NYU Data Science newsletter – August 25, 2015

NYU Data Science Newsletter features journalism, research papers, events, tools/software, and jobs for August 25, 2015

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Data Science News



Cohort Analysis with Python

Greg Reda


from August 23, 2015

Despite having done it countless times, I regularly forget how to build a cohort analysis with Python and pandas. I’ve decided it’s a good idea to finally write it out – step by step – so I can refer back to this post later on. Hopefully others find it useful as well.

I’ll start by walking through what cohort analysis is and why it’s commonly used in startups and other growth businesses. Then, we’ll create one from a standard purchase dataset.

 

Python and Machine Learning

Sebastian Raschka


from August 24, 2015

… When I quantify “productivity,” I literally estimate the sum of (1) the time that it takes to get the idea written down in code, (2) debug it, and (3) execute it. To me, “most productively” means “how long does it take to get the results?” Now, over the years, I figured that Python is for me. Not always, but very often. Like everything else in life, Python is not a “silver bullet,” it’s not the “best” solution to every problem. However, it comes pretty close if you compare programming languages across the spectrum of common and not-so common problem tasks; Python is probably the most versatile and capable all-rounder.

 

AAAS webinar – Careers in data science

AAAS webinar


from August 26, 2015

Companies of all sizes and all sectors today are scrambling to make sense of mountains of incoming data. And they’re turning to data scientists to crunch those numbers and identify trends and relationships.

STEM professionals with strong quantitative and qualitative research skills are highly sought after by firms looking to build or grow their data-science teams.

But what exactly does a data scientist/analyst do? And what hard and soft skills do STEM professionals need to get hired?

 

Why You’re Biased About Being Biased – Facts So Romantic – Nautilus

Nautilus


from August 19, 2015

In a classic experiment in 1953, students spent an hour doing repetitive, monotonous tasks, such as rotating square pegs a quarter turn, again and again. Then the experimenters asked the students to persuade someone else that this mind-numbing experience was in fact interesting. Some students got $1 ($9 today) to tell this fib while others got $20 ($176 today). In a survey at the end of the experiment, those paid only a trivial fee were more likely to describe the boring activity as engaging. They seemed to have persuaded themselves of their own lie.

According to the researchers, psychologists Merrill Carlsmith and Leon Festinger, this attitude shift was caused by “cognitive dissonance,” the discomfort we feel when we try to hold two contradictory ideas or beliefs at the same time. When faced with two opposing realities (“This is boring” and “I told someone it was interesting”), the well-paid students could externally justify their behavior (“I was paid to say that”). The poorly paid students, on the other hand, had to create an internal justification (“I must have said it was interesting for some good reason. Maybe I actually liked it”).

 

The Science Of Love In The 21st Century

The Huffington Post, Highline magazine


from August 19, 2015

… Over decades, John [Gottman] has observed more than 3,000 couples longitudinally, discovering patterns of argument and subtle behaviors that can predict whether a couple would be happily partnered years later or unhappy or divorced. He has won awards from the National Institute of Mental Health and the National Council of Family Relations and has become the subject of increasing public fascination. He went on Oprah and the “Today” show. A book he co-authored that summarizes his findings, Seven Principles for Making Marriage Work, is a New York Times best-seller.

His work took off because the consistency of his predictions is astonishing. One 1992 experiment found that certain indicators in how couples talked about their relationship could forecast–with 94 percent accuracy–which pairs would stay together.

 

Budding UW Data Scientists Use Their Powers for Social Good

Xconomy


from August 24, 2015

Earn a degree in the field of data science these days and your ticket is punched: Google, Amazon, Facebook, leading-edge academic research, a well-funded startup—they’re all clamoring for people proficient in the tools and techniques needed to sift through today’s endless streams of digital data in search of something valuable.

Social service organizations and local governments are confronting the data deluge, too, often without the capacity to pay the salaries that profit-driven companies can offer these sought-after experts.

Enter the University of Washington’s just-concluded Data Science for Social Good summer internship.

 

The Internet has allowed scientists instantaneous access to massive amounts of chemical data

C&EN, How the Internet Changed Chemistry


from August 17, 2015

Credit for the rise of databases—collections of data organized for rapid search and retrieval—rightly goes to the advent of computers. But access to those databases was limited and slow in the early years, requiring researchers to wait for punch cards or magnetic tape to arrive in the mail. What the Internet allowed was for thousands of users to instantaneously access up-to-the-minute information.

Over the past 50 years, “I can’t think of a single change that’s been as dramatic as the Internet in enabling the scientific community to share its data,” says Paul Davie, general manager of the U.S. operations of the Cambridge Crystallographic Data Centre, which manages the Cambridge Structural Database (CSD).

 

Big Data Is Teaching Us About the Nighttime Migrations of Birds, by M.R. O’Connor

Nautilus


from August 20, 2015

In Ithaca, New York, a virtual machine in a laboratory at the Cornell Lab of Ornithology sits in the night, humming. The machine’s name is Bubo, after the genus for horned owls. About every five minutes, Bubo grabs an image from Northeast weather radar stations, and feeds it through a pipeline of artificial-intelligence algorithms. What does this radar image show me? Bubo asks. Is it rain? Are these insects? Could it be pollen? Bubo doesn’t care about those things; all it wants to see are birds in flight. To find them, Bubo analyzes the velocity and direction of targets seen by the radar station. Bubo knows birds have a velocity different from wind and insects, and filters those out. Now Bubo sees only birds. But how dense are they? How fast are they going? How high in the sky are they flying? The machine makes these calculations and creates an image of countless birds in flight, traveling under cover of darkness.

 

What Can I Do With “Deep Learning”? – YouTube

YouTube, NextDayVideo


from August 21, 2015

“Deep learning” is a recent rebranding and mixing of old and new methods in neural networks, graphical modeling, and optimization. We will discuss the applications of these approaches, how these methods are different than others for machine learning, and what recent advances in the field mean for people trying to solve problems in the real world.

 

How NVIDIA Is Unlocking the Potential of GPU-Powered Deep Learning

datanami


from August 20, 2015

Companies across nearly all industries are exploring how to use GPU-powered deep learning to extract insights from big data. From self-driving cars and voice-directed phones to disease-detecting mirrors and high-speed securities trading, the potential use cases for the technology are large and expanding by the day.

Ever since computer scientist Geoff Hinton decided to try training a neural network on a GPU and essentially invented the field of deep neural networks several years back, researchers have been racing to apply the technique to tough modeling problems in the real world.

Hinton, who splits his time between Google and the University of Toronto, gets credit for inventing this field, but GPU-maker NVIDIA is more than happy to pick up the reins and run with it. The company has years of experience applying GPUs in the high performance computing (HPC), gaming, and high-end graphics spaces.

 

Is mass authorship destroying the credibility of papers?

Times Higher Education, UK


from August 24, 2015

The rise in ‘kilo-authors’ and ‘gift authorship’ is causing the academy to rethink how it assesses the worth of academic publications.

 

Why the Stock Market Is So Turbulent

The New York Times, TheUpshot blog


from August 24, 2015

… What’s fascinating is that there is no clear, simple story about what is different about the outlook now for interest rates, for United States and European corporate profits or for economic growth compared with one week ago, when the S.&P. 500 index was 10 percent higher.

Here’s how to make sense of what is a truly global story, stretching from the streets of Shanghai, where stock investing has become a middle-class sport in recent years, to the oil fields of both the Middle East and Middle America, to the hallways of power in the Federal Reserve in Washington.

 

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