Predictive Analytics: How Data Science Predicts What You Are Going to Do

Predictive Analytics: How Data Science Predicts What You Are Going to Do

Eric Siegel / Aug 24, 2019

Predictive Analytics How Data Science Predicts What You Are Going to Do You have been predicted by companies governments law enforcement hospitals and universities Their computers say I knew you were going to do that These institutions are seizing upon the power to pr

  • Title: Predictive Analytics: How Data Science Predicts What You Are Going to Do
  • Author: Eric Siegel
  • ISBN: 9781118416853
  • Page: 176
  • Format: ebook
  • You have been predicted by companies, governments, law enforcement, hospitals and universities Their computers say, I knew you were going to do that These institutions are seizing upon the power to predict whether you re going to click, buy, lie, or die.Why For good reason Predicting human behavior combats financial risk, fortifies healthcare, reduces spam, tougheYou have been predicted by companies, governments, law enforcement, hospitals and universities Their computers say, I knew you were going to do that These institutions are seizing upon the power to predict whether you re going to click, buy, lie, or die.Why For good reason Predicting human behavior combats financial risk, fortifies healthcare, reduces spam, toughens crime fighting and boosts sales.How Prediction is powered by the world s most potent, booming unnatural resource data Accumulated in large part as the by product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away Surprise This heap of refuse is a gold mine Big data embodies an extraordinary wealth of experience from which to learn.Predictive analytics is the science that unleashes the power of data With this technology, the computer literally learns from data how to predict the future behavior of individuals Perfect prediction is not possible, but even lousy predictions can be extremely valuable.In this rich, entertaining primer, former Columbia University professor and Predictive Analytics World co founder Eric Siegel reveals the power and perils of prediction What unique form of mortgage risk Chase Bank predicted before the recession.Predicting which people will drop out of school, cancel a subscription or get divorced before they are even aware of it themselves.Why early retirement decreases life expectancy and vegetarians miss fewer flights.Five reasons organizations predict death, including one health insurance company.The way U.S Bank and European wireless carrier Telenor calculate how to most strongly influence each customer.How companies ascertain untold, private truths how Target figures out you re pregnant and Hewlett Packard deduces you re about to quit your job.How judges and parole boards rely on crime predicting computers to decide who stays in prison and who goes free.What s predicted by the BBC, Citibank, ConEd, Facebook, Ford, Google, IBM, the IRS, Match, MTV, Netflix, Pandora, PayPal, Pfizer, and.A truly omnipresent science, predictive analytics affects everyone, every day Although largely unseen, it drives millions of decisions, determining who to call, mail, investigate, incarcerate, set up on a date, or medicate.Predictive analytics transcends human perception This book s final chapter answers the riddle What often happens to you that cannot be witnessed, and that you can t even be sure has happened afterward but that can be predicted in advance

    Predictive Analytics What it is and why it matters SAS Putting the Magic in the Magic The NBA s Orlando Magic uses SAS predictive analytics to improve revenue and determine starting lineups Business users across the Orlando Magic organization have instant access to information The Magic can now visually explore the freshest data, right down to Predictive Analytics, Big Data, and How to Make Them Work Predictive analytics is the practical result of Big Data and business intelligence BI What do you do when your business collects staggering volumes of new data Today s business applications are raking in mountains of new customer, market, social listening, and real time app, cloud, or Predictive analytics Predictive analytics One of the best known applications is credit scoring, which is used throughout financial services Scoring models process a customer s credit history, loan application, customer data, etc in order to rank order individuals by their likelihood of making future credit payments on time. What Is Predictive Analytics Real World Examples of Predictive analytics refers to using historical data, machine learning, and artificial intelligence to predict what will happen in the future Increasingly often, the idea of predictive analytics has been tied to business intelligence. How Predictive Analytics Is Being Used in Inventory Predictive analytics could generate an alert that a certain product a manufacturer receives is particularly prone to breakage, allowing that company to raise a complaint and cite data from an analytics platform to strengthen a case. Predictive analytics How to bring fortune telling to Predictive analytics is a consistently improving process There should be an ongoing review of the performance of your predictive analytics system, evaluating its impact on cloud operations It can take three to six months to establish the trends and correlations needed to make accurate predictions of system utilization and problem avoidance. Predictive Analytics in What s Possible, Who s Doing Predictive analytics models did take it into account and estimated that the project would take three to four times as long As a result, the company limited the work to the original product team, enabling them to deliver the update on time. What Is Predictive Analytics Things You Need to Know How Predictive Analytics Works Predictive analytics is the process of using data analytics to make predictions based on data This process uses data along with analysis, statistics, and machine learning techniques to create a predictive model for forecasting future events. What is Predictive Analytics Predictive analytics is the branch of the advanced analytics which is used to make predictions about unknown future events Predictive analytics uses many techniques from data mining, statistics, modeling, machine learning, and artificial intelligence to analyze current data to make predictions about future. Predictive Analytics in Human Resources Analytics in HR How HR predictive analytics apply in practice By applying predictive analysis to this data, HR is able to become a strategic partner that relies on proven and data driven predictive models, instead of relying on gut feeling and soft science HR predictive analytics enable HR to forecast the impact of people policies on the well being,

    • Best Read [Eric Siegel] ✓ Predictive Analytics: How Data Science Predicts What You Are Going to Do || [Cookbooks Book] PDF ✓
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      Published :2018-012-26T09:05:17+00:00

    About "Eric Siegel"

      • Eric Siegel

        Eric Siegel Is a well-known author, some of his books are a fascination for readers like in the Predictive Analytics: How Data Science Predicts What You Are Going to Do book, this is one of the most wanted Eric Siegel author readers around the world.


    424 Comments

    1. "Predictive Analytics" is a summary of the state of the art in using computer models to predict individuals actions. I work in the industry and have developed predictive financial models. This book isn't aimed at people like me, at least not ones looking for a more technical, how-to explanation. Instead, this is more a survey of the field, including plentiful real-world examples and some high-level definitions. The definitions of lift, ensemble modeling, and uplift modeling I found new and inter [...]


    2. I wish I could have predicted how much I would dislike this book. After reading just one chapter of Nate Silver's The Signal and the Noise this book comes across as amateurish. Too much noise, not enough signal.


    3. Not thoughtfully written and shallowly propagandistic. It joins so much hype and adds little to the brimming pot. The last couple of chapters are more digestible, but still doesn't do much beyond illuminating the very basics of PA. Here's a bit of my review for book club: I’ll start with positives: Love the suggestion that marketing departments that manufacture quasi-medical data should have to deal with it in a substantive way. That is a solution I haven’t heard proposed yet, but what if HI [...]


    4. This book is extremely introductory, which accounts for Siegel's 50,000-foot view of the topic. Yet, I came away feeling there could have been more details on the "how" of predictive analytics without destroying the book's aim of being an overview. Rather than droning on about IBM's Watson, I thought Siegel could have spent a little more time explaining the logic behind building decision trees and preparing the training data. Instead, we get about 100 pages of fluff out of a 217-page text. A typ [...]


    5. PREDICTIVE ANALYTICS by ERIC SIEGELHaving no previous knowledge of predictive analytics, I was a little afraid this book might leave me bewildered. How wrong I was! My eyes were opened, my interest caught and held throughout this fascinating book.There are many questions that come to mind when reading this book, but as you read on they are all very effectively answered by the author. Predictive analytics are rooted in everyone’s daily lives and can have a substantial effect on their future act [...]


    6. Okay at best. He clearly knows his stuff and has great experience to talk about. He also chooses interesting examples of predictive modeling that he hasn't worked on. But his style is self-absorbed and immature. If you are in the business, you will get something out of reading his book, but you probably won't enjoy it.


    7. Having taken (and taught) persuasion courses in college, it was very interesting to see how far the field has come and how openly it has embraced technology. PA is a much larger field than I had imagined. Its uses, for both good AND evil, offer a lot of insight on ourselves and others, and how we unwittingly give ourselves away more than we think.


    8. 000 stars! what horrifying writing, the middle school me wrote better essays than this guy. I couldn't go pass 8% of this book before quiting. why can't he just stay on point and rationally dissect each point one by one instead of floating all over the place and being lost in his long winded sentences. Dude, maybe you need a crash course in writing in tue social sciences.


    9. This is a painfully badly written book with very layman explanation of the subject. I scanned through first 150 pages reading the same very examples mentioned by lots of other authors. Only 20 pages of this book were worth reading (and still were extremely difficult to consume). And if you're really interested in PA – use your time on Udacity courses – you'll get much more! I would not recommend this book – mostly, it's a waste of your time.


    10. Provides a high level overview of the possibilities of predictive analytics. As someone aspiring to be able to do this type of work, I will definitely be accessing the resources provided throughout the book to obtain more specifics.


    11. As more and more companies try to harness the power of 'Big Data' - the latest business buzz word - books like Siegel's are helpful to get a grasp on just what it is. This book is less 'how to' than an attempt to explain what it is, and how it can work for you, with the latter point venturing a bit too close to hucksterism at times (hey, it is Siegel's field). Siegel does a good job explaining how valuable data is and convinces us that with smart, predictive modeling, data can change how we mark [...]


    12. This was a good overview of the way machine learning is used to improve decision making across a wide range of disciplines from medical, marketing, and politics. It is written in choppy sequences that are separated by quotes, some of the quotes are actually just songs that the author made up. The intended audience of the book is not scientist level. But as an introduction to the field it is very good, and I could see how the methods I know could be used to make these models, even though the auth [...]


    13. This book was full of great examples and was written in a humorous and approachable way. I am somewhat confused by the reviews that say The Signal and The Noise by Nate Silver was better - I found that book desperately in need of an editor. The focus of The Signal and the Noise was also broader, explaining basic statistics, correlation vs. causation, etc. Predictive Analytics was a focused book filled with examples of PA being used successfully.The cover art is awful and the font size a bit too [...]


    14. The content is actually a good, high-level, non-technical overview of the field and the ways data can be used in business.But the writing. Oh goodness, the writing. So many paragraphs feel like the work of a high-schooler just out of "essays 101". Chapters begin with word clouds and quotes (which normally make me shy from a book), and some of those quotes are from the author! I had to force myself to make it to the end.


    15. Lots of filler. This probably could have been condensed to 50 pages or less. I was expecting the book to be a little more technical, or to get into any kind of detail on HOW to conduct predictive analytics (PA). Instead, the author presented multiple examples of how PA has been used or could be used to predict business outcomes. I probably would have liked the book more if I had absolutely no knowledge of the subject area.


    16. A rather interesting book that outlines different prediction methods, but he never gets into the mathematical aspect of predictive analytics. Overall it's a great book for beginners getting into the field and contains ample amounts of information regarding predictions. It also gets into what predictive analytics is being used for today and by what types of companies. It was a rather enlightening read on the subject.


    17. It's an easy reading book for quite heavy topic. Even though most of the topics are not new to me but at least it taught me how to explain predictive analytics in easy term. One thing that I learn most is the last chapter about uplift modeling: not predicting the response, but only focusing those who can be influence through contact.Overall, it's a recommended book for business leaders who wants to double or triple their ROE using analytics.


    18. You've probably heard the story of a Target store figuring out a teenager was pregnant before her family did, and you may have seen Watson beat Ken Jennings at Jeopardy. You may even have some knowledge of A/B testing. This book not only explains how these work -- without getting technical, but with conceptual clarity -- but shows a vast array of related applications.


    19. Siegel's jokey and friendly tone help make this a simple "beginners guide" to PA. by breaking down the often complicated concepts with colloquial language and providing real-life case studies, it becomes an easier read than it should be a good crash course in the basic applications and tenants of predictive analytics.


    20. This is a nice, entertaining book that gives someone an overview about what big data analytics are and how they can be used. it is written so that a novice can understand. I enjoyed the quirky quotes and the various case studies. That said, this book will not help someone who wants to delve deeper into how to actually create these algorithms.


    21. Been through a journey and success stories of predictive models of some famous Corporation. Got this instinct that we can save million bucks if we could do something more wiser which seems quiet small compared to the business operation.


    22. As someone new to machine learning and predictive analytics, this book was a fantastic and accessible overview of all of the major concepts in PA - decision trees, uplift modeling, and the ensemble effect. This is a great read for anyone potentially interested in this emerging field!


    23. Excellent intro into the world of Predictive Analytics - the art and science of using data to learn from individual characteristics, build predictive models, score each element and drive better decisions. Must read for all in business - data analyst or otherwise.


    24. Low signal to noise ratio. Still, a basic introduction to the practical applications of predictive analytics, machine learning and data mining.


    25. Terrible narrator of the audio book version and too wordy and self indulgent. Nate Silver's The Signal and the Noise is much much better.


    26. Amazing introduction to the world of Big Data and predictive analytics. I didn't have much knowledge/experience previously, but this book provoked interest in me.


    27. This is a very superficial overview of the topic of predictive Analytics. Your time is probably better spent researching the topic online.


    28. Good read if you're interested in learning about how analytics can/has been applied in practice. Very quick and easy to get through.


    29. Most books on topics such as this tell you why it is cool but not how it is done. This book tells you how and why it is done. Well done.



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