Introduction to Computational Thinking and Data Science
Massachusetts Institute of Technology via MIT OpenCourseWare
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122
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Overview
Syllabus
1. Introduction, Optimization Problems (MIT 6.0002 Intro to Computational Thinking and Data Science).
2. Optimization Problems.
3. Graph-theoretic Models.
4. Stochastic Thinking.
5. Random Walks.
6. Monte Carlo Simulation.
7. Confidence Intervals.
8. Sampling and Standard Error.
9. Understanding Experimental Data.
10. Understanding Experimental Data (cont.).
11. Introduction to Machine Learning.
12. Clustering.
13. Classification.
14. Classification and Statistical Sins.
15. Statistical Sins and Wrap Up.
Taught by
Prof. Eric Grimson , Prof. John Guttag and Dr. Ana Bell
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Reviews
5.0 rating, based on 1 Class Central review
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The course “Introduction to Computational Thinking and Data Science” provides a comprehensive introduction to the fundamental concepts of computational thinking and data science. It offers a great opportunity for individuals looking to develop their analytical and problem-solving skills in the context of data analysis.
The course covers various essential topics, including algorithms, data structures, programming concepts, and statistical analysis. By understanding these concepts, students gain the necessary foundation to approach real-world problems and make informed decisions using data-driven insights.