Introduction to Computational Thinking and Data Science
- Offered byMIT Professional Education
Introduction to Computational Thinking and Data Science at MIT Professional Education Overview
Introduction to Computational Thinking and Data Science
at MIT Professional Education
Duration | 8 hours |
Total fee | Free |
Mode of learning | Online |
Schedule type | Self paced |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
Introduction to Computational Thinking and Data Science at MIT Professional Education Highlights
Introduction to Computational Thinking and Data Science
at MIT Professional Education
- Earn a Certificate of completion from MIT on successful course completion
- Instructors - Prof. Eric Grimson, Prof. John Guttag, & Dr. Ana Bell
- Learn how computation plays a role in solving problems
Introduction to Computational Thinking and Data Science at MIT Professional Education Course details
Introduction to Computational Thinking and Data Science
at MIT Professional Education
Skills you will learn
Who should do this course?
- The course is designed for students with little or no programming experience.
More about this course
- The course aims to provide students with an understanding of the role computation can play in solving problems and to help students, regardless of their major, feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class uses the Python 3.5 programming language.
Introduction to Computational Thinking and Data Science at MIT Professional Education Curriculum
Introduction to Computational Thinking and Data Science
at MIT Professional Education
Lecture 1: Introduction and Optimization Problems
Lecture 2: Optimization Problems
Lecture 3: Graph-theoretic Models
Lecture 4: Stochastic Thinking
Lecture 5: Random Walks
Lecture 6: Monte Carlo Simulation
Lecture 7: Confidence Intervals
Lecture 8: Sampling and Standard Error
Lecture 9: Understanding Experimental Data
Lecture 10: Understanding Experimental Data (cont.)
Lecture 11: Introduction to Machine Learning
Lecture 12: Clustering
Lecture 13: Classification
Lecture 14: Classification and Statistical Sins
Lecture 15: Statistical Sins and Wrap Up
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