Leon Noel
DS & Algorithms?
#100Devs
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Agenda

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Discuss  #HUNTOBER2022

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Learn  Basic DS & Algorithms

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Data Structures &
Algorithms
An algorithm is just the steps you take to solve a problem
An algorithm is just a function
This function transforms a certain input aka data structure
Into a certain output aka another data structure
The function contains logic that decides the transformation of the input to output
Think of a library
A library contains many data structures

Books

Magazines

CDs

DVDs

Blurays
If your data is a bunch of text, it makes sense to use a book to hold it.
If your data is a bunch of low quality video, it makes sense to use a dvd to hold it.
DVDs have a limit on the amount of data they can hold. Cheaper but constrained
Blurays can hold more data, but they cost more
When worrying about real data, it is also cost, but in space and time
Think of a linked vs. doubly linked list
Less Memory, but Less Efficient
More Memory, but More Efficient
To make good decisions as engineers we need to understand the different structures for our data
Back to the library
The best data structures consume minimal resources while storing data in a meaningful way for various operations
Flip Book vs. DVD
What if we want a specific book?
Data Structure
in equals string
Data Structure
out equals book
AKA our:
ALGORITHM
Do you check every book?
How much time does it take to move through each book?
Do you have to remember all the rows you already checked?
Question:
What would be the most efficient way to find the book?
How long it takes to find the book is our
Time Complexity
Thinking through the appropriate data structures and algorithms is how we can efficiently solve problems
When taking about the effecientness or complexity of a solution we can use
BigO Notation
BigO notation mathematically describes the complexity of an algorithm in terms of time and space
Common Complexities
O(1)
For all inputs to our algorithm there is and will always be only one operation required
 Order 1
 Constant Time
O(1) Example
No matter how many inputs are located in num there will only ever be one operation needed!
const nums = [1,2,3,4,5]
const firstNumber = nums[0]
O(N)
For all inputs to our algorithm there will be one
operation per input
 Order n
 Linear
 Linear Scailing
O(N) Example
Here we sum the numbers in the array. We have to add each number to a running sum, so we have to operate on each one. This means one operation per input.
const nums = [1,2,3,4,5]
let sum = 0
for(let num of nums){
sum += num
}
O(1) VS O(N)
Summing function for a sorted, contiguous array of integers that starts with the number 1? Could easily be O(n) but...
const sumContiguousArray = function(ary){
//get the last item
const lastItem = ary[ary.length  1]
//Gauss's trick
return lastItem * (lastItem + 1) / 2
}
const nums = [1,2,3,4,5]
const sumOfArray = sumContiguousArray(nums)
Committing these to memory is important
O(N^2)
Order N Squared
Text
const hasDuplicates = function(num){
for(let i = 0; i < nums.length; i++){
const thisNum = nums[i]
for(let j = 0; j < nums.length; j++){
if(j !== i){
const otherNum = nums[j]
if(otherNum === thisNum) return true
}
}
}
return false
}
const nums = [1,2,3,4,5,5]
hasDuplicates(nums) //true
O(N^2)
Here we’re iterating over our array, which we already know is O(n), and another iteration inside it, which is another O(n). For every item in our list, we have to loop our list again to calculate what we need.
Homework
#100devs  Intro DS & Algorithms
By Leon Noel
#100devs  Intro DS & Algorithms
Class 62 of our Free Web Dev Bootcamp for folx affected by the pandemic. Join live T/Th 6:30pm ET leonnoel.com/twitch and ask questions here: leonnoel.com/discord
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