6.0002 is the continuation of _[6.0001 Introduction to Computer Science and Programming in Python](/courses/6-0001-introduction-to-computer-science-and-programming-in-python-fall-2016/)_ 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.
Introduction to Computational Thinking and Data Science
Massachusetts Institute of Technology via MIT OpenCourseWare
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
Tags
Related Courses
-
Introduction to Computer Science and Programming in Python
4.6 -
Introduction to Computer Science and Programming (Fall 2008)
5.0 -
Computational Thinking using Python
4.0 -
Introduction to Computer Science and Programming (Spring 2011)
-
Introduction to Computer Science and Programming Using Python
4.5 -
Introduction to Computational Thinking and Data Science
4.5
Related articles
Reviews
5.0 rating, based on 1 Class Central review
-
Venkatdurga SaiThe 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.
