" more comprehensible than many introductory books specifically devoted to these techniques.
In conclusion, I would say this book is a readable and comprehensive introduction to all applications of data mining, machine learning and a wide swath of statistics. One who reads carefully and executes some of the examples will be prepared to ask probing questions of any practitioner of Predictive Analytics and Data Science, and perform their own analysis for many simple cases.
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