Code-Interview-With-JS

Coding interviews problem and solutions with Javascript.

This repo is about practice Coding with most common topics thats asked by tech companies like bigO hash table etc and will try to solve some company questions with javascript.

I also practice coding in few platform like leetcode ,hackerRank and codeforces, i have separate repo for each platform.

Table of Content

• 1. Big O

• 2. Array

• 3. Hash-Tables

• 4. Functions

• 5. Linked List

Big O

Type of Big Os

• O(1): Constant- no loops
• O(log N): Logarithmic- usually searching algorithms have log n if they are sorted (Binary Search).
• O(n): Linear- for loops, while loops through n items.
• O(n log(n)): Log Liniear- usually sorting operations
• O(n^2): Quadratic- every element in a collection needs to be compared to ever other element. Two nested loops.
• O(2^n): Exponential- recursive algorithms that solves a problem of size N.
• O(n!): Factorial- you are adding a loop for every element.

Iterating through half a collection is still O(n) Two separate collections: O(a * b)

What can cause time in a function?

• Operations (+, -, *, /)
• Comparisons (<, >, ==)
• Looping (for, while)
• Outside Function call (function())

Rule Book

• Rule 1: Always worst Case
• Rule 2: Remove Constants
• Rule 3: Different inputs should have different variables. O(a+b). A and B arrays nested would be O(ab) - plus(+) for steps in order - multiply() for nested steps
• Rule 4: Drop Non-dominant terms

What causes Space complexity?

• Variables
• Data Structures
• Function Call
• Allocations

Useful link(s)

• BigO CheatSheet

What are companies looking for ?

• 1.Analytic Skills

  How can you think through a problems and analyze things, and when you're coding during interview they want to hear your thought process and how you go from not knowing the answer to solving the problem.

• 2.Coding SKills

  Is your code clean well, organized readable ?

• 3.Technical Skills

  Did you just memorize things or do you understand the pros and cons of different solutions ?

• 4.Communication Skills

  Does your personality match their companies personality ? Can you communicate well with others with the team most likely you won't be working by yourself but with others.

Good Code

The 3 pillars of good code:

• 1. Readable
• 2. Time Complexity
• 3. Space Complexity

Good code checklist

• [✅]It works
• [✅]Good use of data structures
• [✅]Code Re-use/ Do Not Repeat Yourself
• [✅]Modular - makes code more readable, maintainable and testable
• [✅]Less than O(N^2). We want to avoid nested loops if we can since they are expensive. Two separate loops are better than 2 nested loops
• [✅]Low Space Complexity --> Recursion can cause stack overflow, copying of large arrays may exceed memory of machine

Step By Step through a problem:

• 1. When the interviewer says the question, write down the key points at the top (i.e. sorted array). Make sure you have all the details. Show how organized you are.
• 2. Make sure you double check: What are the inputs? What are the outputs?
• 3. What is the most important value of the problem? Do you have time, and space and memory, etc.. What is the main goal?
• 4. Don't be annoying and ask too many questions.
• 5. Start with the naive/brute force approach. First thing that comes into mind. It shows that you’re able to think well and critically (you don't need to write this code, just speak about it).
• 6. Tell them why this approach is not the best (i.e. O(n^2) or higher, not readable, etc...)
• 7. Walk through your approach, comment things and see where you may be able to break things. Any repetition, bottlenecks like O(N^2), or unnecessary work? Did you use all the information the interviewer gave you? Bottleneck is the part of the code with the biggest Big O. Focus on that. Sometimes this occurs with repeated work as well.
• 8. Before you start coding, walk through your code and write down the steps you are going to follow.
• 9. Modularize your code from the very beginning. Break up your code into beautiful small pieces and add just comments if you need to.
• 10. Start actually writing your code now. Keep in mind that the more you prepare and understand what you need to code, the better the whiteboard will go. So never start a whiteboard interview not being sure of how things are going to work out. That is a recipe for disaster. Keep in mind: A lot of interviews ask questions that you won’t be able to fully answer on time. So think: What can I show in order to show that I can do this and I am better than other coders. Break things up in Functions (if you can’t remember a method, just make up a function and you will at least have it there. Write something, and start with the easy part.
• 11. Think about error checks and how you can break this code. Never make assumptions about the input. Assume people are trying to break your code and that Darth Vader is using your function. How will you safeguard it? Always check for false inputs that you don’t want. Here is a trick: Comment in the code, the checks that you want to do… write the function, then tell the interviewer that you would write tests now to make your function fail (but you won't need to actually write the tests).
• 12. Don’t use bad/confusing names like i and j. Write code that reads well.
• 13. Test your code: Check for no params, 0, undefined, null, massive arrays, async code, etc… Ask the interviewer if we can make assumption about the code. Can you make the answer return an error? Poke holes into your solution. Are you repeating yourself?
• 14. Finally talk to the interviewer where you would improve the code. Does it work? Are there different approaches? Is it readable? What would you google to improve? How can performance be improved? Possibly: Ask the interviewer what was the most interesting solution you have seen to this problem
• 15. If your interviewer is happy with the solution, the interview usually ends here. It is also common that the interviewer asks you extension questions, such as how you would handle the problem if the whole input is too large to fit into memory, or if the input arrives as a stream. This is a common follow-up question at Google, where they care a lot about scale. The answer is usually a divide-and-conquer approach — perform distributed processing of the data and only read certain chunks of the input from disk into memory, write the output back to disk and combine them later.

Hash Tables

Time complexity ot Hash Tables

• insert => 0(1)
• lookup => 0(1)
• delete => 0(1)
• search => 0(1)

Functions

Linked List

What is Linked List ???

As a name suggest its a list that is linked.

const basket = ["apples", "grapes", "pears"];


linked list:  apple --> grapes --> pears


apples
8949 -->  grapes
          8741  --> pears
                    372 --> null