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Computational Thinking using Python

Offered By: Massachusetts Institute of Technology via edX

Tags

Computer Science Courses Python Courses Algorithms Courses Data Structures Courses Computational Thinking Courses Algorithmic Complexity Courses

Course Description

Overview

The courses in the XSeries are designed to help people with no prior exposure to computer science or programming learn to think computationally and write programs to tackle useful problems. Some of the people taking the two courses will use them as a stepping stone to more advanced computer science courses, but for many it will be their first and last computer science courses. Since these courses may be the only formal computer science courses many of the students take, we have chosen to focus on breadth rather than depth. The goal is to provide students with a brief introduction to many topics so they will have an idea of what is possible when they need to think about how to use computation to accomplish some goal later in their career. That said, they are not “computation appreciation” courses. They are challenging and rigorous courses in which the students spend a lot of time and effort learning to bend the computer to their will.

Introduction to Computer Science and Programming Using Python covers the notion of computation, the Python programming language, some simple algorithms, testing and debugging, and informal introduction to algorithmic complexity, and some simple algorithms and data structures. Introduction to Computational Thinking and Data Science will teach you how to use computation to accomplish a variety of goals and provides you with a brief introduction to a variety of topics in computational problem solving.


Syllabus

Courses under this program:
Course 1: Introduction to Computer Science and Programming Using Python

An introduction to computer science as a tool to solve real-world analytical problems using Python 3.5.



Course 2: Introduction to Computational Thinking and Data Science

6.00.2x is an introduction to using computation to understand real-world phenomena.




Courses

  • 130 reviews

    9 weeks, 14-16 hours a week, 14-16 hours a week

    View details

    This course is the first of a two-course sequence: Introduction to Computer Science and Programming Using Python, and Introduction to Computational Thinking and Data Science. Together, they are designed to help people with no prior exposure to computer science or programming learn to think computationally and write programs to tackle useful problems. Some of the people taking the two courses will use them as a stepping stone to more advanced computer science courses, but for many it will be their first and last computer science courses. This run features lecture videos, lecture exercises, and problem sets using Python 3.5. Even if you previously took the course with Python 2.7, you will be able to easily transition to Python 3.5 in future courses, or enroll now to refresh your learning.

    Since these courses may be the only formal computer science courses many of the students take, we have chosen to focus on breadth rather than depth. The goal is to provide students with a brief introduction to many topics so they will have an idea of what is possible when they need to think about how to use computation to accomplish some goal later in their career. That said, they are not "computation appreciation" courses. They are challenging and rigorous courses in which the students spend a lot of time and effort learning to bend the computer to their will

  • 30 reviews

    9 weeks, 14-16 hours a week, 14-16 hours a week

    View details

    6.00.2x will teach you how to use computation to accomplish a variety of goals and provides you with a brief introduction to a variety of topics in computational problem solving . This course is aimed at students with some prior programming experience in Python and a rudimentary knowledge of computational complexity. You will spend a considerable amount of time writing programs to implement the concepts covered in the course. For example, you will write a program that will simulate a robot vacuum cleaning a room or will model the population dynamics of viruses replicating and drug treatments in a patient's body.

    Topics covered include:

    • Advanced programming in Python 3
    • Knapsack problem, Graphs and graph optimization
    • Dynamic programming
    • Plotting with the pylab package
    • Random walks
    • Probability, Distributions
    • Monte Carlo simulations
    • Curve fitting
    • Statistical fallacies

Taught by

John Guttag, Eric Grimson and Ana Bell

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