MicroMasters Program in
Statistics and Data Science

From probability and statistics to data analysis and machine learning, master the skills needed to solve complex challenges with data.

About the Program

Demand for professionals skilled in data, analytics, and machine learning is exploding. The U.S. Bureau of Labor Statistics reports that demand for data science skills will drive a 27.9 percent rise in employment in the field through 2026. Data scientists bring value to organizations across industries because they are able to solve complex challenges with data and drive important decision-making processes. Not only is there a huge demand, but there is a significant shortage of qualified data scientists with 39% of the most rigorous data science positions requiring a degree higher than a bachelor’s.

This MicroMasters® program in Statistics and Data Science (SDS) was developed by MITx and the MIT Institute for Data, Systems, and Society (IDSS). It is a multidisciplinary approach comprised of four online courses and a virtually proctored exam that will provide you with the foundational knowledge essential to understanding the methods and tools used in data science, and hands-on training in data analysis and machine learning. You will dive into the fundamentals of probability and statistics, as well as learn, implement, and experiment with data analysis techniques and machine learning algorithms. This program will prepare you to become an informed and effective practitioner of data science who adds value to an organization.

To complete the SDS MicroMasters program, learners will need to take the three core courses and one out of two electives. Once learners have passed their four courses, they will then take the virtually-proctored Capstone exam to earn the MicroMasters program credential in SDS. The credential can be applied, for admitted students, towards a Ph.D. in Social and Engineering Systems (SES) through the MIT Institute for Data, Systems, and Society (IDSS) or may accelerate your path towards a Master’s degree at other universities around the world.

Anyone can enroll in this MicroMasters program. It is designed for learners who want to acquire sophisticated and rigorous training in data science without leaving their day job but without compromising quality. There is no application process, but college-level calculus and comfort with mathematical reasoning and Python programming are highly recommended if you want to excel.

All the courses of this program are taught by MIT faculty and administered by Institute for Data, Systems, and Society (IDSS), at a similar pace and level of rigor as an on-campus course at MIT. This program brings MIT’s rigorous, high-quality curricula and hands-on learning approach to learners around the world—at scale.

What You'll Learn

  • Master the foundations of data science, statistics, and machine learning
  • Analyze big data and make data-driven predictions through probabilistic modeling and statistical inference; identify and deploy appropriate modeling and methodologies in order to extract meaningful information for decision making
  • Develop and build machine learning algorithms to extract meaningful information from seemingly unstructured data; learn popular unsupervised learning methods, including clustering methodologies and supervised methods such as deep neural networks
  • Master techniques in modern data analysis to leverage big datasets; use python and R skillfully to analyze data

Job Outlook

  • A recent report by IBM and Burning Glass states that there will be 364K new job openings in data-driven professions by 2020 in the US
  • Out of those jobs, the toughest to fill are the Data Scientist/Advanced Analytics positions
  • 39% of these positions require a degree higher than a bachelor’s
  • By completing this MicroMasters program you will be able to solve complex challenges with data and drive decision-making processes for organizations
  • Finishing this MicroMasters program will prepare you for job titles such as: Data Scientist, Data Analyst, Business Intelligence Analyst, Systems Analyst, Data Engineer