reading-notes

Class 1

Pain and suffering

I understand how rigerous this course will be, and understandably I will probably have points where i really want to quit and give up. However the main topic of this article was that growth is made through pain and going outside your comfort zone. If I am to learn this material, its going to be painful, but worth it on the other side.

A beginners guide to Big O notation

This article went over the foundation and basic info about the “Big O”. In simple terms, the big o is a way to define the time complexity of an algorithm. Calculating how long it will take an algorithm to run. As well as calculating the best, worst, and average runtimes for the specific algorithm. The notations we went over were:

0(1), an algoithm that will always execute with the exact same time, no matter what.
0(N), an algorithm that will grow linearly along with the size of the data it is carrying.
O(N²), an algorithm that has a performance tied to the square size of the input data. These can be nested iterations to result in deeper iterations like O(N³) and O(N⁴).
O(2^N), an algorithm that doubles its growth with each addition to its input data. This algorithm starts off slow, but grows faster over a period of time.
Logarithms, the idea of how many times will a number need to multiply itself to reach another number, like 500 = 5 * 5 * 5.

Facts and myths about python names

A very interesting (and confusing) talk led by Ned Batchelder about facts and myths in python. Though some of these concepts were to advanced for me, I got a decent amount of information taken away from this talk.

Points to take away/ remember:

assignment never copies data
immutable values can’t alias
function arguments are assignmeents, these can mess up functions!!!
python is dynamic, no assigned names
python has no variables

Bookmark

Things I want to know more about

These are all brand new topics Im having to wrap my head around, is it alright to not understand them so far? (Especially the video)