Leon Noel

Interview Secrets

#100Devs

"I just popped a bean and I'm on a helicopter
I don’t wanna do shit, unless I'm with my partners"

Agenda

  • Questions?

  • Discuss - THE TEA

  • Submission - Homework?

  • Review - How To Learn

  • Learn - What are DS and Algorithms

  • Learn - Key Data Structures

  • Homework - Challenge Questions and Algo Reading 

"It's really just about reaching your full LSC"

Questions

About class or life

Congrats Sam

12 / 100

Head of Content Strategy & Marketing at

@hashnode

 

Congrats OCC

 

Microsoft

Sponsored Stream

THURSDAY & FRIDAY

The Tea Spillith

Portfolio, Twitter, Linkedin

"Just had a coffee chat was told they see so many applicants with empty githubs, no portfolio sites and no projects under their belt

 

We’re on the right track yall, Leon has definitely given us the cheat codes"

 

@Telescope_Thieves

Github

Profile Generator

Readme Template

PUSH EVERY DAY

Recruiters Love Green Squares

10 Premium Apps

Custom everything plus, tweets, blog, and project

P.R.E.P

Parameters, Returns, Examples, Pseudo Code

Methods Questions

Brute Force

Data Structures & Algorithms

Learning How To Learn

ACTIVE RECALL
SPACED REPETITION

Active Recall

Ali Abdaal: https://youtu.be/ukLnPbIffxE

Forgetting Curve*

https://intelalearning.wordpress.com

Resetting Forgetting Curve

https://www.pearsoned.com/three-simple-research-based-ways-to-ace-a-test

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

Your inputs and outputs should be defined. This is a great use case for unit tests

Think of a library

Thanks Scotch.io

A library contains many data structures

  • Books

  • Magazines

  • CDs

  • DVDs

  • Blu-rays

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

Blu-rays 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

Big-O Notation

Big-O notation mathematically describes the complexity of an algorithm in terms of time and space

Common Complexities

Git Connected Big O Article

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 * (listItem + 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.

O(LOG N)

Logarithmic Time

Divide and Conquer?

Time to build the toolbox

Data Structures

Advantages 

 

Disadvantages

When To Use

Example

  • Linked List

  • Doubly Linked List

  • Queue

  • Stack

  • Hash Table (Map)

  • Heap (min)

  • Heap (max)

  • Tree

  • Graph

Homework

#100devs - Interview Secrets

By Leon Noel

#100devs - Interview Secrets

Class 50 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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