Statistics and Data Science (Social Sciences Track)
Program Information
Certificate Track
About this Program
Social Sciences Track
This track will prepare you to extract meaningful insights from social, cultural, economic, and policy-related data and equip you to tackle complex real-world problems and contribute to cutting-edge advancements in AI and data-driven solutions within all social sciences.
You are currently exploring the Social Sciences track
General Track
This track will prepare you to become an informed and effective practitioner of data science who adds value to your organization across industries.
Explore the General track here
Methods Track
This track will prepare you with in-depth knowledge of data science and time series analysis and will enable you to conduct rigorous analysis, inform decision-making processes, and contribute to evidence-based practices across industries.
Explore the Methods track here
Time Series and Social Sciences Track
This track will equip you to analyze the impact of interventions on time series data, preparing you for roles in economics, public policy, and social sciences where understanding temporal dynamics is crucial for informed decision-making and policy formulation.
Explore the Time Series and Social Sciences track here
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.
What you'll learn
- Master the foundations of data science, statistics, time series with interventions, 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.
- Learn the methods for harnessing and analyzing data to answer questions of cultural, social, economic, and policy interest, and then assess that knowledge.
- Finishing this MicroMasters program will prepare you for job titles such as: Data Scientist, Data Analyst, Business Intelligence Analyst, Systems Analyst, Data Engineer.
Courses
To complete this program, you must take 5 required courses.
Required Courses
Meet your instructors
Regina Barzilay
Professor
Regina Barzilay is a Delta Electronics Professor in the Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology. Her research interests are in natural language processing, applications of deep learning to chemistry and oncology.
She is a recipient of various awards including the NSF Career Award, the MIT Technology Review TR-35 Award, Microsoft Faculty Fellowship and several Best Paper Awards at NAACL and ACL. In 2017, she received a MacArthur fellowship, an ACL fellowship and an AAAI fellowship. She received her Ph.D. in Computer Science from Columbia University, and spent a year as a postdoc at Cornell University.
