US News version of Predictive Analytics
593741
Gives examples where people use modeling for prediction. Precious little about sample models or variables in models.  For Casual reading not the serious student.
1280713
Jeff Hirsch's Super Boom is a prediction that the Dow Jones Index will hit 38,820 by 2025.  When I saw the cover, I thought, "You're kidding, right?"

It was bold enough that I figured I had to take a look.  Plus Doug Kass endorsed the book.  So I got a copy and blasted through it in just a couple days.  Predictions aside, the book is about boom periods, the patterns that surround "super booms" (quantitatively, a 500% rise in a bull market), and the elements that compose a market boom.  According to the book, there have been three recorded super booms each following a major world war, significant inflation, and some kind of significant technological development (internal combustion, automobiles, computers, the internet, etc).  I love books that coin a phrase (Super Boom), prove its existence (with data, not opinion), and then make it worthwhile to know about (relevant to today).

The pattern, which the book is based upon, is interesting and I enjoyed the quantitative focus (though sometimes a bit number-heavy) and the effort of the author to prove the pattern's existence throughout history (that it is a recurrent phenomena, not a blip).

The contents, briefly: Chapter 1 was a breakdown of what a super boom is.  Chapter 2 followed with our current outlook.  The next few chapters went through some of the other famous predictions out there (where they went wrong, why they're wrong now), which was probably worthwhile (remember Dow 36,000???) if a bit tedious.  Then the book gets to the historical stuff -- the patterns throughout history, the main criteria of a boom.  The author concludes with advice about the current boom -- how to prepare for it and what to invest in going forward.

There is specific investment advice (stocks, sectors, strategies, etc) which was helpful (the Switching Strategy in chapter 10 could've been a book by itself), but I think the real value is the boom pattern.  As the author readily admits, the next technological breakthrough hasn't happened yet, so it's very hard to know what will be the largest driver of the next super boom.  So there's a little bit of "wait and see."  That said, with all the fear mongering in the media, it's incredibly refreshing to hear a legitimate argument for long-term market growth, instead of "Is gold a bubble?  Are tech stocks a bubble? Are all financial institutions going to collapse!?"  The book offers a positive outlook grounded in reality.

I'm a fairly simple investor -- some individual equities and a 401(k) -- but I do play with timing in and out of the market (which helped a ton in 2008 and 2009) and so this book was helpful in convincing ole contrarian me that what we're seeing now is not a temporary upswing, but part of a potentially much longer and larger boom, something like what we saw in the 80s-90s, a super boom. I highly recommend picking up a copy.
1127637
Pr. Anil has put great effort on providing much more useful section on predictive analytics, which combines common-sense explanations of the the principles and fairly high-level details of the statistical techniques involved. This book has also 'Caselet' that provides real time examples and review questions that wrap-up what you learn in each chapter.
1074068
This is an excellent potpourri of current news and events and could not be more reasonably priced. Some of the articles are thorough beyond anything else in today's press.
2017431
I recommend this book also for journalists. It has very good chapters how to analyze data.
909047
When I took a job to model prediction of loss reserves for workers compensation insurance, I began to realize that the traditional statistical methods that I generally relied n would not help me (without modification). The required modification would be either to transform variables or to model long-tailed probability distributions. This is because in the insurance business you have to reserve for those big catastrophies. The cost data for workers compensation data generally show a high frequency of low to moderate costs... . However occasionally there are a few cases of sever injury causing permanent disability which could run over 1 million dollars. Even though the probability of occurrence is small the cost is so high that it cannot be ignored. Such claims will surely be found when large insurance company cover millions of employees over many years.
The problem occurs when insuring for floods, earthquakes, fires and other disasters. Stuart Klugman and Bob Hogg in 1984 wrote the first introductory text to acquaint statisticians with such probability models that are important in the insurance business. Other books covering the subject were covered in books on risk theory designed for actuaries. This book covers all the topics and assumes mathematical and staistical knowledge at the level of the book by Hogg and Craig (so some calculus is required).
472801
This is just a beautiful app that works. USA Today will please nearly everyone. If you like general news and want quick reads as well as longer articles this is the app for you
692520
This is my go-to app for clear, concise, correct & current  news reporting.  I like having access to the news always at my finger tips.
1803561
Silver explains application of Bayesian statistics and regression analysis to an intriguing mix of applications that everyone can relate to, or at least to most of them, e.g., from baseball and poker to politics, financial crises, climate change and terrorism. It's intriguing but basically sensible that scientific forecasts work best for systems that can be observed, such as weather forecasting and less for "hidden" phenomena, such as earthquakes. I enjoyed seeing the same statistical principles applied over such a broad range of topics.
2918817
This is a very useful program.  It allows me to keep up with the news at all times without having to read newsprint.  The format is easy to use for me.
2093629
Great applications for data.  The most difficult part of analytics is using data to create usable information. This book does exactly that.
2115413
Nice book relatively easy to read, although sometimes less instructive in the details of particular algorithms than might be desired. I did skip part II of the book so that might be an issue. A great resource for cutting your teeth on data mining.
37850
It's a timeless question that nearly everyone asks at some point.  This is a good book on spotting and predicting trends which is a large part of the answer.  True innovators can predict where a segment is headed and exploit it at the right time.  I like the layout and design, especially the tabbed markers for different trend areas.  The information in this book can help sharpen skills for locating and identifying potential opportunities ripe for exploit or disruption. One nice touch is that Bhargava lists all his prior non-obvious works by publication year and includes a summary and recap of the trends along with a graded longevity rating.  It allows the reader to see just how accurate the predictions were for continuity of the trends. I enjoyed reading this and look forward to reading Bhargava's future Non-Obvious books.
4564522
This book has a whole section with case studies involving instances where businesses used big data to their advantage, which I found interesting. It's interesting how tweets may be used to make predictions in the stock market. It is a very informative and fascinating topic. It's a good beginners book (it is, after all, in the for dummies series) for people interested in big data and systems engineering topics.
998471
I appreciate the straight news, not any liberal rag that doesn't inform. The Wall Street journal satisfies the need for honest news. The opinion page is great.
747891
Financial services company The Vanguard Group had just purchased a shiny new search engine to improve search for their 12,000 employees. There was only one problem: the search results were worse than what they had before.

John Ferrara, an information architect who had helped select the new search platform, blew the whistle, asking the project to be delayed so that relevancy could be improved before the search engine went live. Unfortunately, he failed to make a convincing case to his IT colleagues. Technically, the new platform was running just fine. Besides, the search vendor undoubtedly new more about this kind of stuff than the internal guys.

But not to be deterred, John Ferrara turned to the search logs. He aggregated the most popular search queries from the old platform and, going down the list one by one, measured two metrics for each: relevancy and precision. To measure relevancy, John typed in the query ("company address", for instance), and checked to see how far down the best match appeared from position #1. To measure precision, on the other hand, John looked at the top five results for a given query and marked each as either relevant, nearly relevant, misplaced, or irrelevant. The output from each of those exercises is shown below.

By turning to the data, John was able to convince his colleagues that there was, indeed, a problem. With hands on deck, the team was fortunately able to tune the new search platform before it went live, bringing relevancy and precision up to parity with the old system. Thanks to search analytics, John had saved the team from what could have been a serous blunder.

Lou Rosenfeld opens with this relatable story in his latest book Search Analytics for Your Site: Conversations with Your Customers. The book is a very practical guide on how to exploit query logs to improve your company's search experience. Lou outlines a collection of simple but potent techniques for analyzing search logs, spotting insightful patterns, and putting those insights to use.

As mentioned in the Vanguard story, Lou demonstrates the value of understanding users' most common queries. But he also goes much further. From studying failure situations (which queries lead to zero results), to session data (who searched what when), audience segmentation, and goal-based analysis (using key performance indicators), Search Analytics presents a sweeping collection of techniques for turning search logs into an organizational goldmine.

And if those techniques weren't practical enough already, Lou ends the book with a comprehensive list of tips for using search data to improve your website's search, content, navigation, and metadata.

Search Analytics is well-written, to the point, and does what it says on the tin. The author is authoritative -- he wrote Information Architecture for the World Wide along with Peter Morville -- as well as experienced, having consulted for companies such as PayPal, Caterpillar, Ford, and others. I was able to put into practice techniques I learned within days of first picking up the book. If you in anyway share responsibility for search in your organization, this book is well worth your time. I highly recommend it.
357681
I thought this was a very well written and approachable introduction to using analytics to predict human behavior. It touches on some interesting areas such as ethics and covers a wide range of applications of predictive analytics, with lots of examples and case studies that illustrate how predictive analytics is impacting daily lives already, and some predictions :) for the future.