NYU Data Science newsletter – August 20, 2015

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As companies deal with anxieties about potential unknowable and uncontrollable factors in decision-making, the nature of data partnerships compel them to ask the most basic of all questions: what data science are we in?

Beneath the surface of most data science initiatives, there are three kinds of knowledge-intensive businesses?—?a customer or function augmentation business, a modeling or feature innovation business and an infrastructure or workflow automation business. Although, the underlying variables are intertwined, these businesses differ a great deal.

At Alpha Software I have been working as CTO exploring the ways in which mobile devices, including tablets like the iPad, can help businesses improve their internal processes. Rather than concentrate on consumer use, a major driver of tablet software design at Apple and elsewhere, I have been looking at the needs of businesses.

What I want to cover here is the area that has been served by paper forms. They are just one example of a means for addressing the needs of an organization, and, I believe, a means this is not always as good as what came before or as what we can now efficiently do after.

Emerging data suggests the seemingly impossible — that mysterious new genes arise from “junk” DNA.

In San Diego, a conflict is brewing over a proposal to build a dense residential development in a rural part of the county. The developer has permission to build 110 homes, but has requested to build 1,706 homes.

The conflict pits the county’s goals for healthier urban growth—concentrating development to cut commute times, reduce greenhouse gas emissions and lower wildfire risks—against a proposal for LEED-certified buildings and a water recycling plant to service the new development.

The state of our science is strong, but it’s plagued by a universal problem: Science is hard — really fucking hard.

If we’re going to rely on science as a means for reaching the truth — and it’s still the best tool we have — it’s important that we understand and respect just how difficult it is to get a rigorous result. I could pontificate about all the reasons why science is arduous, but instead I’m going to let you experience one of them for yourself. Welcome to the wild world of p-hacking.

I heard about a new technology that helps power Post Recommends, Clavis, and set out to learn how it works.

In a nutshell, Clavis is technology that figures out what stories are about, categorizes them by topic, and assigns each a series of keywords. It runs that same process on the Post’s readers and identifies their presumed interests based on stories they’ve read. Clavis then pairs readers with stories that match their reading history.

Amazon’s practices & tools make it one of the most important companies in the world of data science. Given this, unless we have been off the grid over the past few days, it is almost impossible to ignore the tech & engineering community discussion about the New York Times article on the brutal culture at Amazon. The article shares anecdotes of employees being expected to work long hours with a brutally frank atmosphere & how unhappy employees are. Defenders of Amazon have called it a biased viewpoint based on anecdotes from dissatisfied ex-employees. Silicon Valley luminaries have come down largely in favor of Amazon saying that this is normal for high growth innovative companies. Instead of opinions, perspectives & finger pointing, why don’t we use data to shed more light on the situation.

If Michael Stonebraker had a coat of arms, it would have “Make it Happen” as a motto and “Make a Difference” as a slogan (or battle cry), arranged above and below a three-dimensional web of ones and zeros.

For more than four decades, Stonebraker has been engaged in a battle to tame the digital data explosion and turn it into knowledge. Along the way, he has started 9 companies and made an indelible mark on academic research in the fields of databases and data management, serving as a member of the computer science faculty first at UC Berkeley and, since 2001, at MIT.

Enrollments are open for Data Visualization with D3.js.

Designed and taught by Kevin Quealy, Graphics Editor for the New York Times, this course is for anyone who wants to be proficient in the use of D3 and seeks expertise visualizing quantitative information. You’ll learn to tell stories and communicate information interactively in ways that are simply not possible outside a web browser.

Deadline to enroll is (probably) before the start of the course on Wednesday, September 16. The 6-week course is held on Monday and Wednesday evenings from 6:30 – 9:30pm at Metis, 27 East 28th Street, New York City, and costs $2500.

The research into developing learning algorithms combining these components and the analysis of those algorithms is still in its infancy. The purpose of this workshop is to bring together researchers from diverse backgrounds to exchange ideas which could lead to addressing the various drawbacks associated with such models leading to more interesting models in the quest for moving towards true AI.

Deadline for Submissions: Friday, October 9

This talk by Andreas Mueller presents several methods for learning non-linear models on a single machine, where the dataset does not fit into ram. It will cover the hashing trick, kernel approximations, neural networks, and extreme learning machines (random neural networks).