Data Science Education: Massachusetts Institute of Technology

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MIT’s Institute for Data, Systems, and Society (IDSS) is focused on addressing a broad range of challenges by promoting research and education in Data Science education, statistics, decision making, and the social sciences. It was launched simultaneously, in the Fall of 2016, with MIT’s new Minor in Statistics and Data Science, and is available to MIT undergraduates, from any major. They do not yet have a Data Science PhD program, but are in the process of developing one. However, it should be noted that MIT has been researching “Machine Learning since 1997,” and has developed a significant expertise in this area. Machine Learning, in turn, has laid a foundation for their research in Artificial Intelligence (AI). Additionally, MIT has put a heavy emphasis on providing business professionals with a need-based, non-degree, Data Science education, providing the school with an understanding of real-world business needs.

In 1997, MIT’s Laboratory for Computer Science installed a new, very powerful supercomputer. In an interview, Professor Paul Viola of MIT’s Artificial Intelligence Laboratory, stated:

“We can now investigate problems in Machine Learning, smart text retrieval, and visual object recognition. These problems are either difficult, or impossible, without high-speed parallel computers, because of the massive calculations involved.”

The AI and Computer labs were (and are) subdivisions of the Electrical Engineering & Computer Science Department (EECS). The Machine Learning Group is not a part of the IDSS, but is supported by the EECS.

MIT has chosen to list Data Science education as a subdivision of IDSS, but in truth many of the needed classes are taught by the EECS. The Minor in Statistics and Data Science requires six core classes, taken from a list of twenty-seven options. IDSS is spread out among MIT’s five schools of learning and embraces a philosophy of producing “well-rounded” Data Scientists. Students will be taught information theory, social behavior, optimization, control theory, and network science. These varied disciplines promote the understanding of complex design principles and structures, and stimulate new paradigms and new ideas. The program focuses on giving students a working knowledge of computation, statistics, and probabilities, and the skills to perform a data analysis.

Alexander Wang is a former student at MIT, and speaking about Machine Learning classes, stated:

“The homework structure is interesting, and after the fact, I’m beginning to really appreciate it. The graded homework is three academic-style papers, in which you implement a specific algorithm derived in class, investigate how various parameters affect the results, and apply it to some interesting real-world data set. For example, one assignment was applying ridge regression to the Titanic dataset, and another was creating a neural network to classify handwritten numbers on the classic MNIST handwritten digit database.”

MIT Professional Education

The MIT Professional Education program has existed for over 65 years and is a part of MIT’s identity. It provides access to MIT expertise, research, and knowledge by offering advanced education programs specifically designed for working professionals. In addition to its Digital Programs, the MIT Professional Education program offers industry-focused, on-campus, one to five day sessions through its “Short Programs,” and classes abroad through its “International Programs.” They also offer Custom Programs specifically designed for corporate clients. After completing of a program, individuals receive an MIT Professional Education Certificate of Completion and also gain access to MIT’s private professional network, and useful resource benefits.


MIT has developed a research initiative, titled SystemsThatLearn@CSAIL, with the goal of accelerating the evolution, development, and deployment of software systems using Machine Learning and Artificial Intelligence. ‘Systems That Learn’ was created to allow cross-collaboration and to accelerate the development of human-like AI systems to serve humanity. MIT coordinates its faculty to work with select, state-of-the-art industry partners in resolving some of the most difficult real-world business issues.

The CSAIL initiative has played a major role in the evolution of computers. Its researchers have been prominently involved in major developments, such as massively parallel computers, time-sharing, and a great deal of the technology supporting the World Wide Web and the Internet. Its members (past and present) have started over 100 businesses, including Lotus Development Corporation, Akamai, and iRobot. CSAIL is also the home of the World Wide Web Consortium (W3C).

CSAIL research supports roughly 50 research groups. Each group is led by a faculty Principal Investigator and is generally made up of a mixture of undergraduate and graduate students, post-docs, and research staff. CSAIL research is focused on three areas of interest:

  • Artificial Intelligence
  • Systems

Advancing AI Ethics and Governance

A $27 million fund has been established to advance Artificial Intelligence research for the public good. The Berkman Klein Center for Internet & Society at Harvard University, and the MIT Media Lab will act as bases for the initiative. The goal is to support a variety of AI governance and ethics projects in the United States, and abroad. The Berkman Klein Center’s mission is to understand and explore cyberspace. The Center studies AI norms, development, dynamics, and standards and attempts to assess the need for sanctions and laws.

AI and complicated algorithms in general, combined with Deep Learning systems and Big Data, are rapidly changing how individuals work and live. Generally speaking, people are unaware of the information about their lives that is gathered by various corporations. Another significant concern is built in prejudices installed in decision making programs, such as bank loans and social services.

Joi Ito, MIT’s Media Lab director said:

“AI’s rapid development brings along a lot of tough challenges. For example, one of the most critical challenges is how do we make sure that the machines we ‘train’ don’t perpetuate and amplify the same human biases that plague society? How can we best initiate a broader, in-depth discussion about how society will co-evolve with this technology, and connect computer science and social sciences to develop intelligent machines that are not only ‘smart,’ but also socially responsible?”

The funding will work with existing efforts, and with communities focused on similar goals. A significant portion of the fund will sponsor and support an AI fellowship, provide support for joint projects, and build networks to guide AI’s evolution in ways that help create a healthy society.

This research will include questions that address society’s ethical expectations of AI, using Machine Learning to learn ethical and legal norms from data. The Media Lab has recently begun exploring the moral issues involved with autonomous vehicles and by the ethics of human-robot interaction.

Cynthia Breazeal, the leader of MIT’s Personal Robots group, said:

“Artificial Intelligence provides the potential for deeply personalized learning experiences for people of all ages and stages.” But she added, “It is also a kind of double-edged sword. What should it be learning and adapting to benefit you? And what should it do to protect your privacy and your security?”

Laboratory for Information and Decision Systems (LIDS)

LIDS is an interdepartmental center of research focused on advancing the education and research of decision-making sciences and analytical information. Areas of interest are:

  • Systems and control
  • Communications and networks
  • Inference and statistical data processing

LIDS is a part of the new Institute for Data, Systems, and Society (IDSS) and develops new analytical techniques and tools for analysis, design, system modeling, and optimization.

The New IDSS

Munther Dahleh, a lead professor in the Department of Electrical Engineering and Computer Science, is the new head for the IDSS. Professor Dahleh stated:

“In order to understand things like power outages and bank failures, you still need electrical engineers and economists—but today you also need anthropologists and Data Scientists, too. Our ability to collect and aggregate data is already well beyond our ability to understand what it could tell us— and no single discipline, on its own, holds the keys to solving this problem.”

MIT is a leading institution in providing Data Science education and with all its ongoing research, investment, and continuing growth of its many programs in Data Science, Machine Learning, and Artificial Intelligence, MIT is assured of being a leading proponent of these inter-related fields far into the future.

Photo Credit: Massachusetts Institute of Technology

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