NYU Data Science newsletter – July 8, 2016

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There’s actually an extensive decision-making process behind even some of the most mundane-seeming aspects of a fast food company’s business … Thanks to software testing and data analytics, fast food chains can test the financial impact of a menu item before it rolls out nationwide.

More on data & design:

A framework for evaluating electronic health record vendor user-centered design and usability testing processes (July 03, Journal of the American Medical Informatics Association; Raj M Ratwani et al)

Mike Kuniavsky on the mindshift needed to design for ecosystems (July 07, O’Reilly Radar, Mary Treseler)

Ambient Computing by Mike Barlow (July 08, O’Reilly Media, Mike Barlow)

Data Visualization, Design and Information Munging (November 2015, Martin Krzywinski/ Genome Sciences Center)

There is a strong need to conduct a thorough apple-to-apple comparison of sentiment analysis methods, as they are used in practice, across multiple datasets originated from different data sources. Such a comparison is key for understanding the potential limitations, advantages, and disadvantages of popular methods. This article aims at filling this gap by presenting a benchmark comparison of twenty-four popular sentiment analysis methods. [full text]

Networks are a fundamental tool for understanding and modeling complex systems in physics, biology, neuroscience, engineering, and social science. Many networks are known to exhibit rich, lower-order connectivity patterns that can be captured at the level of individual nodes and edges. However, higher-order organization of complex networks—at the level of small network subgraphs—remains largely unknown. Here, we develop a generalized framework for clustering networks on the basis of higher-order connectivity patterns.

Big Data is Big News, a Big Deal, and Big Business, but what is it, really? What does Big Data even mean? To those in the thick of it, Big Data is obvious and I’m stupid for even asking the question. But those in the thick of Big Data find most people stupid, don’t you? So just for a moment I’ll speak to those readers who are, like me, not in the thick of Big Data. What does it mean?

Google could be holding as much as 15 exabytes on their servers. That’s 15 million terrabytes of data which would be the equivalent of 30 million personal computers. Storing and managing such a humongous volume of data is no easy task and how Google handles this data is a lesson for anybody who deals with cloud and big data.

Currently, there are few resources for electronic health record (EHR) purchasers and end users to understand the usability processes employed by EHR vendors during product design and development. We developed a framework, based on human factors literature and industry standards, to systematically evaluate the user-centered design processes and usability testing methods used by EHR vendors.

Farmers are adopting smart-sensor technologies and connected farm equipment more quickly than ever, stretching each season to eke out greater yield from finite acreage.

Thirty years on from the first congress on peer review, Drummond Rennie reflects on the improvements brought about by research into the process — and calls for more.

In the Intelligent World Hackathon you can be one of the first developers to access GE smart LED streetlight network data and build urban apps on Predix, GE’s new IIoT data analytics platform.

The hackathon runs until Tuesday, August 2.

What is a data scientist? The question is seemingly simple, and yet deceptively complex. A data scientist obviously works with data, but in what capacity? Is a data scientist more concerned with the collection of data, or data analysis? And what is the end goal for a data scientist? With data available on everything from digital advertisements to online purchases to personal health records, the breadth of fields requiring data scientists makes the definition even more elusive.

Last Tuesday, an organization called the New York Data Science Study group coordinated a data science career panel talk, titled, “What Does a Data Scientist Do?”

starting with … Pipeline frameworks & libraries

ActionChain – A workflow system for simple linear success/failure workflows.

Airflow – Python-based workflow system created by AirBnb.

Anduril – Component-based workflow framework for scientific data analysis.

How will simple beacons broadcast information to your phone as you pass businesses on your morning walk? How can emotional speech analysis monitor the emotional state of employees, students, or people in crowds? Pick up this report and find out.

Evan Sparks describes the principles behind KeystoneML and introduces its programming model by way of example pipelines in NLP and image classification. [video, 49:29]