15 episodes

This course provides students with an understanding of the role computation can play in solving problems. Student will learn to write small programs using the Python 3.5 programming language.

Introduction to Computational Thinking and Data Science MIT

    • Technology
    • 4.9 • 29 Ratings

This course provides students with an understanding of the role computation can play in solving problems. Student will learn to write small programs using the Python 3.5 programming language.

    • video
    Lecture 1: Introduction and Optimization Problems

    Lecture 1: Introduction and Optimization Problems

    Prof. Guttag provides an overview of the course and discusses how we use computational models to understand the world in which we live, in particular he discusses the knapsack problem and greedy algoriths.

    • 2 sec
    • video
    Lecture 2: Optimization Problems

    Lecture 2: Optimization Problems

    Prof. Guttag explains dynamic programming and shows some applications of the process.

    • 2 sec
    • video
    Lecture 3: Graph-theoretic Models

    Lecture 3: Graph-theoretic Models

    Prof. Grimson discusses graph models and depth-first and breadth-first search algorithms.

    • 3 sec
    • video
    Lecture 4: Stochastic Thinking

    Lecture 4: Stochastic Thinking

    Prof. Guttag introduces stochastic processes and basic probability theory.

    • 2 sec
    • video
    Lecture 5: Random Walks

    Lecture 5: Random Walks

    Prof. Guttag discusses how to build simulations and plot graphs in Python.

    • 2 sec
    • video
    Lecture 6: Monte Carlo Simulation

    Lecture 6: Monte Carlo Simulation

    Prof. Guttag discusses the Monte Carlo simulation, Roulette

    • 3 sec

Customer Reviews

4.9 out of 5
29 Ratings

29 Ratings

smartsingh24 ,

Great course

First timer interested in data science with self taught coding experience, and so far I am loving this course. Great insight, simple to understand, and moves at a nice pace. So glad I've found this!

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