reading-notes

Reading notes for Code Fellows Courses

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Notes on Pain/Suffering & Big O

Pain and Suffering

The next ten weeks will be incredibly demanding, and I will need to sacrifice other aspects of my well-being most likely. I need to continue to think about what I want and why I am doing this so that I can stay motivated and gritty and push through to tackle every problem that comes my way.

Beginners Guide to Big O

This topic is crucial as it introduces the concept of algorithms, which I know are incredibly common, useful, and powerful in practical implementations of Python and programming generally.

  • O(1) is an algorithm that executes in same time/space regardless of data set input size.
  • O(N) is an algorithm the output of which will grow linearly and directly proportional to input data set size.
  • O(N^2) is an algorithm the output of which is directly proportional to the square of the input data set size.
  • O(2^N) is an algorithm with growth doubling as the input data set is added to (i.e. Fibonacci).
  • Logarithm has quick growth that then flattens.

This is all bringing flashbacks to my calculus and applied math courses in high school / college.

Things I want to know more about

I’m really eager to see how we will start implementing algorithms in our code.