Data Analysis: Statistical Modeling and Computation in Applications
A hands-on introduction to the interplay between statistics and computation for the analysis of real data. -- Part of the MITx MicroMasters program in Statistics and Data Science.
Course Information
Certificate Track
Learn for Free
About this Course
If you have specific questions about this course, please contact us at sds-mm@mit.edu.
Data science requires multi-disciplinary skills ranging from mathematics, statistics, machine learning, problem solving to programming, visualization, and communication skills. In this course, learners will combine these foundational and practical skills with domain knowledge to ask and answer questions using real data.
This course will start with a review of common statistical and computational tools such as hypothesis testing, regression, and gradient descent methods. Then, learners will study 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
Learners will be guided to analyze a real data set from each of these areas of focus, and present their findings in written reports. They will also discuss relevant and practical issues with peers.
This course is part of the MITx MicroMasters Program in Statistics and Data Science. It is at a similar pace and level of rigor as an on-campus course at MIT. Master the skills needed to be an informed and effective practitioner of data science. You will complete this course and three others from MITx and then take a virtually-proctored exam to earn your MicroMasters, an academic credential that will demonstrate your proficiency in data science or accelerate your path towards an MIT PhD or a Master's at other universities. To learn more about this program, please visit https://micromasters.mit.edu/ds/.
What you'll learn
- Model, form hypotheses, perform statistical analysis on real data
- Use dimension reduction techniques such as principal component analysis to visualize high-dimensional data and apply this to genomics data
- Analyze networks (e.g. social networks) and use centrality measures to describe the importance of nodes, and apply this to criminal networks
- Model time series using moving average, autoregressive and other stationary models for forecasting with financial data
- Use Gaussian processes to model environmental data and make predictions
- Communicate analysis results effectively
Prerequisites
- Undergraduate Python programming
- Undergraduate multi-variable calculus, and linear algebra
- Undergraduate probability theory and statistics
- Undergraduate machine learning
Meet your instructors
Stefanie Jegelka
X-Consortium Career Development Associate Professor
Prof Jegelka is an X-Consortium Career Development Associate Professor at MIT EECS, and a member of CSAIL, IDSS, the Center for Statistics and Machine Learning at MIT. She is also affiliated with the ORC. Before that, she was a postdoc in the AMPlab and computer vision group at UC Berkeley, and a PhD student at the Max Planck Institutes in Tuebingen and at ETH Zurich.
Her research is in algorithmic machine learning, and spans modeling, optimization algorithms, theory and applications. In particular, she has been working on exploiting mathematical structure for discrete and combinatorial machine learning problems, for robustness and for scaling machine learning algorithms.
Her research is supported by a Sloan Research Fellowship, an NSF CAREER Award, a DARPA Young Faculty Award, an NSF BIGDATA, an Adobe Research award, an STL award and other awards by NSF and DARPA. Previously, she was also supported by a Google Research Award and an MIT RSC award.
