AI News, New York City: Data Science’s Best Bet for Growth and Opportunity

New York City: Data Science’s Best Bet for Growth and Opportunity

The reason New York is often overlooked as a cornerstone of the data science community is because almost all the city’s data scientists were previously locked within enormous institutions like banks, ad agencies, and major media companies.

Instead of thinking of themselves as a community that could leverage their expertise to transform how data is used, they maintained focus on building specific analytical tools and products for their own particular domains of expertise.

One of the reasons New York’s data community stands out from the rest is that it is the best at recognizing the need for both natural sciences and social sciences to come together to do truly great and innovative data work.

As a diverse group comprised of skeptical academics, social sector employees, and public and municipal organizations, data scientists in NYC deal with the question of how data is really serving people on a daily basis.

They also take the time to step back and ask “Okay, there’s lots of good work happening, but what are the limits?” New York City’s density and diversity support this kind of questioning, by keeping all of us in direct contact with the users and consumers we’re hoping to serve.

If you work at a software firm where you sit by yourself and imagine what your customer needs or wants are, you’ll never be as successful as you could if you’re able to walk down the street from your office and talk directly to your customers.

The community’s diversity will allow them to combine methodologies that work well from fields with more established pathways like software engineering, scientific research careers, or product management, and mesh them with less obvious systems modeled after those in finance, healthcare, design, and media.

The E.P.A. Says It Wants Research Transparency. Scientists See an Attack on Science.

In that study, which began in the mid-1970s, scientists signed confidentiality agreements so they could track the private medical and occupational histories of more than 22,000 individuals in six cities around the country.

Academics aren’t typically required to turn over such private data when submitting studies for peer review by other specialists in the field, or for review and publication in scientific journals, which is the traditional way that this kind of research is evaluated.

According to data compiled by the Center for Responsive Politics, a nonpartisan research group that tracks campaign finance data, versions of the bill have received support from Exxon Mobil, Peabody Energy, Koch Industries and the American Chemistry Council, which provides policy and research for major chemical companies including Arkema, DuPont and Monsanto.

Data for Good: Bloomberg supports data scientists’ work with nonprofits and municipalities to solve real-world problems

Yuan’s research focuses on informatics and data science for urban development integrating data analytics, physical computing, and system engineering.

His recent work deals with visualizing high-dimensional, multivariate data into lower dimensions using genetic algorithms.

He also worked as a research intern at Indian Institute of Technology, Delhi where he tried to predict solar radiation to ensure power supply-demand equilibrium.

He has been involved in data science projects ranging from predicting the occupancy of homes in Detroit, to predicting lot level solid waste generation in New York City, to classifying bike lane and street quality using accelerometer and imagery data collected from cell phones.

His current mission consists of analyzing and monitoring log data to better understand user behaviors as well as setting up new data pipelines to allow for producing new metrics and indicators to be exploited.

In his free time, he likes to dig into specific fields of machine learning to broaden his knowledge, knowing that it will eventually be useful for future challenges.

More specifically, he is applying machine learning techniques to electronic medical record data for a variety of tasks.

His research in New Zealand focused on mathematical modeling of respiratory dynamics for critical care patients supported by invasive mechanical ventilation.

He focuses on applying Deep Learning methods towards heterogenous data to derive useful recommendations and support decision making.

Currently he focuses on connecting behavioral science and technology to study and improve human behavior in the areas of productivity and health.

You’re a part of data science history.

The goal of this initiative is to develop a community-driven code of ethics for data collection, sharing and utilization that provides people in the data science community a standard set of easily digestible, recognizable principles for guiding their behaviors.

Rather, these principles will provide academia, industry, and individual data scientists a common set of guidelines for driving the development of standards, curriculums, and best practices for the ethical use and sharing of data, ultimately advancing the responsible and ethical use of data as a collective force for good.

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