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Jordan Tigani
Works at Google
Attended Harvard University
Lives in Seattle, WA
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Jordan Tigani

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My talk from the BigQuery meetup in Stockholm is now public. Mostly it is an introduction, but has some cool demos.
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+Jordan Tigani Particularly, the online demo that scanned 4 PB in 7 seconds - quite an eye-opener.
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For anyone in the Seattle area that likes big data and cannot lie, Queen Anne Book Co is graciously hosting a launch party for our book. No purchase necessary, just come, have a glass of wine and a big data themed snack in a great local bookstore.

Since this is the first time +Siddartha Naidu and I have been in the same city since the book came out, we might have to do rap in SQL-92 to celebrate.
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do u have any photos of those snacks? : )
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Here are my predictions for the quarterfinals:
Brazil (71%) vs. Colombia (29%)
France (69%) vs. Germany (31%)
Netherlands (68%) vs. Costa Rica (32%)
Argentina (81%) vs. Belgium (19%)

Three of these are pretty uncontroversial... however picking France over Germany is probably the biggest surprise. This may be due to France having a softer route in the group phase than Germany, but also it looks like France has been more consistently solid than Germany. France was only scored on in one game, and that was in a 5-2 drubbing of Switzerland. They also won all of their games outright. Germany ended up drawing with Ghana and had the Algeria game been only 90 minutes long, that would have been a draw as well.

Lastly, I want to say that these predictions are just for fun; they were something that I worked on in spare time with the goal of demonstrating Machine Learning techniques for our I/O talk. I've been pleasantly surprised that the predictions were at least reasonable; that they did so well on the round of 16 was a lot a big dose of luck.
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do you mind to have a chat ..
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Jordan Tigani

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+Felipe Hoffa and I are going to be giving a talk at Google I/O on using machine learning with the Google Cloud Platform to make predictions, specifically to figure out who is going to win the World Cup. Check out the live stream (or attend in person, if you're going to I/O) if you're interested.
Can you predict the future using Big Data? Can you divine if your users will come back to your site or where the next social conflict will arise? And most importantly, can Brazil be defeated at soccer on their own turf? In this talk, we\u0027ll go through the process of data extraction, modelling and prediction as well as generating a live dashboard to visualize the results. We’ll demonstrate how you can use Google Cloud and Open Source technolog...
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My book "Google BigQuery Analytics" is now available on Google Play and wherever fine Electronic Books are sold (I hear that there is a company that is named after a south american river that also has it, but as the spouse of an independent bookseller, I cannot bring myself to link to it.)

The dead tree version should be available in a couple of weeks (June 9th, as far as I know) for anyone who needs a monitor stand. 
<b>How to effectively use BigQuery, avoid common mistakes, and execute sophisticated queries against large datasets</b><p><i>Google BigQuery Analytics</i> is the perfect guide...
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I read the first chapter and it reads like a fascinating narrative!
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Great blog post by +Felipe Hoffa about new features in BigQuery. 

Amidst all of the hoopla around pricing changes and streaming, there some of the less photogenic features have been overlooked. Felipe's post highlights a lot of the interesting changes.

I'd like to mention one in particular, however--- table views.  BigQuery has taken a number of small steps in the past couple of quarters to make it look more like a traditional database (albeit a massive one). From allowing joins of any size tables, to SQL improvements that inch closer to standards-compliant SQL, to windowing and analytic functions, BigQuery is adding features that make it easier to translate your relational database knowledge into BigQuery queries.

Table Views are familiar to people who use relational databases, and allow you to have a standing query that you treat like a normal table. You can visualize it in Tableau,  you can join it against another table, or you can materialize it as a concrete table. When the data underlying the view changes, the data in the view changes. Views can also simplify your queries, since you can take complex inner queries and save them as a view, so you can refer to them in a more straightforward way in your outer query.
 
Updates to Google BigQuery following Cloud Platform Live
/by +Felipe Hoffa  #cloud   #cloudplatform   #bigquery  

Last Tuesday, we announced an exciting set of changes to Google BigQuery making your experience easier, faster and more powerful. In addition to new features and improvements like table wildcard functions, views, and parallel exports, BigQuery now features increased streaming capacity, lower pricing, and more. 
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Jordan Tigani

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Interview with BIME Analytics. I suppose a little bit of hyperbole never hurts.
An interview with Jordan Tigani, founding member of the Google BigQuery team about everything Big Data at Google. Everything from concept development, the Dremel breakthrough, the night when BigQuery emerged as a brilliant idea and future product, the advantages the technology holds... all described in the first book on Google BigQuery Analytics signed by him and Siddharta Naidu.
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I'm pretty excited about this post. Not because we've done so well on the predictions (more on that in a minute), but because we've been able to turn the predictions over to anyone who wants to give them a try.

When +Felipe Hoffa and I  first started working on the Google I/O talk that was the background for this effort, we did so because we wanted to demystify machine learning. Many developers and technologists think that ML is 'hard' and so don't think about all of the ways that it can work for them. However, between the open source tools that are available and Google's cloud, it is now pretty easy to do a lot of things that look 'hard'. We applied those tools to something that both of us are passionate about (soccer) and used them to make some predictions.

So now we've packaged up the models that we've built in a way that anyone can see exactly what we did and try them out themselves.  If you have an idea, it should be easy to incorporate in your model. There is lots of room for improvement.

It turns out that it is pretty easy to set this up -- the detailed instructions are in the post: just cut and paste a couple of command lines (you don't even need to modify them), then navigate to the iPython notebook in your web browser. That's all you need to do to start making predictions of your own.

And about that prediction accuracy. We've gone 13 for 14 so far, but I'd like to call out that this is a lot of luck. Soccer is just not predictable at that level of accuracy.  This world cup knockout stage was particularly surprising in that the favorites all won (so far). (See Nate Silver's take on it here: http://fivethirtyeight.com/datalab/its-a-huge-upset-when-all-the-world-cup-favorites-win/).  So please don't think that these models are some magic oracle that will tell you who will win upwards of 90% of the time. At best, they'll tell you who should win. Or who would win more often if the game was played under the same conditions 100 times.

So give this a try and start predicting. I'd be happy to hear your results.
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well i could like to know more about this and also get to know you more if you don't mind ..
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Our talk on using machine learning to predict soccer outcomes managed to predict all of the round of 16 games correctly in the world cup.

This wasn't our goal; we just wanted to demonstrate different approaches to machine learning with Google's Cloud. But it is still nice to be right. 

I've been tweaking the models slightly and have predictions for the next round. However, this is where it starts to get a little bit since you don't have many obvious mismatches to get "for free". And our luck has got to run out sometime :-)
 
Machine learning and soccer: We predicted the results for 8 matches, we just got the 8 right! +Jordan Tigani and me at Google I/O 2014: Google I/O 2014 - Predicting the future with the Google Cloud Platform
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Cool new dataset hosted in BigQuery.  +Ilya Grigorik ... any chance of adding this GDELT as a tag in bigqueri.es? My guess is that there are a lot of queries on this data that might interesting to a broad audience.
 
Summary: The Global Database of Events, Languages, and Tones is a growing trove of information about meaningful events that have happened across the world in the past three decades. Now, it’s available to the public to access and analyze using Google’s cloud computing services.
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This article gets hiring exactly backwards. The point of interviewing (or companies) is not to hire all of the qualified people -- it is to hire none of the unqualified ones.

In the article, they mention that a good interviewer can pick 8 of 10 of the qualified candidates in a pool of 100. They use this to state that more interviews are worse, because the other interviewer wouldn't agree on the same people, and would lead you to miss some of the qualified candidates.

However, an interviewer isn't always going to make the right decision; just as they can clearly miss a qualified candidate, they can also pick an unqualified one. Let's say if they interview all 100 people, they say yes to 2 of the unqualified people. That is a pretty good accuracy rate, since of the 90 unqualified people they only pick 2 (a 2.2% false positive rate).

But this means that for the 10 people they say yes to 20%, or 2 of them are unqualified. That is really bad for the company, if 20% of the workforce is unqualified.

However, if you have 2 interviewers and required consensus, then you'd still get 6 qualified candidates, on average, but the chances that any given hire was unqualified goes down from 20% to 4%. 

I think most companies would want to reduce this even further, since a 4% false positive rate means that one out of 20 hires is going to be unqualified. Hiring the wrong person is expensive (since they won't be as productive as someone more qualified, and you might need to eventually fire them, which is also expensive) and it can lead to lower standards in the future (as in steve jobs' quote "As hire As, Bs hire Cs").
Vox.com
American employers might be getting many aspects of the job interview all wrong, from the questions asked to the number of interviewers. The best approach is to keep it simple, and have one person who's good at interviewing do a single interview.
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An open letter from outgoing president of the Authors guild who is worried that technology is endangering the ability of authors to make a living. While he may have a some valid points, when you worry about the threat of "militant librarians", you're going to lose a lot of your sympathy.

Sounds like the Authors guild will be better off without him, and hopefully his replacement will be someone who can figure out how to engage technological change rather than just rail against it. 

"Not everyone believes, as I do, that the writing life is endangered by the downward pressure of e-book pricing, by the relentless, ongoing erosion of copyright protection, by the scorched-earth capitalism of companies like Google and Amazon, by spineless publishers who won’t stand up to them, by the “information wants to be free” crowd who believe that art should be cheap or free and treated as a commodity, by internet search engines who are all too happy to direct people to on-line sites that sell pirated (read “stolen”) books, and even by militant librarians who see no reason why they shouldn’t be able to “lend” our e-books without restriction"
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What would be wrong with a system where renumeration is purely voluntary? Surely the recourse of litigation is not a virtue! Suppose that people learn to reward that which they enjoy - or else do without! Would fewer authors be rewarded? In the long run? A brave new world... And is not software also written?
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    Electrical Engineering
  • University of Washington
    Computer Science
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