Computational thinking is becoming widely recognized as a skill necessary for every educated person in a technologically advanced society.
We will focus on just a subset of computational thinking which concerns creating models of the physical world – something that engineers frequently need to do. Because of that choice, this course covers many topics normally viewed as within the domain of mathematics such as algebra and calculus, but the solution procedures are algorithmic rather than symbolic.
The major themes of the course are:
Representation -- How do you encode information about the world in a computer? How do your choices in representation affect the ease with which you can solve problems?
Decomposition -- How do you break a large and diverse problem into many simpler parts?
Discretization -- How do you break up space and time into a large number of relatively small pieces? What are the alternative ways of doing this? What are the consequences of discretization procedures for accuracy and speed?
Verification -- How do you build confidence in the results of a model?
What is Computational Thinking? (representation, discretization, error, decomposition, verification)
Interpolation (building simple surrogates for more complex functions)
Integration (processes for numerical quadrature)
Randomness (generating and using pseudorandom variables in models)
Differentiation (numerical derivatives)
Solving equations (Gaussian elimination for linear systems, Newton-Raphson for non-linear systems)
Eduardo Rodrigues is taking this course right now and found the course difficulty to be very easy.
This is not 2.086 MIT offers to its local students. It a *very* watered down version of this course. Personally, I felt disappointed, since MITx usually offers very interesting and challenging courses.