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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

前往原始課程

CC BY-NC-SA 4.0

課程簡介

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

Source	MIT 開放式課程
科系	Electrical Engineering and Computer Science
Language	English
影片數	15

課程影片 (15)

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. Optimization Problems

2. Optimization Problems

3. Graph-theoretic Models

3. Graph-theoretic Models

4. Stochastic Thinking

4. Stochastic Thinking

5. Random Walks

5. Random Walks

6. Monte Carlo Simulation

6. Monte Carlo Simulation

7. Confidence Intervals

7. Confidence Intervals

8. Sampling and Standard Error

8. Sampling and Standard Error

9. Understanding Experimental Data

9. Understanding Experimental Data

10. Understanding Experimental Data (cont.)

10. Understanding Experimental Data (cont.)

11. Introduction to Machine Learning

11. Introduction to Machine Learning

13. Classification

13. Classification

14. Classification and Statistical Sins

14. Classification and Statistical Sins

15. Statistical Sins and Wrap Up

15. Statistical Sins and Wrap Up