Statistics and Data Science (Time Series and Social Sciences Track)
Program Information
Certificate Track
About this Program
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.
You are currently exploring the Time Series and 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.
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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
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.
Explore the 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, data analysis, 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.
- Understand the interplay between statistics and computation for the analysis of real data.
- 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 track of the MicroMasters program will prepare you for job titles such as: Data Scientist, Data Analyst, Business Intelligence Analyst, Systems Analyst, Data Engineer in Social Sciences contexts
Courses
To complete this program, you must take 5 required courses.
Required Courses
Meet your instructors
Devavrat Shah
Andrew (1956) and Erna Viterbi Professor, Department of Electrical Engineering and Computer Science (EECS); Director, MicroMasters Program in Statistics and Data Science
Professor Shah’s research focuses on statistical inference and stochastic networks. His contributions span a variety of areas including resource allocation in communications networks, inference and learning on graphical models, and algorithms for social data processing including ranking, recommendations and crowdsourcing. Within the broad context of networks, his work spans a range of areas across electrical engineering, computer science and operations research.
