Introduction to Computational Thinking and Data Science
6.00.2x is an introduction to using computation to understand real-world phenomena.
Created by: John Guttag
Produced in 2015
What you will learn
- Advanced Python 3 programming.
- Graphs and graphical optimization.
- Dynamic programming.
- Plotting with pylab.
- Random walks.
- Probability and distributions.
- Monte Carlo.
- Curve fitting.
- Statistical fallacies.
Overall Score : 92 / 100
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Topics covered include:
Advanced programming in Python 3
Knapsack problem, Graphs and graph optimization
Plotting with the pylab package
Monte Carlo simulations
- Covers some of the most common and important algorithms in all of programming.
- Supplemental MIT resources are available that make this into a master class in computational science.
- Completing the substantial challenges in this course is richly rewarding and will help develop a strong understanding of data science.
- Prerequisites are not to be taken lightly. This course covers deep topics related to data scientists and assumes a strong background in Python.
- Course is difficult and will overwhelm students who are not prepared to be challenged.
- Trying this course without first completely 6.00.1x can prove prohibitively difficult.
- 4.6 Rating
- 12 Reviews
Professor Guttag is the Dugald C. Jackson Professor of Computer Science and Electrical Engineering at MIT. He leads the Computer Science and Artificial Intelligence Laboratory's Data Driven Medical Research Group. The group works on the application of advanced computational techniques to medicine. Current projects include prediction of adverse medical events, prediction of patient-specific response to therapies, non-invasive monitoring and diagnostic tools, and tele-medicine. He has also done research, published, and lectured in the areas of data networking, sports analytics, software defined radios, software engineering, and mechanical theorem proving.