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Massachusetts Institute of Technology

Introduction to Computational Thinking and Data Science (Fall 2016)

Massachusetts Institute of Technology via MIT OpenCourseWare

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Overview

Course Features
  • Video lectures
  • Captions/transcript
  • Lecture notes
  • Assignments: problem sets (no solutions)
  • Assignments: programming (no examples)
Course Description

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.

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

Start your review of Introduction to Computational Thinking and Data Science (Fall 2016)

  • Profile image for Venkatdurga Sai
    Venkatdurga Sai
    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.
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