Course description
Data Analysis: Statistical Modeling and Computation in Applications
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.
Upcoming start dates
Who should attend?
Prerequisites
- Undergraduate Python programming
- Undergraduate multi-variable calculus, and linear algebra
- Undergraduate probability theory and statistics
- Undergraduate machine learning
Course delivery details
This course is offered through Massachusetts Institute of Technology, a partner institute of EdX.
10-15 hours per week
Costs
- Verified Track -$300
- Audit Track - Free
Certification / Credits
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
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edX
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