Data Science and Beyond

NOVEMBER 22, 2015

Contrary to common belief, the hardest part of data science isn’t building an accurate model or obtaining good, clean data. It is much harder to define feasible problems and come up with reasonable ways of measuring solutions. This post discusses some examples of these issues and how they can be addressed. The not-so-hard parts Before discussing the hardest parts of data science, it’s worth quickly addressing the two main contenders: model fitting and data collection/cleaning.

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Data Science Data Collection Measurement Modeling 92

The Unofficial Google Data Science Blog

NOVEMBER 19, 2015

by SEAN GERRISH If you are a regular at this blog, thanks for reading. We will continue to bring you posts from the range of data science activities at Google. This post is different. It is for those who are interested enough in our activities to consider joining us. We briefly highlight some of the things we look for in data scientists we hire at Google and give tips on ways to prepare.

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Statistics Data Science Machine Learning Sales 75

Nutanix

NOVEMBER 23, 2015

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Occam's Razor

NOVEMBER 2, 2015

Facebook, at last count, has 1.5 billion monthly active users. YouTube has 1.2 billion users (watching 6 billion hours of videos!). Instagram has an estimated 400 million users. Those are some big gigantic numbers! I believe that every human with time to spare, and a connection to the web, should be on social media. The benefits are numerous. Facebook allows you to stay close to people you choose to.

B2B 167

B2B Metrics Recreation/Entertainment Testing 167

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Data Science and Beyond

NOVEMBER 3, 2015

Bandcamp Recommender (BCRecommender) is a web application that serves music recommendations from Bandcamp. I recently switched BCRecommender’s data store from MongoDB to Elasticsearch. This has made it possible to offer a richer search experience to users at a similar cost. This post describes the migration process and discusses some of the advantages and disadvantages of using Elasticsearch instead of MongoDB.

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IT 52

The Unofficial Google Data Science Blog

NOVEMBER 4, 2015

by OMKAR MURALIDHARAN Many machine learning applications have some kind of regression at their core, so understanding large-scale regression systems is important. But doing this can be hard, for reasons not typically encountered in problems with smaller or less critical regression systems. In this post, we describe the challenges posed by one problem — how to get approximate posteriors — and an approach that we have found useful.

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KDD Testing Machine Learning Measurement 40

Nutanix

NOVEMBER 23, 2015

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Nutanix

NOVEMBER 4, 2015

There’s no question that enterprise application usability has for the most part been left behind.

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Enterprise 20