Data Analysis: Statistical Modeling and Computation in Applications
- Free to audit
- Paid certificate
- 16 weeks
6.419x is the applied capstone-style course in the MITx MicroMasters Program in Statistics and Data Science. It combines the mathematics, statistics, machine learning, programming and visualisation skills that data science requires with domain knowledge, so that learners can ask and answer questions using real data. The course begins with a review of common statistical and computational tools, including hypothesis testing, regression and gradient descent, and then studies models and methods for four domain areas: epigenetic codes and data visualisation, criminal networks and network analysis, prices, economics and time series, and environmental data and spatial statistics.
In each area learners analyse a real dataset and present their findings in a written report, and discuss practical issues with peers. The course is instructor-paced and estimated at 16 weeks, at a level MIT compares with an on-campus class. Auditing is free with access to the course and materials during the session (MIT's help centre says audit access ends shortly after the session closes), while the $300 certificate track adds graded assignments and exams and an MIT certificate on completion, counting toward the MicroMasters credential with the programme's other courses.
What you’ll learn
- Form hypotheses and perform statistical analysis on real data
- Visualise high-dimensional data with dimension reduction such as PCA, applied to genomics
- Analyse networks with centrality measures, applied to criminal networks
- Model and forecast time series with moving average and autoregressive models
- Use Gaussian processes to model environmental data
- Communicate analysis results in written reports
Who it’s for
Learners who already have probability, statistics and Python skills and want to practise end-to-end analysis on real datasets across several domains.
Instructors
- Stefanie Jegelka MIT
- Caroline Uhler MIT
- Karene Chu MIT
Course details
- Prerequisites
- Undergraduate Python programming Undergraduate multi-variable calculus, and linear algebra Undergraduate probability theory and statistics
Source: MIT Open Learning (opens in a new tab) · Verified · Report a change
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