MITx MicroMasters Program in Statistics and Data Science opens enrollment

The new MITx MicroMasters Program in Statistics and Data Science, which opened for enrollment today, will help online learners develop their skills in the booming field of data science. The program offers learners an MIT-quality, professional credential, while also providing an academic pathway to pursue a PhD at MIT or a master’s degree elsewhere.

“There are many online programs that provide a professional overview of data science, but they don’t offer the level of detail learners gain from an actual, residential master’s program,” says Professor Devavrat Shah, faculty director of the program and MIT professor in the Department of Electrical Engineering and Computer Science (EECS). “This new MicroMasters program in Statistics and Data Science is bringing the quality, rigor, and structure of a master’s-level, residential program in data science at MIT to a wider audience around the world, and at a very accessible price, so people can learn anywhere they are while keeping their day jobs.”

In all, seven universities will be accepting the new MicroMasters Statistics and Data Science (SDS) credential towards a master’s degree, including the Rochester Institute of Technology (United States), Doane University (United States), Galileo University (Guatemala), Reykjavik University (Iceland), Curtin University (Australia), Deakin University (Australia), and RMIT University (Australia).

“This high-quality, reasonably-priced online program prepares learners to become practicing data science professionals, giving them a lasting foundation on which to build a career as well as hands-on experience. MicroMasters credential recipients will be able to solve complex problems with data and bring value to their organizations,” says Dean for Digital Learning Krishna Rajagopal. “This program will help close the gap between the insufficient supply and the booming demand for data scientists.”

Learning that blends data science, theory, and practice

With courses starting in the fall of 2018, this new MicroMasters program will blend theory and practice, providing learners with the foundational knowledge in the methods and tools used in data science, as well as offering hands-on training in data analysis and machine learning. Through a set of four online courses developed by MIT faculty and delivered on edX, students will learn, implement, and experiment with data analysis techniques and machine learning algorithms.

The rigor of the new MicroMasters program is essential, according to Shah: “You can read all the blog posts about self-driving cars or any other aspect of data science, but that doesn’t equip you to think carefully about a technology’s strengths and limitations. You need a rigorous and structured educational environment that helps you learn things in a careful manner. That’s the value of any academic institution — to summarize the historical background and key ideas, present them in a meaningful manner so learners can carefully consider them, and help people fully prepare for the challenges of the real world.”

IDSS Director Munther Dahleh, MIT professor in EECS, believes that the new SDS program could become a model for other data science programs. “This new MicroMasters Program in Statistics and Data Science embodies the Institute for Data, Systems, and Society’s view of what education in statistics and data science should look like,” he says. “We expect many other universities to adopt this new program as the foundation for a master’s program in data science.”

The four courses being offered as part of the new MicroMasters program in SDS are:

Probability: The Science of Uncertainty and Data: An introduction to probabilistic models, including random processes and the basic elements of statistical inference. This course content is essentially the same as the corresponding MIT class Introduction to Probability — a course that has been offered and continuously refined over more than 50 years. The class will enable learners to apply the tools of probability theory to real-world applications or their research and is taught by John Tsitsiklis, the Clarence J. Lebel Professor in EECS.

Data Analysis in Social Science: Introduces learners to the essential notions of probability and statistics, covering techniques in modern data analysis: estimation, regression and econometrics, prediction, experimental design, randomized control trials (and A/B testing), machine learning, and data visualization. This class will illustrate these concepts with applications drawn from real world examples and frontier research and is taught by Esther Duflo, the Abdul Latif Jameel Professor of Poverty Alleviation and Development Economics in the MIT Department of Economics, and Sara Fisher Ellison, senior lecturer in the MIT Department of Economics.

Fundamentals of Statistics: Statistics is the science of turning the proliferating amount of available “raw” data into insights that support better decisions. Fundamental statistical principles are at the core of recent advances in machine learning, data science, and artificial intelligence. The course is taught by Philippe Rigollet, associate professor in the MIT Department of Mathematics and member of the Statistics and Data Science Center.

Machine Learning with Python — From Linear Models to Deep Learning: Machine learning tries to design and understand computer programs that learn from experience for the purpose of prediction or control. Machine learning algorithms are deployed by search engines, recommender systems, advertisers, and financial institutions for content recommendation, predicting customer behavior, compliance, or risk. This course is taught by Regina Barzilay, the Delta Electronics Professor in EECS, and Tommi S. Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society.

“The challenge before us in data science,” says Shah, “is that we want to go from data to decisions. So we need well-informed and skillful people to build a data infrastructure to support data-driven decision-making.” Opening for enrollment today, the new MicroMasters in Statistics and Data Science program will prepare the in-demand data scientists who’ll build that sustainable data infrastructure.

The MicroMasters Program in Statistics and Data Science credential enables learners to receive academic credit to seven universities around the world, making the credential a pathway to a master’s degree. The amount of credit, and the conditions for receiving it, depends upon each institution. For more information, visit the program website.

Offering pathways to a master’s degree

The MicroMasters in Statistics and Data Science credential enables learners to receive academic credit to seven universities around the world, making the credential a pathway to a master’s degree. The amount of credit, and the conditions for receiving it, depends upon each institution. The seven schools below have created 19 different pathways:

Rochester Institute of Technology (U.S.): The Master of Science in Professional Studies allows learners the opportunity to draw on courses offered across RIT graduate programs. Learners who hold the MITx MicroMasters credential in statistics and data science may apply any time during the year and upon acceptance, will be awarded approximately one-third of the credit hours required for the degree.

Doane University (U.S.): Upon acceptance into Doane's MBA program, the Statistics and Data Science MicroMasters will satisfy approximately one-third of the credit hours required for their Master of Business Administration degree.

Galileo University (Guatemala): Galileo University will offer the credential holders of the MITx MicroMasters in Statistics and Data Science the possibility of earning an equivalent of one year of the total graduate credits towards the completion of the master’s degree in data science.

Reykjavík University (Iceland): Reykjavík University School of Computer Science offers the credential holders of the MITx MicroMasters in Statistics and Data Science the possibility of earning one-quarter of the total graduate credits towards the completion of the master's in computer science. Reykjavík University School of Business offers the credential holders of the MITx MicroMasters in Statistics and Data Science the possibility of earning approximately one-third of the total graduate credits towards the completion of the following master’s programs:

• Master of Business Management
• Master of Science in Business Management
• Master of Information Management
• Master of Science in Information Management

Curtin University (Australia): Curtin Business School provides a pathway for credential holders of the MITx MicroMasters in Statistics and Data Science to their Master of Predictive Analytics (Finance and Investment Analytics stream). If a learner applies for admission to the Master of Predictive Analytics (Finance and Investment Analytics stream) at Curtin University, and is accepted, the MITx MicroMasters credential will count towards one-quarter of the coursework required for graduation.

Deakin University (Australia): Deakin offers MITx MicroMasters credential holders one-quarter of the credits needed towards the completion of these master’s degree programs:

• Master of Business Analytics (online)
• Master of Health Economics (online)
• Master of Information Systems (online)
• Master of Public Health (online)
• Master of Data Analytics (online)
• Master of Cyber Security Professional (online)
• Master of Nutrition and Population Health (on campus)

One-third of the credits will be offered towards the online Master of Information Technology. Deakin will also offer credits or an entry pathway towards its online Master of Human Nutrition.

RMIT University (Australia): RMIT University will grant academic credit towards a Master of Data Science for learners who successfully earn a MITx MicroMasters credential in the MITx MicroMasters Program in Statistics and Data Science.

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