 55scala,
 44java,
 36Eclipse,
 35development,
 33maven,
 28javascript,
 24dataanalysis,
 21osgi,
 19python,
 17angularjs
"A few months ago, I had the privilege of being one of the judges at the LAUNCH Hackathon hosted by Expedia, CapitalOne Labs, and BitPay. Over 800 developers and designers participated and over $2 million in prizes were given away. In addition to the grand prizes for best overall product (a $100,000 investment from the LAUNCH Fund), Expedia was a premier sponsor, giving away prizes to the top 5 teams who used our API in an interesting way."

The Oasis team used our Points of Interest API to build an exciting app in the discovery space. The app recommends points of interest to visit on a road trip. If a traveler accepts or ignores this feedback, it is used to improve suggestions for future travellers. We found the app to be creative, fun, and an excellent use of our API!

Serendipit.us tied Facebook and travel itineraries together to let travelers meet up with other people who were traveling to the same destination as you during overlapping days. We thought it was an interesting take on integrating social and travel.

Travel Nanny built a mobile app to augment your itinerary with information you might need. They used a third party API to parse your itinerary, then added additional information like what visas you need for the trip. We liked that they were solving a real problem for a lot of our customers and it made us interested to find out more about the visa information service they were consuming.

 Building enabled and control groups is an art
 Granularity of our data is per week with Omniture
 We're new at this
"The nullhypothesis of this test is that the population is normally distributed. Thus if the pvalue is less than the chosen alpha level, then the null hypothesis is rejected and there is evidence that the data tested are not from a normally distributed population. In other words, the data are not normal. On the contrary, if the pvalue is greater than the chosen alpha level, then the null hypothesis that the data came from a normally distributed population cannot be rejected. E.g. for an alpha level of 0.05, a data set with a pvalue of 0.02 rejects the null hypothesis that the data are from a normally distributed population.[2] However, since the test is biased by sample size,[3] the test may be statistically significant from a normal distribution in any large samples. Thus a Q–Q plot is required for verification in addition to the test."
"The P value is used all over statistics, from ttests to regression analysis. Everyone knows that you use P values to determine statistical significance in a hypothesis test. In fact, P values often determine what studies get published and what projects get funding.
Despite being so important, the P value is a slippery concept that people often interpret incorrectly. How do you interpret P values?
In this post, I'll help you to understand P values in a more intuitive way and to avoid a very common misinterpretation that can cost you money and credibility."
"The ShapiroWilk test, proposed in 1965, calculates a W statistic that tests whether a random sample, x1,x2,…,xn comes from (specifically) a normal distribution ."
"KolmogorovSmirnov Test
A goodnessoffit test for any statistical distribution. The test relies on the fact that the value of the sample cumulative density function is asymptotically normally distributed."
Montreal's bylaw concerning traffic and parking
"What is ngInfiniteScroll?
ngInfiniteScroll is a directive that you can use to implement infinite scrolling in your AngularJS applications. Simply declare which function to call when the user gets close to the bottom of the content with the directive and the module will take care of the rest. Of course, you can specify several options to ensure that the behavior is just what you're looking for."
"The Applicative Builder operator @ takes Validations (and other Applicative Functors) and a handler that accepts the combined result as a tuple. In the case of Validations, the hander is called if all inputs are Successes. If any of the inputs is a Failure, the handler is not called, while @ accumulates the Failures for you."
Awesome data analysis on the rising trend of emojis on Instagram. Worth to read just for the used techniques (but the conclusions are quite normal, but backed with data though).
"In Part 1 of this blog post series, we will take a deep dive into emoji usage on Instagram. By applying machine learning and natural language processing techniques, we’ll discover the hidden semantics of emoji."
"tDistributed Stochastic Neighbor Embedding (tSNE) is a (prizewinning) technique for dimensionality reduction that is particularly well suited for the visualization of highdimensional datasets. The technique can be implemented via BarnesHut approximations, allowing it to be applied on large realworld datasets. We applied it on data sets with up to 30 million examples. "
"Today, I’m going to explain in plain English the top 10 most influential data mining algorithms as voted on by 3 separate panels in this survey paper.
Once you know what they are, how they work, what they do and where you can find them, my hope is you’ll have this blog post as a springboard to learn even more about data mining."
"The big problem, which has revealed itself to me over the course of the past year, as I've watched numerous programmers struggle with the PouchDB API and other promiseheavy APIs, is this:
Many of us are using promises without really understanding them."
"The subject of this trip is Validation: how to represent a computation which can either give a result, or a failure message. Or rather: how to represent a sequence of computations (each of which may fail) giving either a complete result or else a list of the failure messages."
"One of the more common things you run into during software development is the need to parse arbitrary text for data. Typically, you might use regular expressions, or encode assumptions about the data format in the way you parse the text (think slicing a string at specific indices, splitting on commas, etc). Both of these are brittle, and require a lot of verbose code to properly handle all of the possible failure points. This might lead you to writing your own parser if you are committed enough – but this is a large undertaking for most developers. You have to learn how to write a parser, or learn a parser generator in order to even begin coding the solution to your particular use case. Scala has a fantastic solution to this problem however, and that solution is parser combinators."
"An intro to Bayesian methods and probabilistic programming from a computation/understandingfirst, mathematicssecond point of view."
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