The field of data science continues to grow, and with it come thought leaders who contribute to the industry through outreach and education. Many of the data science professors teaching today are leaders in the big-data field, speaking at conferences, writing books, and even creating groundbreaking big-data developments themselves. Find out which schools boast the most influential leaders in the data science industry.
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Syracuse University
Syracuse University
Syracuse University's online Master's in Applied Data Science can be completed in as few as 18 months.
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Southern Methodist University
Southern Methodist University
SMU prepares you to manage and analyze large amounts of data and drive strategic change in organizations.
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University of Denver
University of Denver
Earn your MS in Data Science online in as few as 18 months. Bridge courses are available.
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* GRE waivers are available.Learn More
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University of California, Berkeley
University of California, Berkeley
Earn your Master's in Data Science online from UC Berkeley in as few as 12 months.
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University of Dayton
University of Dayton
Master of Business Analytics
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Gain in-demand analytics skills with an online master's in business analytics. Complete in as few as 12 months.
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American University
American University
Make informed decisions using data analysis in 12 months with a Master's in Business Analytics online from American University.
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Pepperdine University
Pepperdine University
Master's in Business Analytics
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Earn your MS in Business Analytics online from Pepperdine University.
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Syracuse University
Syracuse University
Looking to become a data-savvy leader? Earn your M.S. in Business Analytics online.
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* GMAT waivers available.Learn More
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Babson College
Tom Davenport
President’s Distinguished Professor of Information Technology & Management
- Specialties: Business process reengineering, knowledge management, enterprise systems, analytics
- Conferences: Keynote Speaker, 2014 Informs Business Analytics & Operations Research
- Books: Big Data @ Work, Analytics at Work, Enterprise Analytics, Keeping Up with the Quants
Cornell University
Hod Lipson
Associate Professor, Mechanical & Aerospace Engineering
- Specialties: Evolutionary robotics, design automation, rapid prototyping, artificial life, self-assembly
- Notoriety: Named one of Forbes’ World’s Most Powerful Data Scientists
- Books: Fabricated: The New World of 3D Printing
Massachusetts Institute of Technology (MIT)
Alex Pentland
Toshiba Professor of Media Arts and Sciences, Director of Human Dynamics Lab, Director of MIT Media Lab Entrepreneurship Program
- Specialties: Computational social science, organizational engineering, mobile computing
- Notoriety: Named one of Forbes’ World’s Most Powerful Data Scientists in 2014
- Projects: Social Physics
- Books: Social Physics: How Good Ideas Spread — The Lessons from a New Science, Honest Signals: How They Shape Our World
Stanford University
Hector Garcia-Molina
Professor, Computer Science and Electrical Engineering Departments
- Specialties: Distributed computing systems, digital libraries, database systems
- Conference: IEEE BigData 2013, Using Crowdsourcing for Data Analytics
- Projects: Stanford Digital Library Project
- Books: Database Systems: The Complete Book, Database System Implementation
James Matheson
Consulting Professor, Stanford School of Engineering
- Specialties: Management science and engineering
- Conferences: 2014 Informs Conference on Business Analytics & Operations Research
- Projects: SmartOrg
- Books: The Smart Organization: Creating Value Through Strategic R&D
- On big data’s impact on our lives: “Exploring data can be revealing. However, big data is not so good for decision-making. We have asked executives to look back at important decisions to see how much more data about the past would have helped, versus better judgements about the future. We get about 30% from past data and 70% from better judgements. Also, for big decisions it may be more important to adapt well and quickly as the future unfolds. So good data about the present and near past may loom in importance. Analysis of decision can direct data searches to the most beneficial areas. Of course, sometimes just playing with the data can produce valuable insights, but that is serendipity.”
Chris Re
Assistant Professor, Computer Science
- Specialties: Theoretical and practical problems in data management
- Projects: Hazy
Sebastian Thrun
Research Professor, Google Fellow, co-founder of Udacity
- Specialties: Robotics, AI
- Conferences: DataBeat 2014
- Projects: Google’s Self-Driving Car, Google Glass, Udacity
- Books: Probabilistic Robots, Robots: Science and Systems I
- Recommendation for learning about big data: “Udacity has a data science track built by industry. Leading companies like Cloudera, Facebook, and MongoDB have contributed courses. Learn from the leading experts in the world. All content is accessible for free, and every student can sign up for our classes.”
University of California-Berkeley
Michael J. Franklin
Professor of Computer Science
- Specialties: Large-scale data management infrastructure and applications
- Conferences: IEEE BigData 2013, The Berkeley Data Analysis Stack: Present and Future
- Projects: AMPLab, MLbase, CrowdDB
AnnaLee Saxenian
Dean and Professor, School of Information
- Specialties: Economics, international communities and migration of talent
- Conferences: DataEDGE
- Books: The New Argonauts: Regional Advantage in a Global Economy, Regional Advantage: Culture and Competition in Silicon Valley and Route 128
- On big data and how it touches our lives: “The impacts of big data are currently visible in the worlds of social media, technology, advertising and marketing, and finance. Big data is also many science and engineering fields like physics, biology, and astronomy. It will increasingly be visible in in health care, schools, government, and in a wide range of older industries, from autos to aerospace. Virtually every organization will want to be able to work with their data.Big data is working behind the scenes when we surf the web, use social media, and even email–whether on our mobile devices or computers. Big data is being used in our financial transactions and in our cars. It is really widespread–and soon will become ubiquitous.”
Ion Stoica
Professor, Computer Science Division
- Specialties: Cloud computing, distributed systems, networking
- Conferences: DataEDGE
- Projects: AMPLab, Mesos, Spark, BlinkDB
- On big data’s largest impact on our lives: “Today, more and more companies collect and use data to provide better services to their users (e.g., Amazon), improve safety (e.g., Boeing), improve efficiency (e.g., General Electric, PG&E), detect fraud (e.g., Paypal), and, for better or worse, optimize ad targeting. In the future, we will continue to see improvements in all these areas, and, in addition, we will see great strides in new areas, such as medicine (e.g., cancer genomics), energy conservation, and environment protection.”
Bin Yu
Chancellor’s Professor, Department of Statistics, Department of Electrical Engineering & Computer Science
- Specialties: High-dimensional data problems, statistical modeling and analysis of data structures, machine learning
- Projects: Stability, Embracing Statistical Challenges in the Information Technology Age
University of Massachusetts
Jeffrey M. Keisler
Professor of Management Information Systems
- Specialties: Decision and risk analysis, analytics, spreadsheet modeling, project/portfolio management
- Conferences: 2014 Informs Conference on Business Analytics & Operations Research
- On big data’s largest impact: “In recent years, big data was finding a lot of small uses, such as figuring out which pop-up ad to show you on a web page. More recently, it has been used to find efficiencies in business processes, which has a lot of impact in the economy. Big data also plays a role in national security, of course. I don’t think it has yet had tremendous impact on the most important decisions companies and our society makes, but it has the potential to and it should. For example, the debate on healthcare reform involved a lot of conjecture on a wide range of issues about what the likely impacts would be from various changes to the system. Answers to a lot of the questions that were asked or should have been asked might have been found in the existing data covering the experience of many millions of Americans. This would have been possible if enough of the circumstances of each individual case were encoded and analysts were able to extract and compare all the micro experiments of policy variations that happen every day. I would like to see the methods of decision analysis in particular used as a front end to large policy and strategic decisions that would provide a framework for identifying and incorporating the most valuable information to extract from the sea of data.”
University of Virginia
John Elder
Adjunct Professor, Data Mining Consultant at Elder Research Incorporated
- Specialties: Optimization, data mining
- Conferences: 2013 Predictive Analytics World
- Projects: Elder Research
Yael Grushka-Cockayne
Assistant Professor of Business Administration
- Specialties: Multi-criteria decision analysis, behavioral decision making, project management, innovation and new product development
- Conferences: 2014 Informs Conference on Business Analytics and Operations Research
University of Washington
Cecilia Aragon
Associate Professor, Department of Human Centered Design & Engineering
- Specialties: Human factors in computer interaction, data science, collaborative games
- Projects: Scientific Collaboration and Creativity Lab, Sunfall
- Recommendation for learning more about big data: “Some good resources for learning about big data can be found in a proposed data science curriculum that I developed along with the eScience Institute. These are the key skills that market research and scientific experience have taught us are critical to data-intensive science. We are also currently developing big data PhD tracks across multiple departments in the University of Washington.”
Magdalena Balazinska
Associate Professor, Computer Science and Engineering
- Specialties: Big data management, sensor and scientific data management, cloud computing
- Projects: Myria, Nuage, CQMS, Data Eco$y$tem
- On how big data is improving science: “We’ve started to collaborate with scientists on the UW campus looking at their data management challenges from a database perspective. Existing tools are failing them, so they need new tools to help them manage their increasingly large datasets and be successful at doing their science.”
Carlos Guestrin
Amazon Professor of Machine Learning, Associate Professor in Computer Science and Engineering, Adjunct Professor in Statistics
- Specialties: Machine learning
Jeffrey Heer
Associate Professor, Computer Science and Engineering
- Specialties: Human factors in understanding large data collections, interactive systems for data visualization
- Projects: Data-Driven Documents (D3), Wrangler, Trifacta