Nandan-N/Leetcode-grind

My codes and solutions from leetode grinding are stored in here

Let's Get Started -

Alright, enough talk, let's learn!

But don't forget to do coding problems from above while you learn!

Algorithmic complexity / Big-O / Asymptotic analysis

• Nothing to implement here, you're just watching videos and taking notes! Yay!
• There are a lot of videos here. Just watch enough until you understand it. You can always come back and review.
• Don't worry if you don't understand all the math behind it.
• You just need to understand how to express the complexity of an algorithm in terms of Big-O.
• Harvard CS50 - Asymptotic Notation (video)
• Big O Notations (general quick tutorial) (video)
• Big O Notation (and Omega and Theta) - best mathematical explanation (video)
• Skiena (video)
• UC Berkeley Big O (video)
• Amortized Analysis (video)
• TopCoder (includes recurrence relations and master theorem):
  • Computational Complexity: Section 1
  • Computational Complexity: Section 2
• Cheat sheet
• [Review] Big-O notation in 5 minutes (video)

Well, that's about enough of that.

When you go through "Cracking the Coding Interview", there is a chapter on this, and at the end there is a quiz to see if you can identify the runtime complexity of different algorithms. It's a super review and test.

Data Structures

• Arrays

  • About Arrays:
    • Arrays CS50 Harvard University
    • Arrays (video)
    • UC Berkeley CS61B - Linear and Multi-Dim Arrays (video) (Start watching from 15m 32s)
    • Dynamic Arrays (video)
    • Jagged Arrays (video)
  • Implement a vector (mutable array with automatic resizing):
    • Practice coding using arrays and pointers, and pointer math to jump to an index instead of using indexing.
    • New raw data array with allocated memory
      • can allocate int array under the hood, just not use its features
      • start with 16, or if starting number is greater, use power of 2 - 16, 32, 64, 128
    • size() - number of items
    • capacity() - number of items it can hold
    • is_empty()
    • at(index) - returns item at given index, blows up if index out of bounds
    • push(item)
    • insert(index, item) - inserts item at index, shifts that index's value and trailing elements to the right
    • prepend(item) - can use insert above at index 0
    • pop() - remove from end, return value
    • delete(index) - delete item at index, shifting all trailing elements left
    • remove(item) - looks for value and removes index holding it (even if in multiple places)
    • find(item) - looks for value and returns first index with that value, -1 if not found
    • resize(new_capacity) // private function
      • when you reach capacity, resize to double the size
      • when popping an item, if size is 1/4 of capacity, resize to half
  • Time
    • O(1) to add/remove at end (amortized for allocations for more space), index, or update
    • O(n) to insert/remove elsewhere
  • Space
    • contiguous in memory, so proximity helps performance
    • space needed = (array capacity, which is >= n) * size of item, but even if 2n, still O(n)

• Linked Lists

  • Description:
    • Linked Lists CS50 Harvard University - this builds the intuition.
    • Singly Linked Lists (video)
    • CS 61B - Linked Lists 1 (video)
    • CS 61B - Linked Lists 2 (video)
    • [Review] Linked lists in 4 minutes (video)
  • C Code (video) - not the whole video, just portions about Node struct and memory allocation
  • Linked List vs Arrays:
    • Core Linked Lists Vs Arrays (video)
    • In The Real World Linked Lists Vs Arrays (video)
  • Why you should avoid linked lists (video)
  • Gotcha: you need pointer to pointer knowledge: (for when you pass a pointer to a function that may change the address where that pointer points) This page is just to get a grasp on ptr to ptr. I don't recommend this list traversal style. Readability and maintainability suffer due to cleverness.
    • Pointers to Pointers
  • Implement (I did with tail pointer & without):
    • size() - returns number of data elements in list
    • empty() - bool returns true if empty
    • value_at(index) - returns the value of the nth item (starting at 0 for first)
    • push_front(value) - adds an item to the front of the list
    • pop_front() - remove front item and return its value
    • push_back(value) - adds an item at the end
    • pop_back() - removes end item and returns its value
    • front() - get value of front item
    • back() - get value of end item
    • insert(index, value) - insert value at index, so current item at that index is pointed to by new item at index
    • erase(index) - removes node at given index
    • value_n_from_end(n) - returns the value of the node at nth position from the end of the list
    • reverse() - reverses the list
    • remove_value(value) - removes the first item in the list with this value
  • Doubly-linked List
    • Description (video)
    • No need to implement

• Stack

  • Stacks (video)
  • [Review] Stacks in 3 minutes (video)
  • Will not implement. Implementing with array is trivial

• Queue

  • Queue (video)
  • Circular buffer/FIFO
  • [Review] Queues in 3 minutes (video)
  • Implement using linked-list, with tail pointer:
    • enqueue(value) - adds value at position at tail
    • dequeue() - returns value and removes least recently added element (front)
    • empty()
  • Implement using fixed-sized array:
    • enqueue(value) - adds item at end of available storage
    • dequeue() - returns value and removes least recently added element
    • empty()
    • full()
  • Cost:
    • a bad implementation using linked list where you enqueue at head and dequeue at tail would be O(n) because you'd need the next to last element, causing a full traversal each dequeue
    • enqueue: O(1) (amortized, linked list and array [probing])
    • dequeue: O(1) (linked list and array)
    • empty: O(1) (linked list and array)

• Hash table

  • Videos:

    • Hashing with Chaining (video)
    • Table Doubling, Karp-Rabin (video)
    • Open Addressing, Cryptographic Hashing (video)
    • PyCon 2010: The Mighty Dictionary (video)
    • PyCon 2017: The Dictionary Even Mightier (video)
    • (Advanced) Randomization: Universal & Perfect Hashing (video)
    • (Advanced) Perfect hashing (video)
    • [Review] Hash tables in 4 minutes (video)

  • Online Courses:

    • Core Hash Tables (video)
    • Data Structures (video)
    • Phone Book Problem (video)
    • distributed hash tables:
      • Instant Uploads And Storage Optimization In Dropbox (video)
      • Distributed Hash Tables (video)

  • Implement with array using linear probing

    • hash(k, m) - m is size of hash table
    • add(key, value) - if key already exists, update value
    • exists(key)
    • get(key)
    • remove(key)

More Knowledge

• Binary search

  • Binary Search (video)
  • Binary Search (video)
  • detail
  • blueprint
  • [Review] Binary search in 4 minutes (video)
  • Implement:
    • binary search (on sorted array of integers)
    • binary search using recursion

• Bitwise operations

  • Bits cheat sheet - you should know many of the powers of 2 from (2^1 to 2^16 and 2^32)
  • Get a really good understanding of manipulating bits with: &, |, ^, ~, >>, <<
    • words
    • Good intro: Bit Manipulation (video)
    • C Programming Tutorial 2-10: Bitwise Operators (video)
    • Bit Manipulation
    • Bitwise Operation
    • Bithacks
    • The Bit Twiddler
    • The Bit Twiddler Interactive
    • Bit Hacks (video)
    • Practice Operations
  • 2s and 1s complement
    • Binary: Plusses & Minuses (Why We Use Two's Complement) (video)
    • 1s Complement
    • 2s Complement
  • Count set bits
    • 4 ways to count bits in a byte (video)
    • Count Bits
    • How To Count The Number Of Set Bits In a 32 Bit Integer
  • Swap values:
    • Swap
  • Absolute value:
    • Absolute Integer

Trees

• Trees - Intro

  • Intro to Trees (video)
  • Tree Traversal (video)
  • BFS(breadth-first search) and DFS(depth-first search) (video)
    • BFS notes:
      • level order (BFS, using queue)
      • time complexity: O(n)
      • space complexity: best: O(1), worst: O(n/2)=O(n)
    • DFS notes:
      • time complexity: O(n)
      • space complexity: best: O(log n) - avg. height of tree worst: O(n)
      • inorder (DFS: left, self, right)
      • postorder (DFS: left, right, self)
      • preorder (DFS: self, left, right)
  • [Review] Breadth-first search in 4 minutes (video)
  • [Review] Depth-first search in 4 minutes (video)
  • [Review] Tree Traversal (playlist) in 11 minutes (video)

• Binary search trees: BSTs

  • Binary Search Tree Review (video)
  • Introduction (video)
  • MIT (video)
  • C/C++:
    • Binary search tree - Implementation in C/C++ (video)
    • BST implementation - memory allocation in stack and heap (video)
    • Find min and max element in a binary search tree (video)
    • Find height of a binary tree (video)
    • Binary tree traversal - breadth-first and depth-first strategies (video)
    • Binary tree: Level Order Traversal (video)
    • Binary tree traversal: Preorder, Inorder, Postorder (video)
    • Check if a binary tree is binary search tree or not (video)
    • Delete a node from Binary Search Tree (video)
    • Inorder Successor in a binary search tree (video)
  • Implement:
    • insert // insert value into tree
    • get_node_count // get count of values stored
    • print_values // prints the values in the tree, from min to max
    • delete_tree
    • is_in_tree // returns true if given value exists in the tree
    • get_height // returns the height in nodes (single node's height is 1)
    • get_min // returns the minimum value stored in the tree
    • get_max // returns the maximum value stored in the tree
    • is_binary_search_tree
    • delete_value
    • get_successor // returns next-highest value in tree after given value, -1 if none

• Heap / Priority Queue / Binary Heap

  • visualized as a tree, but is usually linear in storage (array, linked list)
  • Heap
  • Introduction (video)
  • Binary Trees (video)
  • Tree Height Remark (video)
  • Basic Operations (video)
  • Complete Binary Trees (video)
  • Pseudocode (video)
  • Heap Sort - jumps to start (video)
  • Heap Sort (video)
  • Building a heap (video)
  • MIT: Heaps and Heap Sort (video)
  • CS 61B Lecture 24: Priority Queues (video)
  • Linear Time BuildHeap (max-heap)
  • [Review] Heap (playlist) in 13 minutes (video)
  • Implement a max-heap:
    • insert
    • sift_up - needed for insert
    • get_max - returns the max item, without removing it
    • get_size() - return number of elements stored
    • is_empty() - returns true if heap contains no elements
    • extract_max - returns the max item, removing it
    • sift_down - needed for extract_max
    • remove(x) - removes item at index x
    • heapify - create a heap from an array of elements, needed for heap_sort
    • heap_sort() - take an unsorted array and turn it into a sorted array in-place using a max heap or min heap

Sorting

• Notes:

  • Implement sorts & know best case/worst case, average complexity of each:
    • no bubble sort - it's terrible - O(n^2), except when n <= 16
  • Stability in sorting algorithms ("Is Quicksort stable?")
    • Sorting Algorithm Stability
    • Stability In Sorting Algorithms
    • Stability In Sorting Algorithms
    • Sorting Algorithms - Stability
  • Which algorithms can be used on linked lists? Which on arrays? Which on both?
    • I wouldn't recommend sorting a linked list, but merge sort is doable.
    • Merge Sort For Linked List

• For heapsort, see Heap data structure above. Heap sort is great, but not stable

• Sedgewick - Mergesort (5 videos)

  • 1. Mergesort
  • 2. Bottom up Mergesort
  • 3. Sorting Complexity
  • 4. Comparators
  • 5. Stability

• Sedgewick - Quicksort (4 videos)

  • 1. Quicksort
  • 2. Selection
  • 3. Duplicate Keys
  • 4. System Sorts

• UC Berkeley:

  • CS 61B Lecture 29: Sorting I (video)
  • CS 61B Lecture 30: Sorting II (video)
  • CS 61B Lecture 32: Sorting III (video)
  • CS 61B Lecture 33: Sorting V (video)
  • CS 61B 2014-04-21: Radix Sort(video)

• Bubble Sort (video)

• Analyzing Bubble Sort (video)

• Insertion Sort, Merge Sort (video)

• Insertion Sort (video)

• Merge Sort (video)

• Quicksort (video)

• Selection Sort (video)

• Merge sort code:

  • Using output array (C)
  • Using output array (Python)
  • In-place (C++)

• Quick sort code:

  • Implementation (C)
  • Implementation (C)
  • Implementation (Python)

• [Review] Sorting (playlist) in 18 minutes

  • Quick sort in 4 minutes (video)
  • Heap sort in 4 minutes (video)
  • Merge sort in 3 minutes (video)
  • Bubble sort in 2 minutes (video)
  • Selection sort in 3 minutes (video)
  • Insertion sort in 2 minutes (video)

• Implement:

  • Mergesort: O(n log n) average and worst case
  • Quicksort O(n log n) average case
  • Selection sort and insertion sort are both O(n^2) average and worst case
  • For heapsort, see Heap data structure above

• Not required, but I recommended them:

  • Sedgewick - Radix Sorts (6 videos)
    • 1. Strings in Java
    • 2. Key Indexed Counting
    • 3. Least Significant Digit First String Radix Sort
    • 4. Most Significant Digit First String Radix Sort
    • 5. 3 Way Radix Quicksort
    • 6. Suffix Arrays
  • Radix Sort
  • Radix Sort (video)
  • Radix Sort, Counting Sort (linear time given constraints) (video)
  • Randomization: Matrix Multiply, Quicksort, Freivalds' algorithm (video)
  • Sorting in Linear Time (video)

As a summary, here is a visual representation of 15 sorting algorithms. If you need more detail on this subject, see "Sorting" section in Additional Detail on Some Subjects

Graphs

Graphs can be used to represent many problems in computer science, so this section is long, like trees and sorting were.

• Notes:

  • There are 4 basic ways to represent a graph in memory:
    • objects and pointers
    • adjacency matrix
    • adjacency list
    • adjacency map
  • Familiarize yourself with each representation and its pros & cons
  • BFS and DFS - know their computational complexity, their trade offs, and how to implement them in real code
  • When asked a question, look for a graph-based solution first, then move on if none

• MIT(videos):

  • Breadth-First Search
  • Depth-First Search

• Skiena Lectures - great intro:

  • CSE373 2020 - Lecture 10 - Graph Data Structures (video)
  • CSE373 2020 - Lecture 11 - Graph Traversal (video)
  • CSE373 2020 - Lecture 12 - Depth First Search (video)
  • CSE373 2020 - Lecture 13 - Minimum Spanning Trees (video)
  • CSE373 2020 - Lecture 14 - Minimum Spanning Trees (con't) (video)
  • CSE373 2020 - Lecture 15 - Graph Algorithms (con't 2) (video)

• Graphs (review and more):

  • 6.006 Single-Source Shortest Paths Problem (video)
  • 6.006 Dijkstra (video)
  • 6.006 Bellman-Ford (video)
  • 6.006 Speeding Up Dijkstra (video)
  • Aduni: Graph Algorithms I - Topological Sorting, Minimum Spanning Trees, Prim's Algorithm - Lecture 6 (video)
  • Aduni: Graph Algorithms II - DFS, BFS, Kruskal's Algorithm, Union Find Data Structure - Lecture 7 (video)
  • Aduni: Graph Algorithms III: Shortest Path - Lecture 8 (video)
  • Aduni: Graph Alg. IV: Intro to geometric algorithms - Lecture 9 (video)
  • CS 61B 2014: Weighted graphs (video)
  • Greedy Algorithms: Minimum Spanning Tree (video)
  • Strongly Connected Components Kosaraju's Algorithm Graph Algorithm (video)
  • [Review] Shortest Path Algorithms (playlist) in 16 minutes (video)
  • [Review] Minimum Spanning Trees (playlist) in 4 minutes (video)

• Full Coursera Course:

  • Algorithms on Graphs (video)

• I'll implement:

  • DFS with adjacency list (recursive)
  • DFS with adjacency list (iterative with stack)
  • DFS with adjacency matrix (recursive)
  • DFS with adjacency matrix (iterative with stack)
  • BFS with adjacency list
  • BFS with adjacency matrix
  • single-source shortest path (Dijkstra)
  • minimum spanning tree
  • DFS-based algorithms (see Aduni videos above):
    • check for cycle (needed for topological sort, since we'll check for cycle before starting)
    • topological sort
    • count connected components in a graph
    • list strongly connected components
    • check for bipartite graph

Even More Knowledge

• Recursion

  • Stanford lectures on recursion & backtracking:
    • Lecture 8 | Programming Abstractions (video)
    • Lecture 9 | Programming Abstractions (video)
    • Lecture 10 | Programming Abstractions (video)
    • Lecture 11 | Programming Abstractions (video)
  • When it is appropriate to use it?
  • How is tail recursion better than not?
    • What Is Tail Recursion Why Is It So Bad?
    • Tail Recursion (video)
  • 5 Simple Steps for Solving Any Recursive Problem(video)

  Backtracking Blueprint: Java Python

• Dynamic Programming

  • You probably won't see any dynamic programming problems in your interview, but it's worth being able to recognize a problem as being a candidate for dynamic programming.
  • This subject can be pretty difficult, as each DP soluble problem must be defined as a recursion relation, and coming up with it can be tricky.
  • I suggest looking at many examples of DP problems until you have a solid understanding of the pattern involved.
  • Videos:
    • Skiena: CSE373 2020 - Lecture 19 - Introduction to Dynamic Programming (video)
    • Skiena: CSE373 2020 - Lecture 20 - Edit Distance (video)
    • Skiena: CSE373 2020 - Lecture 20 - Edit Distance (continued) (video)
    • Skiena: CSE373 2020 - Lecture 21 - Dynamic Programming (video)
    • Skiena: CSE373 2020 - Lecture 22 - Dynamic Programming and Review (video)
    • Simonson: Dynamic Programming 0 (starts at 59:18) (video)
    • Simonson: Dynamic Programming I - Lecture 11 (video)
    • Simonson: Dynamic programming II - Lecture 12 (video)
    • List of individual DP problems (each is short): Dynamic Programming (video)
  • Yale Lecture notes:
    • Dynamic Programming
  • Coursera:
    • The RNA secondary structure problem (video)
    • A dynamic programming algorithm (video)
    • Illustrating the DP algorithm (video)
    • Running time of the DP algorithm (video)
    • DP vs. recursive implementation (video)
    • Global pairwise sequence alignment (video)
    • Local pairwise sequence alignment (video)

• Design patterns

  • Quick UML review (video)
  • Learn these patterns:
    • strategy
    • singleton
    • adapter
    • prototype
    • decorator
    • visitor
    • factory, abstract factory
    • facade
    • observer
    • proxy
    • delegate
    • command
    • state
    • memento
    • iterator
    • composite
    • flyweight
  • Series of videos (27 videos)
  • Book: Head First Design Patterns
    • I know the canonical book is "Design Patterns: Elements of Reusable Object-Oriented Software", but Head First is great for beginners to OO.
  • Handy reference: 101 Design Patterns & Tips for Developers

• Combinatorics (n choose k) & Probability

  • Math Skills: How to find Factorial, Permutation and Combination (Choose) (video)
  • Make School: Probability (video)
  • Make School: More Probability and Markov Chains (video)
  • Khan Academy:
    • Course layout:
      • Basic Theoretical Probability
    • Just the videos - 41 (each are simple and each are short):
      • Probability Explained (video)

• NP, NP-Complete and Approximation Algorithms

  • Know about the most famous classes of NP-complete problems, such as traveling salesman and the knapsack problem, and be able to recognize them when an interviewer asks you them in disguise.
  • Know what NP-complete means.
  • Computational Complexity (video)
  • Simonson:
    • Greedy Algs. II & Intro to NP Completeness (video)
    • NP Completeness II & Reductions (video)
    • NP Completeness III (Video)
    • NP Completeness IV (video)
  • Skiena:
    • CSE373 2020 - Lecture 23 - NP-Completeness (video)
    • CSE373 2020 - Lecture 24 - Satisfiability (video)
    • CSE373 2020 - Lecture 25 - More NP-Completeness (video)
    • CSE373 2020 - Lecture 26 - NP-Completeness Challenge (video)
  • Complexity: P, NP, NP-completeness, Reductions (video)
  • Complexity: Approximation Algorithms (video)
  • Complexity: Fixed-Parameter Algorithms (video)
  • Peter Norvig discusses near-optimal solutions to traveling salesman problem:
    • Jupyter Notebook
  • Pages 1048 - 1140 in CLRS if you have it.

• How computers process a program

  • How CPU executes a program (video)
  • How computers calculate - ALU (video)
  • Registers and RAM (video)
  • The Central Processing Unit (CPU) (video)
  • Instructions and Programs (video)

• Caches

  • LRU cache:
    • The Magic of LRU Cache (100 Days of Google Dev) (video)
    • Implementing LRU (video)
    • LeetCode - 146 LRU Cache (C++) (video)
  • CPU cache:
    • MIT 6.004 L15: The Memory Hierarchy (video)
    • MIT 6.004 L16: Cache Issues (video)

• Processes and Threads

  • Computer Science 162 - Operating Systems (25 videos):
    • for processes and threads see videos 1-11
    • Operating Systems and System Programming (video)
  • What Is The Difference Between A Process And A Thread?
  • Covers:
    • Processes, Threads, Concurrency issues
      • Difference between processes and threads
      • Processes
      • Threads
      • Locks
      • Mutexes
      • Semaphores
      • Monitors
      • How they work?
      • Deadlock
      • Livelock
    • CPU activity, interrupts, context switching
    • Modern concurrency constructs with multicore processors
    • Paging, segmentation and virtual memory (video)
    • Interrupts (video)
    • Process resource needs (memory: code, static storage, stack, heap, and also file descriptors, i/o)
    • Thread resource needs (shares above (minus stack) with other threads in the same process but each has its own pc, stack counter, registers, and stack)
    • Forking is really copy on write (read-only) until the new process writes to memory, then it does a full copy.
    • Context switching
      • How context switching is initiated by the operating system and underlying hardware?
  • threads in C++ (series - 10 videos)
  • CS 377 Spring '14: Operating Systems from University of Massachusetts
  • concurrency in Python (videos):
    • Short series on threads
    • Python Threads
    • Understanding the Python GIL (2010)
      • reference
    • David Beazley - Python Concurrency From the Ground Up: LIVE! - PyCon 2015
    • Keynote David Beazley - Topics of Interest (Python Asyncio)
    • Mutex in Python

• Testing

  • To cover:
    • how unit testing works
    • what are mock objects
    • what is integration testing
    • what is dependency injection
  • Agile Software Testing with James Bach (video)
  • Open Lecture by James Bach on Software Testing (video)
  • Steve Freeman - Test-Driven Development (that’s not what we meant) (video)
    • slides
  • Dependency injection:
    • video
    • Tao Of Testing
  • How to write tests

• String searching & manipulations

  • Sedgewick - Suffix Arrays (video)
  • Sedgewick - Substring Search (videos)
    • 1. Introduction to Substring Search
    • 2. Brute-Force Substring Search
    • 3. Knuth-Morris Pratt
    • 4. Boyer-Moore
    • 5. Rabin-Karp
  • Search pattern in text (video)

  If you need more detail on this subject, see "String Matching" section in Additional Detail on Some Subjects.

• Tries

  • Note there are different kinds of tries. Some have prefixes, some don't, and some use string instead of bits to track the path
  • I read through code, but will not implement
  • Sedgewick - Tries (3 videos)
    • 1. R Way Tries
    • 2. Ternary Search Tries
    • 3. Character Based Operations
  • Notes on Data Structures and Programming Techniques
  • Short course videos:
    • Introduction To Tries (video)
    • Performance Of Tries (video)
    • Implementing A Trie (video)
  • The Trie: A Neglected Data Structure
  • TopCoder - Using Tries
  • Stanford Lecture (real world use case) (video)
  • MIT, Advanced Data Structures, Strings (can get pretty obscure about halfway through) (video)

• Floating Point Numbers

  • simple 8-bit: Representation of Floating Point Numbers - 1 (video - there is an error in calculations - see video description)

• Unicode

  • The Absolute Minimum Every Software Developer Absolutely, Positively Must Know About Unicode and Character Sets
  • What Every Programmer Absolutely, Positively Needs To Know About Encodings And Character Sets To Work With Text

• Endianness

  • Big And Little Endian
  • Big Endian Vs Little Endian (video)
  • Big And Little Endian Inside/Out (video)
    • Very technical talk for kernel devs. Don't worry if most is over your head.
    • The first half is enough.

• Networking

  • If you have networking experience or want to be a reliability engineer or operations engineer, expect questions
  • Otherwise, this is just good to know
  • Khan Academy
  • UDP and TCP: Comparison of Transport Protocols (video)
  • TCP/IP and the OSI Model Explained! (video)
  • Packet Transmission across the Internet. Networking & TCP/IP tutorial. (video)
  • HTTP (video)
  • SSL and HTTPS (video)
  • SSL/TLS (video)
  • HTTP 2.0 (video)
  • Video Series (21 videos) (video)
  • Subnetting Demystified - Part 5 CIDR Notation (video)
  • Sockets:
    • Java - Sockets - Introduction (video)
    • Socket Programming (video)

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Final Review

This section will have shorter videos that you can watch pretty quickly to review most of the important concepts.
It's nice if you want a refresher often.

• Series of 2-3 minutes short subject videos (23 videos)
  • Videos
• Series of 2-5 minutes short subject videos - Michael Sambol (46 videos):
  • Videos
  • Code Examples
• Sedgewick Videos - Algorithms I
• Sedgewick Videos - Algorithms II

---

Update Your Resume

• See Resume prep information in the books: "Cracking The Coding Interview" and "Programming Interviews Exposed"
• "This Is What A GOOD Resume Should Look Like" by Gayle McDowell (author of Cracking the Coding Interview),
  • Note by the author: "This is for a US-focused resume. CVs for India and other countries have different expectations, although many of the points will be the same."
• "Step-by-step resume guide" by Tech Interview Handbook
  • Detailed guide on how to set up your resume from scratch, write effective resume content, optimize it, and test your resume

Interview Process & General Interview Prep

• How to Pass the Engineering Interview in 2021
• Demystifying Tech Recruiting
• How to Get a Job at the Big 4:
  • How to Get a Job at the Big 4 - Amazon, Facebook, Google & Microsoft (video)
  • How to Get a Job at the Big 4.1 (Follow-up video)
• Cracking The Coding Interview Set 1:
  • Gayle L McDowell - Cracking The Coding Interview (video)
  • Cracking the Coding Interview with Author Gayle Laakmann McDowell (video)
• Cracking the Facebook Coding Interview:
  • The Approach
  • Problem Walkthrough
• Prep Courses:
  • Python for Data Structures, Algorithms, and Interviews (paid course):
    • A Python centric interview prep course which covers data structures, algorithms, mock interviews and much more.
  • Intro to Data Structures and Algorithms using Python (Udacity free course):
    • A free Python centric data structures and algorithms course.
  • Data Structures and Algorithms Nanodegree! (Udacity paid Nanodegree):
    • Get hands-on practice with over 100 data structures and algorithm exercises and guidance from a dedicated mentor to help prepare you for interviews and on-the-job scenarios.
  • Grokking the Behavioral Interview (Educative free course):
    • Many times, it’s not your technical competency that holds you back from landing your dream job, it’s how you perform on the behavioral interview.
  • AlgoMonster (paid course with free content):
    • The crash course for LeetCode. Covers all the patterns condensed from thousands of questions.

Mock Interviews:

• Gainlo.co: Mock interviewers from big companies - I used this and it helped me relax for the phone screen and on-site interview
• Pramp: Mock interviews from/with peers - peer-to-peer model of practice interviews
• interviewing.io: Practice mock interview with senior engineers - anonymous algorithmic/systems design interviews with senior engineers from FAANG anonymously
• Meetapro: Mock interviews with top FAANG interviewers - an Airbnb-style mock interview/coaching platform.

Be thinking of for when the interview comes

Think of about 20 interview questions you'll get, along with the lines of the items below. Have at least one answer for each. Have a story, not just data, about something you accomplished.

• Why do you want this job?
• What's a tough problem you've solved?
• Biggest challenges faced?
• Best/worst designs seen?
• Ideas for improving an existing product
• How do you work best, as an individual and as part of a team?
• Which of your skills or experiences would be assets in the role and why?
• What did you most enjoy at [job x / project y]?
• What was the biggest challenge you faced at [job x / project y]?
• What was the hardest bug you faced at [job x / project y]?
• What did you learn at [job x / project y]?
• What would you have done better at [job x / project y]?

Have questions for the interviewer

Some of mine (I already may know the answers, but want their opinion or team perspective):

• How large is your team?
• What does your dev cycle look like? Do you do waterfall/sprints/agile?
• Are rushes to deadlines common? Or is there flexibility?
• How are decisions made in your team?
• How many meetings do you have per week?
• Do you feel your work environment helps you concentrate?
• What are you working on?
• What do you like about it?
• What is the work life like?
• How is the work/life balance?

Once You've Got The Job

Congratulations!

Keep learning.

You're never really done.