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MicroMasters®

Statistics and Data Science

Program Information

Estimated: 1 year, 5 months, 10-14 hours per week

About this Program

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 54% 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 separate tracks with four online courses each and a virtually proctored exam. Each track focuses on a combination of methods-centered courses and domain analysis courses to provide you with foundational knowledge and hands-on training. All learners complete the Probability and Machine Learning courses, two other courses determined by the chosen track, and the Capstone Exam.

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 54% 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 separate tracks with four online courses each and a virtually proctored exam. Each track focuses on three methods-centered courses and a domain analysis course to provide you with foundational knowledge and hands-on training in a discipline of your choice. All learners complete the Probability and Machine Learning courses. The track you choose determines your third methods course and a final domain analysis course.

All tracks are taught by MIT faculty and administered by IDSS at a similar pace and level of rigor as an on-campus course at MIT. The program 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.

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What you'll learn

  • Master the foundations of data science, data analysis, 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.
  • Understand the interplay between statistics and computation for the analysis of real data.
  • Finishing this MicroMasters program will prepare you for job titles such as: Data Scientist, Data Analyst, Business Intelligence Analyst, Systems Analyst, Data Engineer.

Requirements

Tracks: Complete 1 out of 6

Meet your instructors

Sara Fisher Ellison

Senior Lecturer

Sara Fisher Ellison is a Senior Lecturer in the MIT Economics Department. She was an undergraduate at Purdue University and received graduate degrees from both Cambridge University and MIT. She has been a fellow at both the Institute for Advanced Study and the Hoover Institute. Her recent research has investigated a number of questions in industrial organization, with a focus on the pharmaceutical industry and ecommerce. She serves on a number of editorial boards. She has taught at the undergraduate, MBA, and Ph.D. levels, and has received awards for both outstanding teaching and pedagogical innovation. To learn more, please click here.

FAQs

This course is for learners who want rigorous, graduate-level training in statistics, machine learning, and data analysis that can be completed alongside their day job. The program is developed by MITx and the MIT Institute for Data, Systems and Society (IDSS) and runs at a similar pace and rigor as an on-campus MIT course.

Working professionals in the following roles will also benefit from this program:

  • Working professionals in analytical roles (analysts, engineers, researchers) who want to add rigorous data science skills to their current job
  • Economists, public policy analysts, and social science researchers (Social Sciences and Time Series and Social Sciences tracks)
  • Sports analysts and sports technology professionals working with professional organizations and sports enterprises (both Sports Analytics tracks)
  • Sports management and policy professionals who want to inform strategy using data (Sports Analytics and Social Sciences track)
  • Career changers with a quantitative background who are looking ton move into data science roles

Each individual course has its own prerequisites, as listed below:

  • Probability - The Science of Uncertainty and Data (all tracks)
    • College-level calculus (single-variable & multivariable)
    • Comfort with mathematical reasoning
    • familiarity with sequences, limits, infinite series, the chain rule, and ordinary or multiple integrals
  • Machine Learning with Python: from Linear Models to Deep Learning (all tracks)
    • Undergraduate Python programming
    • Undergraduate probability theory
    • Undergraduate single and multi-variable calculus
    • Vectors and matrices
  • Fundamentals of Statistics (General track, Methods Track, Social Sciences Track, and Sports Analytics & Statistics Track)
    • Undergraduate probability theory
    • Undergraduate level single and multi-variable calculus
    • Vectors and matrices
  • Data Analysis: Statistical Modeling and Computation in Applications (General track)
    • Undergraduate Python programming
    • Undergraduate multi-variable calculus, and linear algebra
    • Undergraduate probability theory and statistics
    • Undergraduate machine learning
  • Learning Time Series with Interventions (Methods Track, Time Series and Social Sciences Track)
    • Undergraduate Python programming
    • Undergraduate multi-variable calculus, and linear algebra,.
    • Undergraduate probability theory and statistics
    • basic knowledge of complex numbers
  • Data Analysis in Social Science — Assessing Your Knowledge (Time Series and Social Sciences Track, Social Sciences Track, and Sports Analytics & Social Sciences)
    • Successful completion of the Data Analysis for Social Scientists course
  • Sports Analytics (Sports Analytics & Statistics Track and Sports Analytics & Social Sciences track)
    • Successful completion of the initial two courses in this program: Probability and Machine Learning
    • Undergraduate multivariable calculus, linear algebra
    • Undergraduate Python programming
  • Capstone Exam in Statistics and Data Science (all tracks)
    • You must complete the 4 required courses on a respective track before you are eligible to take the Capstone Exam

The MicroMasters Program in Statistics and Data Science is taught by MIT faculty at a similar pace and level of rigor as an on-campus MIT course, and it prepares learners for roles that leverage probability, statistical modeling, and machine learning to analyze complex data, make data-driven predictions, and inform decision-making across industries. Course content is tied to current professional challenges, and due to the rigorous nature of courses, a MicroMasters credential is a demonstrated achievement you can use with employers to show your expertise, while also providing a pathway toward a full master’s degree.

Additionally, upon completing this program, you will:

  • Gain access to an official Infinite Connection account on the MIT Alumni Association website
  • Gain access to a private LinkedIn group, ideal for discovering job postings and professional opportunities
  • Participate in MIT Alumni Association events and exclusive MITx MicroMasters in SCM credential holder events
  • Receive discounted registration for select MIT CTL conferences, bootcamps, and workshops
  • Enjoy access to library journals through JSTOR and EBSCO
  • Be eligible for a 10% discount on all MIT Press publications (print books and e-books) for continued career development

In your day-to-day, this program will help you:

  • Build a strong statistical foundation. Master the foundations of data science, data analysis, statistics, and machine learning.
  • Make data-driven predictions. Analyze big data using probabilistic modeling and statistical inference, and choose the right methods to extract meaningful information for decision-making.
  • Build machine learning models. Develop algorithms that pull insight from seemingly unstructured data, including clustering methods and supervised methods such as deep neural networks.
  • Connect statistics and computation. Understand how the two work together when analyzing real data.
  • Prepare for data roles. Position yourself for titles like data scientist, data analyst, and data engineer.

Each track of the MicroMasters Program in Statistics and Data Science consists of five graduate-level courses: four online courses, plus a virtually-proctored Capstone Exam. All learners will take two core courses and the capstone: Probability - The Science of Uncertainty and Data; Machine Learning with Python: from Linear Models to Deep Learning; Capstone Exam in Statistics and Data Science, while the remaining two courses are track-specific.

See here for the annual course run schedule.

The six tracks are:

  • General: informed, effective practitioner of data science across industries
  • Methods: in-depth data science and time series analysis
  • Social Sciences: insights from social, cultural, economic, and policy data
  • Time Series and Social Sciences: analyzing the impact of interventions on time series data, for economics, public policy, and social sciences
  • Sports Analytics and Statistics: advanced statistics and project-based work on engineering and data challenges in sports
  • Sports Analytics and Social Sciences: data science with social science perspectives on organizations, athlete performance, and stakeholder engagement

Each individual course runs anywhere from 14-17 weeks, with an estimated time commitment of 10-16 hours per week, while the capstone runs for 4 weeks, with an estimated time commitment of 10-14 hours per week.

Courses are pre-recorded, and lessons are released according to a calendar specific to each course. The calendar will be posted at the beginning of each course. Students can work through the course content on their own schedule after it has been released. Please note some assignments have strict deadlines and work will not be accepted after that deadline.

Successful completion of this program makes you eligible to apply to master's programs at MIT and at universities around the world (and MIT is actively working to expand the list of credit pathway institutions for MicroMasters Program Credentials!). Upon acceptance, you may receive credit for your online coursework.

You will also receive a verified certificate for each course you successfully complete, plus a program certificate upon completing the full program.

Each course offers a free audit track to give you the opportunity to explore the course content. The audit track includes access to course materials and the first four graded assessments in each course, but does not include verified certificates or the ability to pursue a full masters program.

The paid, certified track of each course comes with access to all course materials, graded assignments and exams, plus the MIT Open Learning certificate of completion (one per course, plus one for the full program). Additionally, the paid track allows you to apply for a full masters program.

You do not need to purchase the full program, and can instead purchase each course individually. However, the bundled program purchase is the best value, as it includes all five courses plus the comprehensive exam for $1350 ($150 in savings compared to purchasing courses individually). Additionally, we recommend taking the courses in order for the best learning experience, and to ensure you gain the prerequisite knowledge needed for each course. Please note that some courses require you to complete other courses in the program first, and this will be listed under ‘Prerequisites’ on the individual course page.