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Uedu Open / Introduction to Computational Thinking and Data Science
6.0002

Introduction to Computational Thinking and Data Science

Prof. Eric Grimson, Prof. John Guttag, Dr. Ana Bell | Fall 2016
Data Science, Analytics & Computer Technology Computer Science Science & Math Mathematics Engineering Probability and Statistics
Go to original course
CC BY-NC-SA 4.0
Course introduction
6.0002 is the continuation of 6.0001 Introduction to Computer Science and Programming in Python and is intended for students with little or no programming experience. It 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.
Course Information
SourceMIT 開放式課程
DepartmentElectrical Engineering and Computer Science
LanguageEnglish
Number of videos15
Course videos (15)
1
1. Introduction, Optimization Problems (MIT 6.0002 Intro to Computational Thinking and Data Science)
1. Introduction, Optimization Problems (MIT 6.0002 Intro to Computational Thinking and Data Science)
2
2. Optimization Problems
2. Optimization Problems
3
3. Graph-theoretic Models
3. Graph-theoretic Models
4
4. Stochastic Thinking
4. Stochastic Thinking
5
5. Random Walks
5. Random Walks
6
6. Monte Carlo Simulation
6. Monte Carlo Simulation
7
7. Confidence Intervals
7. Confidence Intervals
8
8. Sampling and Standard Error
8. Sampling and Standard Error
9
9. Understanding Experimental Data
9. Understanding Experimental Data
10
10. Understanding Experimental Data (cont.)
10. Understanding Experimental Data (cont.)
11
11. Introduction to Machine Learning
11. Introduction to Machine Learning
12
12. Clustering
12. Clustering
13
13. Classification
13. Classification
14
14. Classification and Statistical Sins
14. Classification and Statistical Sins
15
15. Statistical Sins and Wrap Up
15. Statistical Sins and Wrap Up