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Massachusetts Institute of Technology

Data Analysis: Statistical Modeling and Computation in Applications

Massachusetts Institute of Technology via edX

Overview

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/.

Taught by

Stefanie Jegelka, Caroline Uhler and Karene Chu

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Reviews

1.0 rating, based on 1 reviews

Start your review of Data Analysis: Statistical Modeling and Computation in Applications

  • Anonymous
    This course is quite disorganized and at times packed with too much material which is taught too superficially.

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