NYU Data Science newsletter – April 11, 2016

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Sometime at the start of the decade, YouTube was abuzz with viral videos of small children — yet to speak, read or write — “pinching” magazine articles with their fingers as they would an iPad. These children were heralded as members of a new generation of “digital natives”: People who grew up surrounded by computers, shaped by always-on technology and the Internet.

Today we are witnessing a new revolution, this time of “data natives” who expect their world to be “smart” and seamlessly adapt to them and their taste and habits.

According to a recently published report by Grand View Research, the global smart cities market will hit $1.4 trillion in 2020, nearly triple the global market size of $568 billion back in 2013.

The main factors leading to this increase are the rapidly expanding population, speedy urbanization and industrialization. Smart cities are designed and built to cater to a plethora of challenges and issues – including water management, energy management, urban mobility, street lighting and safety of the citizens. Rapid growth is expected to occur in the smart city market, with an estimated annualized 13.6 % rise each year between 2014 and 2020.

Toyota Motor Corp. is expanding its artificial intelligence research to University of Michigan, its third university collaboration in the U.S. to try to advance its efforts in autonomous driving.

Last year Toyota announced a $1 billion effort to expand its research in autonomous driving and has hired professors from Stanford University and Massachusetts Institute of Technology. It also brought on the entire staff of Jaybridge Robotics in Cambridge. The latest partnership includes the hiring of UM researchers Ed Olson and Ryan Eustice, autonomous vehicle experts at the Ann Arbor-based school.

Persuasion is at the core of norm creation, emergence of collective action, and solutions to ‘tragedy of the commons’ problems. In this paper, we show that the directionality of friendship ties affect the extent to which individuals can influence the behavior of each other. Moreover, we find that people are typically poor at perceiving the directionality of their friendship ties and that this can significantly limit their ability to engage in cooperative arrangements. This could lead to failures in establishing compatible norms, acting together, finding compromise solutions, and persuading others to act. We then suggest strategies to overcome this limitation by using two topological characteristics of the perceived friendship network. The findings of this paper have significant consequences for designing interventions that seek to harness social influence for collective action.

The health care field is on the cusp of entering the era of “Moneyball” medicine. Just ask Eric Topol, M.D., director of San Diego-based Scripps Translational Science Institute. He recently hired Paul DePodesta, a data analytics guru who transformed the Oakland Athletics baseball team and now has his algorithms set on doing the same for STSI and health care. And he is not alone. More than 200 data analytics companies are vying for the attention of health care organizations, which are sitting on an untapped trove of data.

With the near universal adoption of electronic health records, large hospitals and health systems have begun to recognize something that consumer retailers have relied on for more than a decade: With the right analytics, data can predict the future and help organizations get out in front of consumer trends.

As with many industries, big data science is transforming the energy vertical, providing insights into cost reductions in down markets and allowing oil producers to adjust to market demands in boom times.

As noted in my last post comparing NYC neighborhood murder rates to American cities, the City has finally released raw crime data for the first time in its history. But the more I look at data released by other cities, the more the data released by NYC seems simply inadequate. I have found over a dozen cities which release raw crime data, but NYC is the only city I have found that has filtered the crime data down to a subset of seven major crimes, (a subset which just so happens to paint the rosier picture for the Administration. Those numbers are down and not controversial.)

Though I continue to call for NYC to stop filtering most crime out of the data set, that won’t stop me from finding value in what has already been released to date. And my question for today’s post: What times of day do major felonies happen?

An hour’s drive from Wichita, Kansas, in a little town called Potwin, there is a 360-acre piece of land with a very big problem.

The plot has been owned by the Vogelman family for more than a hundred years, though the current owner, Joyce Taylor née Vogelman, 82, now rents it out. The acreage is quiet and remote: a farm, a pasture, an old orchard, two barns, some hog shacks and a two-story house. It’s the kind of place you move to if you want to get away from it all. The nearest neighbor is a mile away, and the closest big town has just 13,000 people. It is real, rural America; in fact, it’s a two-hour drive from the exact geographical center of the United States.

But instead of being a place of respite, the people who live on Joyce Taylor’s land find themselves in a technological horror story.

For the last decade, Taylor and her renters have been visited by all kinds of mysterious trouble. They’ve been accused of being identity thieves, spammers, scammers and fraudsters. They’ve gotten visited by FBI agents, federal marshals, IRS collectors, ambulances searching for suicidal veterans, and police officers searching for runaway children. They’ve found people scrounging around in their barn. The renters have been doxxed, their names and addresses posted on the internet by vigilantes. Once, someone left a broken toilet in the driveway as a strange, indefinite threat.

Internet service providers are public utilities. For now, they are also ad agencies.

ISPs such as Comcast, Time Warner Cable, AT&T and Verizon sell access to the pipes you need to surf the Web. In turn, they use data from your online escapades to target advertising at you. Your Internet provider, for example, can use info about the video sites you frequent to offer you a higher-tiered broadband plan. It can also serve hoverboard ads to 20-something males who live in L.A, were it so inclined.

New Federal Communications Commission privacy rules, however, could nip this still-nascent revenue stream in the bud.

As the tech industry gets ever more data-driven, a “Ph.D. in economics” is more often becoming a job requirement in the sector.

Established giants and newer tech firms alike are enlisting economists to help with many crucial tasks. Companies that employ economists include Amazon.com (AMZN), Airbnb, IBM (IBM), Facebook (FB), Microsoft (MSFT), eBay (EBAY), Yahoo (YHOO) and Uber.

At Springboard, we pair mentors with learners in data science. We often get questions about whether to use Python or R – and we’ve come to a conclusion thanks to insight from our community of mentors and learners.

Data science is the sexiest job of the 21st century. Data scientists around the world are presented with exciting problems to solve. Within the complex questions they have to ask, a growing mountain of data rests a set of insights that can change entire industries. In order to get there, data scientists often rely on programming languages and tools.

This is an excerpt of our free, comprehensive guide to getting a job in data science that deals with two of the most common tools in data science, Python and R.

www.deeplearningbook.org

Many thanks to the Google Brain team for encouraging me to devote several months of concentrated effort to this project!

Granular is pleased to announce the release of pyspatial: an open source, BSD-licensed, Python library for simplifying geospatial data science. Here at Granular, geospatial data is an integral part of our software products. Be it our grower’s fields, sensor data from tractors and combines, weather, remote sensing, or soil information. The Granular Data Science Team uses this information to provide analyses and build models to help our growers make real world decisions on the farm.