MITx MicroMasters Program in Statistics and Data Science announces new Data Analysis elective
A new elective course in the MITx MicroMasters Program in Statistics and Data Science (SDS) offers an increased focus on applying data science to complex, real-world problems. Data Analysis: Statistical Modeling and Computation in Applications launches in Spring 2021, and is open for enrollment now.
Developed by IDSS on the edX platform, the new course is a hands-on introduction to the interplay between statistics and computation for the analysis of real data. This elective lets learners apply data analysis methods to different domains, and offers a unique choice to help tailor their learning experience. Learners will apply fundamental analytic tools and methods on randomized control trials, hypothesis testing, linear regression, and principal component analysis. They’ll learn and implement common models and methods to analyze specific types of data in four different domain areas:
- Epigenetic codes and data visualization
- Criminal networks and network analysis
- Prices, economics, and time series
- Environmental data and spatial statistics
Increased options, deeper training
The MITx MicroMasters program offers a professional and academic credential for online learners from anywhere in the world. To earn the credential, learners must pass an integrated set of MIT graduate-level courses online and proctored exams online. Credential-holders can then apply for an accelerated master’s degree program at pathway universities around the world. The SDS MicroMasters program was launched in fall 2018, and there are currently 188 credential holders worldwide.
To complete the SDS MicroMasters Program, learners must take the three core courses, and one elective — choosing between 14.310Fx Data Analysis in Social Science — Assessing Your Knowledge, or the new elective course 6.419x Data Analysis: Statistical Modeling and Computation in Applications. Data Analysis in Social Science focuses on questions of cultural, social, economic, and policy interest, while the new elective covers data analysis methods and applications in science, networks, economics and industry. Once learners have passed their four courses (3 core +1 elective), they can take the virtually proctored Capstone exam to earn the MicroMasters credential in Statistics and Data Science.
“When we launched the MicroMasters program we had a strong focus on applications in the social sciences. Now we are offering a different course, which tries to provide exposure to a collection of data science applications,” says Prof. Devavrat Shah, director of the Statistics and Data Science Center at IDSS.
The course content was developed by course instructors Stefanie Jegelka and Caroline Uhler, both associate professors of Electrical Engineering & Computer Science. On campus, this course is the required capstone subject for the minor in Statistics and Data Science that is available to MIT undergraduates from any major.
“We have prepared a series of real-world case studies to demonstrate the different ways hands-on data analysis intersects with different disciplines, and how those working in data analysis can help to advance the state of the art in specific fields,” says Uhler. Learners will use Python, R, and other software to perform full analysis and improve their data visualization and communication skills through written reports. They will discuss their methods and ideas with peers, and gain a sophisticated understanding of how to formulate questions and approaches to data analysis problems within their own areas of interest.
Says Jegelka, “The course closely couples statistical models with different application areas, by introducing problem domains, models, and hands-on exercises with domain data. Participants will get a broad view of applications and methods.”
Karene Chu, research scientist at IDSS and MITx Digital Learning Scientist, led the development of the online version for the MicroMasters program, and believes it will have broad applications. “Any learner who is interested in extracting information from data and making data-driven decisions, who has fundamental knowledge in statistics (such as covered in the MicroMasters Program in Statistics and Data Science) and proficiency in programming, will benefit from this course,” she says.
By enriching the curriculum and enhancing the data analysis pillar of the program, the team hopes to provide more flexibility for learners who may wish to focus on one or another of the many avenues of study in the complex world of data science.
“If we want to train data scientists really well, they need the same foundational pillars of knowledge, but we need to offer different options depending on one’s interests,” says Shah.
