[carlcheo]

Here's the article: 40 Key Computer Science Concepts Explained In Layman’s Terms. It explains important concepts like big O notation, hill climbing, P vs NP, public-key cryptography, and more.

I thought analogies are awesome to explain complex theories to beginners and non-technical people. Formal explanations can be too complicated and boring sometimes!

What do you guys think? Please do suggest new concepts to add or point out any mistakes that I made .

[macosxnerd101]

I looked through some of the sections; and honestly, I didn't think they really did a good job of making the concepts very clear. I think you tried to oversimplify things, and you lost important information there. That doesn't help folks looking to brush up on the material, nor does it really help non-technical folks (which, I'm not sure why you would be gearing a CS cheat sheet to non-technical folks like managers).

It's great that you're enthusiastic and want to create content, but I would pick a topic and audience, then do a really thorough writeup of it. Don't try and bite off too much, only to water everything down.

[TheAlmightyPug]

I thought the article was good for someone who has maybe heard some of the terms before but never really knew what they were. Yes, some of the explanations are watered down but I thought it was good to include some external links to articles for a more through explanation.

[carlcheo]

macosxnerd101, on 27 April 2015 - 12:47 PM, said:

I looked through some of the sections; and honestly, I didn't think they really did a good job of making the concepts very clear. I think you tried to oversimplify things, and you lost important information there. That doesn't help folks looking to brush up on the material, nor does it really help non-technical folks (which, I'm not sure why you would be gearing a CS cheat sheet to non-technical folks like managers).

It's great that you're enthusiastic and want to create content, but I would pick a topic and audience, then do a really thorough writeup of it. Don't try and bite off too much, only to water everything down.

Appreciate your comment! Will see what I can do, maybe include more in-depth explanations after the analogies.

TheAlmightyPug, on 27 April 2015 - 05:54 PM, said:

I thought the article was good for someone who has maybe heard some of the terms before but never really knew what they were. Yes, some of the explanations are watered down but I thought it was good to include some external links to articles for a more through explanation.

Agree, most of the explanations are more geared towards junior computer science students who are confused with some of the theories.

[macosxnerd101]

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Agree, most of the explanations are more geared towards junior computer science students who are confused with some of the theories.

I remember falling into this category at one point. Big-O notation perplexed me, as I didn't know exactly what it meant. It was hard to find an approachable, concise, and accurate definition. I think furthermore, seeing it applied was tough as well. Most junior computer scientists will benefit the most from seeing things applied. I might suggest trying to cover three things in an explanation of a given topic: what the topic/term is precisely, how to apply it (ie., solve problems), and why anyone should care. I think that will resonate with many learners.

Also, I was skimming through your link again and came across this:

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Note: Big O notation represents the worst-case scenario of an algorithm. Let’s assume that O(1) and O(n) are the worst-case scenarios of the example above.

It is something that tricks up a lot of people. Big-O denotes an asymptotic upper bound on a function. In the case of algorithms, we are interested in T(n), the runtime function of the algorithm where n is the size of the problem. Big-O does not represent the worst-case scenario of an algorithm. Consider Quicksort. Its average runtime complexity is O(n log n), while its worst case is O(n^2). It is also correct to say that Quicksort's time complexity is O(n!), as n^2 is dominated by n!. Saying Quicksort is O(n!) isn't the tightest upper bound though; hence, why it is a little misleading.