Hello @ahmedev,
I wanted to know what are the good ways to understand how algorithms work, how to create your own algorithms, and how to think like a programmer.
understand how algorithms work
I think a better approach is study a specific algorithm using books, papers, blog posts, courses, etc. . A better approach if you definition of an algorithm is âa finite sequence of welldefined instructionsâ[0]. You always have the option of try to recreate the âclassic algorithmsâ from scratch âŠ but I think that is a really difficult task.
how to create your own algorithms
I think that there is a misconception that make learn how to program really difficult:
the idea that the code the programmer writes is an algorithm.
The code is not an algorithm, the code is an implementation of an algorithm.
Identify the code with the algorithm is problematic, an example: most people doesnât have problems understanding a recursive algorithm, but they have problems implementing the algorithm in JS. Why? because JS uses a call stack
and nested (function) calls
, things that the algorithm doesnât have.
You can create an algorithm without write a single line of code. Most of the time is better to use pen and paper and not use a programming language (because programming languages are difficult to use).
how to think like a programmer
I think that most of the time programmers use a heuristic
[1] approach to problem solving.
An example of heuristics problem solving using the exercise âDNA Pairingâ[2]:
Problem Explanation, (exercise âDNA Pairingâ)[3]:
There are four potential characters that exist in DNA: âAâ, âTâ, âGâ, and âCâ.
âAâ and âTâ are always paired together, and âGâ and âCâ are always paired together.
Heuristic/rule of thumb:
Forms of data descriptions[4]:
a. enumerations
 collection with a finite number of element
 lists every single piece of data that belongs to it
b. intervals
 collections of elements that satisfy a specific property
 collection with infinitely many elements
 specifies a range of data
c. itemizations
 specifies ranges in one clause of its definition
 and specific pieces of data in another clause
If we read the problem explanation
we can try to solve the exercise using an enumeration
:
4.3 Enumerations[5]
The main idea of an enumeration is:
 it defines a collection of data as a finite number of pieces of data
Each item explicitly:

spells out which piece of data belongs to the class of data that we are defining. A data representation in which every possibility is listed

When a functionâs input is a class of data whose description spells out its elements on a casebycase basis:

The function should distinguish just those cases

and compute the result on a percase basis

To distinguish in code requires:
 conditional functions: functions that choose different ways of computing results depending on the value of some argument.
Example of a conditional function and enumeration:
; TrafficLight > TrafficLight
; yields the next state given current state s
(define (trafficlightnext s)
(cond
[(string=? "red" s) "green"]
[(string=? "green" s) "yellow"]
[(string=? "yellow" s) "red"]))
In JS you can use if...else
[6] or switch
[7] statements.
Why is a rule of thumb:
Because âThe function should distinguish just those cases and compute the result on a percase basisâ is not an algorithm, is just a ârecipeâ/recomendation that you can use (the exercise can be solved in multiple ways).
Cheers and happy coding
Notes:
[0] Algorithm  Wikipedia
[1] Heuristic  Wikipedia
A heuristic âŠ (from Ancient Greek Î”áœÏÎŻÏÎșÏ (heurĂskĆ) âI find, discoverâ), or heuristic technique, is any approach to problem solving or selfdiscovery that employs a practical method that is not guaranteed to be optimal, perfect, or rational, but is nevertheless sufficient for reaching an immediate, shortterm goal or approximationâŠ
âŠ Examples that employ heuristics include using trial and error, a rule of thumb or an educated guess.
[2] https://www.freecodecamp.org/learn/javascriptalgorithmsanddatastructures/intermediatealgorithmscripting/dnapairing
[3] freeCodeCamp Challenge Guide: DNA Pairing
[4] IÂ FixedSize Data
[5] IÂ FixedSize Data
[6] if...else  JavaScript  MDN
[7] switch  JavaScript  MDN