Thursday, April 30, 2020

Data Structures and Algorithms

I'm finally done all my courses and since the job market isn't that great right now, I have taken a different approach. Instead of working personal projects or contributing to open source, I've decided to brush up on data structures and algorithms for a bit.

One thing I found lacking for Seneca's Computer Science related programs was the science portion of Computer Science, maybe it was because I was enrolled in CPA and not their BSD program.

For CPA the only course that deals with data structures and algorithms, DSA555 is offered as a professional option. After taking the course I noticed why, as a pretty smart person in the class said "If this was a mandatory course, a lot of people would be dropping out of the program, it was pretty hard". I still wish there was another similar course or two offered so we could learn more about analyzing the run times of more complex functions and graphs.

I took DSA555 last winter and have more or less forgot how to implement or how most of the things I learned in the class work: linked lists, trees, different types of searches + sorts. So now as I am typing this blog, I am solving and looking at problems on leetcode.

A friend of mine currently works for Tesla and is looking for a new job. Most of the places he's been interviewing at for a full stack position have also asked him data structure and algorithm questions on top of questions involving joining two tables in SQL or how to mock a request for testing.

I think this is fair as it makes a developer conscious of the code they write and makes it easier to recognize patterns and respond accordingly.

For example, say I have an array of sorted numbers and I have to find if a given number exists:

I could loop through the array and check each element
I could check if my given number is the middle element in the array. Depending on if it is bigger or smaller I can use the upper or lower half of the array and repeat the same steps, until the number is found or not found.

The second option sounds tedious, but depending on the size of the array, it may actually turn out to be faster than the initial option.

It also allows developers to think about the function they are writing performance-wise. Is it a O(n) solution? O(n^2) or worse O(n^3)? If it is the latter two, can I improve the run time of it? For personal projects this may not matter as much, but if you are working on software or systems that will be used by millions of people or contains a ton of data, these little things start to add up!

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