9BOOKS

THAT

MAKE PREDICTIVE ANALYTICS EASY

Since Big Data has made some very deep footprints in this era of technology, predictive analysis has emerged as a highly effective, efficient and lucrative part of data analysis.

Data scientists conduct predictive analyses by:

Discovering patterns in past and present data through statistics, data mining and machine learning tools Creation of models Using models to predict business outcomes

 

THE MAGIC OF PREDICTIVE ANALYTICS

Businesses act on these predictions, helping them to:

Retain customers Get ahead of trends Make informed budget decisions

Want to learn about this magical science? Introduce yourself to these 9 books, which make it easy!

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#1 PREDICTIVE ANALYTICS FOR DUMMIESAuthors: Anasse Bari, Mohamed Chaouchi and Tommy Jung

A must read, this 2013 for Dummies book is the perfect introduction to predictive analytics (PA).

Less scientific theory and more practical, you will learn:

Setting realistic goals Creating a PA roadmap Real world applications Building models Employing effective algorithms

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Source: http://as.wiley.com/WileyCDA/WileyTitle/productCd-1118728963.html

#2PREDICTIVE ANALYTICSAuthor: Eric Siegel

Written by a former professor, 2013’s Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die is an award winning, industry staple.

Packed with case studies, the book explores:

State of the art techniques Real world applications Privacy and civil liberty concerns A wide range of affected industries

Easy to understand, fundamental and eye opening, this book is a must read for any aspiring data scientist.

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Source: https://www.salford-systems.com/news/salford-systems-cart-featured-in-new-predictive-analytics-book

#3APPLIED PREDICTIVE MODELING

Authors: Max Kuhn and Kjell Johnson

An experienced statistician’s guide to problem solving with real data, this 2013 publication provides tools for data analysts to create predictive models for yet-to-be-seen data.

An in depth study for both beginners and experienced model builders, the book covers:

Case studies Using R to create graphs Making accurate predictions Classification and regression models

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Source: http://www.texata.com/resources/books/

#4APPLIED PREDICTIVE ANALYTICSAuthor: Dean Abbott

Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst is a 2014 publication.

Armed with hands on examples and case studies, the book explains:

Theory behind predictive analytics Principles and techniques for PA projects How to successfully apply principles to achieve solutions

Readers can apply their knowledge using the companion website and free software trial that comes with the book.

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Source: https://www.bookdepository.com/Applied-Predictive-Analytics-Dean-Abbott/9781118727966

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#5THE SIGNAL AND THE NOISEAuthor: Nate Silver

Originally published in 2012, The Signal and the Noise: Why So Many Prediction Fail – and Some Don’t is written by an expert political forecaster. Interspersed with personal stories and musings, the book explores what sets good predictions apart from bad ones.

The discussion considers: Using biased data sets Using elaborate models to wrongly fit data Ascertaining an underlying theory to guide predictions Bayesian methods of prediction

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Source: http://www.bookdepository.com/The-Signal-and-the-Noise-Nate-Silver/9780143125082?ref=grid-view

#6THE ELEMENTS OF STATISTICAL LEARNING

Authors: Trevor Hastie, Robert Tibshirani and Jerome Friedman

Published in 2001, this classic and comprehensive book covers:

Supervised and unsupervised learning Neural networks Support vector machines Classification trees

The first book to address data mining, inference and prediction from an all inclusive approach, it remains relevant to statisticians and data analysts even today.

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Source: http://www.bookdepository.com/The-Elements-of-Statistical-Learning-Trevor-Hastie-Robert-Tibshirani-Jerome-Friedman/9781489905185?ref=grid-view

#7DATA MINING

Authors: Ian H. Witten, Eibe Frank and Mark A. Hall

Data Mining: Practical Machine Learning Tools and Techniques was originally published in 1999.

Updated since then, readers can explore:

Machine learning concepts Applying predictive tools in real world data mining situations Ensemble learning Performance improvement tips and tricks

Weka’s machine learning software toolkit comes with the latest edition, allowing readers to practice and implement the data mining methods in the book.

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Source: http://www.bookdepository.com/Dat-Mining-Ian-H-Witten-Eibe-Frank-Mark--Hall-Geoffrey-Holmes/9780123748560?ref=grid-view

#8UNCONTROLLEDAuthor: Jim Manzi

Published in 2012, Uncontrolled: The Surprising Payoff of Trial-and-Error for Business, Politics, and Society explores:

The scientific method and why controlled experimentation assures accurate predictions in business. The unrealised potential of Randomized Field Trials and examples of how they can change businesses.

The books claims that controlled experimentation reduces the risk of inaccurate predictive models and can even augment the results of predictive analytics.

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Source: http://www.bookdepository.com/Uncontrolled-Jim-Manzi/9780465023240?ref=grid-view

#9MODELING TECHNIQUES IN

PREDICTIVE ANALYTICS

Author: Thomas W. Miller

In this 2014 guide to data science, Professor Miller explains:

Using languages R and Python to solve real business problems Maximizing the value of different models Crafting and optimizing models

The book is a step by step, highly useful guide to determining problems, pulling data and reaching applicable business results.

WITH PYTHON & R

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Source: http://www.bookdepository.com/Modeling-Techniques-in-Predictive-Analytics-with-Python-and-R-Thomas-W-Miller/9780133892062?ref=grid-view

ANALYZE YOUR FUTURE!As predictive analytics reaches industries from finance to law enforcement, there’s every chance you will come across it. Study the impressive science of predictive analytics by considering an industry relevant business analytics course by IBM at AIMS Institutes.

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Source: http://predictiveanalytics.org