Artificial Intelligence Coming To NYU
IBM Watson Partners with NYU Learn More

NYU To Collaborate On Multi-Million Dollar Data Science And Big Data Initiative
Learn More

A University-Wide Initiative
NYU emphasizes data science across schools and research centers. What's this about?

Center for Data Science
A new research center dedicated to advancing the field of data science. Learn More

Applied Statistics
Center for the Promotion of Research Involving Innovative Statistical Methodology (PRIISM) Learn More

Big Data. Big Cities.
Center for Urban Science and Progess Learn More

  • Turning data into insight.

    Our networked world is generating a deluge of data that no human, or group of humans, can process fast enough. This data deluge has the potential to transform the way business, government, science and healthcare are carried out. The emerging discipline of data science holds the key to unlocking that potential. It uses automated methods to analyze massive amounts of data and extract knowledge from them. Data science combines aspects of computer science, applied mathematics and statistics.


    Big data monitors 12 terabytes of tweets each day to improve product sentiment analysis.

    “Data science will also revolutionize medicine, education, and other areas that are slated to become ‘evidence-based.’” —NYU’s Data Science and Statistics Working Group

  • From healthcare to business to government.

    Data science is already being put to use in nearly every sphere of modern life. The transportation industry is using it to better predict flight arrival times. Online retailers are using it to better understand consumer habits. Managers are using it to make decisions based on facts rather than instincts. But data science is in its infancy. As it matures, data science holds the promise of creating drugs customized for individual patients, making government run more efficiently and helping our cities be healthier places to live.


    By 2010, 28% of the digital universe required some level of data network security.

    “Robust, unbiased data are the first step toward addressing our long-term economic needs and key policy priorities.”—Peter Orszag, Former Director, White House Office of Management and Budget

  • Process_AcquireParse_Color

    Acquire & Parse

    Sensors, medical devices, the Web and smartphones — to name just a few sources — capture structured and unstructured data in massive amounts in real-time, all the time.


    Filter & Mine

    Data scientists use mathematics, statistics, data mining and other automated methods to “clean” data. They also advance the field by developing new automated tools and methodologies.


    Analyze & Refine

    With an explorer’s desire to uncover what others might not see, data scientists analyze data. Their insights are used in sectors ranging from biology to finance to government.



    Data scientists excel in presenting complicated findings for experts and non-experts alike. Information visualization, graphic design and human computer interaction all come into play.

  • Where will the data revolution take us?

    Data science has already started to revolutionize most areas of intellectual endeavor found at NYU and in the world. We believe this revolution is just beginning. Data science can help us answer not only the great questions and challenges of our time, but also centuries-old questions. Through data science we may improve our understanding of how the brain works, for example. With that understanding we can build intelligent machines capable of advancing life-saving medical research. Data science can help us realize the promises of the digital age.

Unlocking Big Data’s Potential

From government, social networks and ecommerce sites to sensors, smart meters and mobile networks, data is being collected at an unprecedented speed and scale. Data science can put big data to use.


Average number of “likes” and “comments” posted on facebook daily.


Percentage of the world’s data that has been produced in the last two years.


Projected volume of
e-commerce transactions in 2016.


Recent News

  • Ethics in Data Collection

    As data collection and data analysis have become entrenched in our daily lives, the conversations surrounding the ethical treatment and usage of data are becoming increasingly important.  Almost every interaction you have—talking with a friend on social media, information you give to your doctor, or a purchase you make online—can be translated into and analyzed […]

    Read the rest
  • Money, Machines, and Markets with Vasant Dhar

    The financial markets—the stock exchange, bonds, commodities—are a historically high-risk, high-reward avenue towards making, or losing, money.  Besides working as a television weatherman, investment banking is one of the few fields where you only have to be correct sixty percent of the time to be considered a smashing success.  Investment decisions are generally gauged against the S&P […]

    Read the rest
  • Faculty Interview: Sam Bowman

    Sam Bowman is one of the leading researchers in the field of natural language processing (NLP), and recently joined NYU as an Assistant Professor in Computational Linguistics, a joint position between NYU’s Linguistics department, and the Center for Data Science.  This fall, he will be teaching a course titled “Seminar in Semantics: Artificial Neural Networks.”  […]

    Read the rest
  • The “Unknown Unknowns” of Machine Learning

    One of the struggles in the field of data science is striking the necessary balance between human decision making, and automated computer processing.  The field of machine learning—which looks to create computing systems that can solve problems on their own—is a perfect example of where human intelligence and mechanical computing power must go hand in […]

    Read the rest