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6.0002 Introduction to Computational Thinking and Data Science

MIT's follow-on to 6.0001 on optimization, simulation, statistics and intro machine learning.

Taught byMIT OpenCourseWare
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About this course

Using Python, this course teaches how computation helps understand data: optimization problems, graph models, stochastic thinking, Monte Carlo simulation, sampling and confidence, experimental data, and basic machine learning, ending with common statistical mistakes.

Best for: Programmers moving into data science foundations.

What you will be able to do

  • Model optimization and graph problems
  • Run Monte Carlo simulations
  • Reason about sampling and confidence
  • Apply simple clustering and classification

Syllabus and your progress

Tick modules off as you finish them on MIT OpenCourseWare.

0 of 7 modules done

Plan your pace

At 4 hours a week you would finish in about 15 weeks, using the course time estimate above.

Progress is saved in this browser only. No account needed.

Cost and certificate

No certificate

OpenCourseWare offers no certificate.

Before you start: 6.0001 or equivalent Python

MIT OpenCourseWare sets the terms and can change them. Check the course page before you commit serious time, and tell us if something changed.

Quick answers

Is 6.0002 Introduction to Computational Thinking and Data Science really free?
Yes, the course material is free on MIT OpenCourseWare. OpenCourseWare offers no certificate.
How long does 6.0002 Introduction to Computational Thinking and Data Science take?
Plan for about 60 hrs in total (Editorial estimate; the provider does not publish a total. Half-semester course.). It is video lectures, so you can go faster or slower.
Do I need experience before 6.0002 Introduction to Computational Thinking and Data Science?
It helps to have: 6.0001 or equivalent Python.

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6.0002 Introduction to Computational Thinking and Data Science

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