Statistics and Data Science (Methods Track)
Program Information
Certificate Track
About this Program
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.You are currently exploring the Methods 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
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
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
Tommi Jaakkola
Professor of Electrical Engineering and Computer Science
Tommi S. Jaakkola received M.Sc. in theoretical physics from Helsinki University of Technology and Ph.D. from MIT in computational neuroscience. He joined MIT faculty 1998 and he is now the Thomas Siebel Professor in EECS and IDSS at MIT.
His research covers theory, algorithms, and applications of machine learning, from statistical inference and estimation to natural language processing, computational biology, as well as recently machine learning for chemistry. His awards include Sloan research fellowship, AAAI Fellow, and many publication awards across the research areas.
