Study Guide

Two guys curate a study guide

Mathematics

Physics

3Blue2Brown Physics Playlist
General (uncategorized)
- 8.01 Physics I: Classical Mechanics, Fall 1999, Walter Lewin (YouTube)
- 8.02 Physics II: Electricity and Magnetism, Spring 2002, Walter Lewin (YouTube)
- 8.03 Physics III: Vibrations and Waves, Fall 2004, Walter Lewin (YouTube)
- Collection of Physics video lectures
- The Mechanical Universe - Caltech
- 8.421 Atomic and Optical Physics I, MIT OCW
- 8.422 Atomic and Optical Physics II, MIT OCW
- 8.821 String Theory and Holographic Duality, MIT OCW
- 8.851 Effective Field Theory, MIT OCW
- The Theoretical Minimum, Leonard Susskind, Stanford University (YouTube - All lectures in order), courses:
  - Classical Mechanics
  - Quantum Mechanics
  - Special Relativity and Electrodynamics
  - General Relativity
  - Cosmology
  - Statistical Mechanics
  - Advanced Quantum Mechanics
  - Higgs Boson
  - Quantum Entanglement
  - Relativity
  - Particle Physics 1: Basic Concepts
  - Particle Physics 2: Standard Model
  - Particle Physics 3: Supersymmetry and Grand Unification
  - String Theory
  - Cosmology and Black Holes
- Richard Feynman's Lectures on Physics
- PHYS 200: FUNDAMENTALS OF PHYSICS I, Yale University
- PHYS 201: FUNDAMENTALS OF PHYSICS II, Yale University
- Physics 3A: Basic Physics (English), UC Irvine
- Physics 3B: Basic Physics II (English)
- Physics 3C: Basic Physics III (English), UC Irvine
- Physics 255: General Relativity, UC Irvine
- Physics 20B: Cosmology (English), UC Irvine
- Physics 8B - Introductory Physics - Melvin Pomerantz, Spring 2015 - UC Berkeley
- Physics 10, 001 - Spring 2006 - UC Berkeley
- Physics C10 - Descriptive Introduction to Physics, Spring 2014 - UC Berkeley
- Physics 111 - Advanced Experimentation Lab - UC Berkeley
- Physics 151 - Physics for Future Physicists, Fall 2013 - UC Berkeley
- Physics 253: Quantum Field Theory Lectures by Sidney R. Coleman, Harvard University
- Quantum Field Theory II, Tata Institute of Fundamental Research
- Quantum Field Theory (University of Cambridge)
- Neutrino Physics (Amol Dighe), Tata Institute of Fundamental Research
- Quantum Space Time Series
- Lectures on Geometrical Anatomy of Theoretical Physics, Fredric Schuller
- Lectures on Quantum Theory, Fredric Schuller
- Quantum Field Theory, String Theory and Beyond, Israel Institute for Advanced Studies
- Physics 121 Spring 2015, Santa Barbara City College
- Physics 122 Fall 2013, Santa Barbara City College
- SBCC Physics 123 Spring 2014, Santa Barbara City College
- Supersymmetry (University of Cambridge)
- Introduction to Nuclear Physics MIT
- Tensor Calculus, Multilinear Algebra and Differential Geometry (General Relativity Prerequisites)
- Phys 237: Gravitational Waves with Kip Thorne - Caltech
- The WE-Heraeus International Winter School on Gravity and Light
Quantum Physics
Relativity
- General Relativity (Sunil Mukhi), Tata Institute of Fundamental Research
Astronomy/Astrophysics/Cosmology
String Theory
Statistical Mechanics

Programming Languages

C++

Java

Python

Data Structures && Algorithms

Algorithmic complexity / Big-O / Asymptotic analysis

Nothing to implement
There are a lot of videos here. Just watch enough until you understand it. You can always come back and review.
If some of the lectures are too mathy, you can jump down to the bottom and watch the discrete mathematics videos to get the background knowledge.
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
- slides
A Gentle Introduction to Algorithm Complexity Analysis
Orders of Growth (video)
Asymptotics (video)
UC Berkeley Big O (video)
UC Berkeley Big Omega (video)
Amortized Analysis (video)
Illustrating "Big O" (video)
TopCoder (includes recurrence relations and master theorem):
- Computational Complexity: Section 1
- Computational Complexity: Section 2
Cheat sheet

Data Structures

Arrays
- Implement an automatically resizing vector.
- Description:
  - Arrays (video)
  - UC Berkeley CS61B - Linear and Multi-Dim Arrays (video) (Start watching from 15m 32s)
  - Basic Arrays (video)
  - Multi-dim (video)
  - Dynamic Arrays (video)
  - Jagged Arrays (video)
  - Jagged Arrays (video)
  - Resizing 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:
- 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)
- Using Stacks Last-In First-Out (video)
- Will not implement. Implementing with array is trivial.
Queue
- Using Queues First-In First-Out(video)
- Queue (video)
- Circular buffer/FIFO
- Priority Queues (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:
- Online Courses:
- 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
- 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: &, |, ^, ~, >>, <<
- 2s and 1s complement
- count set bits
- round to next power of 2:
  - Round Up To Next Power Of Two
- swap values:
  - Swap
- absolute value:
  - Absolute Integer

Trees

Trees - Notes & Background
- Series: Core Trees (video)
- Series: Trees (video)
- basic tree construction
- traversal
- manipulation algorithms
- 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)
Binary search trees: BSTs
- Binary Search Tree Review (video)
- Series (video)
  - starts with symbol table and goes through BST applications
- Introduction (video)
- MIT (video)
- C/C++:
- 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)
- Naive Implementations (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)
- 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(i) - 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
    - note: using a min heap instead would save operations, but double the space needed (cannot do in-place).

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?")
- 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)
Sedgewick - Quicksort (4 videos)
UC Berkeley:
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:
Quick sort code:
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:

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 tradeoffs, 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:
Graphs (review and more):
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:
- 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)
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:
  - the Skiena videos can be hard to follow since he sometimes uses the whiteboard, which is too small to see
  - Skiena: CSE373 2012 - Lecture 19 - Introduction to Dynamic Programming (video)
  - Skiena: CSE373 2012 - Lecture 20 - Edit Distance (video)
  - Skiena: CSE373 2012 - Lecture 21 - Dynamic Programming Examples (video)
  - Skiena: CSE373 2012 - Lecture 22 - Applications of Dynamic Programming (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:
Object-Oriented Programming
- Optional: UML 2.0 Series (video)
- Object-Oriented Software Engineering: Software Dev Using UML and Java (21 videos):
  - Can skip this if you have a great grasp of OO and OO design practices.
  - OOSE: Software Dev Using UML and Java (video)
- SOLID OOP Principles:
  - Bob Martin SOLID Principles of Object Oriented and Agile Design (video)
  - SOLID Principles (video)
  - S - Single Responsibility Principle | Single responsibility to each Object
    - more flavor
  - O - Open/Closed Principal | On production level Objects are ready for extension but not for modification
    - more flavor
  - L - Liskov Substitution Principal | Base Class and Derived class follow ‘IS A’ principal
    - more flavor
  - I - Interface segregation principle | clients should not be forced to implement interfaces they don't use
    - Interface Segregation Principle in 5 minutes (video)
    - more flavor
  - D -Dependency Inversion principle | Reduce the dependency In composition of objects.
    - Why Is The Dependency Inversion Principle And Why Is It Important
    - more flavor
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
- Chapter 6 (Part 1) - Patterns (video)
- Chapter 6 (Part 2) - Abstraction-Occurrence, General Hierarchy, Player-Role, Singleton, Observer, Delegation (video)
- Chapter 6 (Part 3) - Adapter, Facade, Immutable, Read-Only Interface, Proxy (video)
- Series of videos (27 videos)
- 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
- Design patterns for humans
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:
- Skiena:
- 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.
Caches
- LRU cache:
- 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)
  - Scheduling (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)
- concurrency in Python (videos):
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
- TDD is dead. Long live testing.
- Is TDD dead? (video)
- Video series (152 videos) - not all are needed (video)
- Test-Driven Web Development with Python
- Dependency injection:
  - video
  - Tao Of Testing
- How to write tests
Scheduling
- in an OS, how it works
- can be gleaned from Operating System videos
String searching & manipulations
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)
- Notes on Data Structures and Programming Techniques
- Short course videos:
- 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)
- 32 bit: IEEE754 32-bit floating point binary (video)
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)

Systems Design

Coding exercises/challenges

Coding Interview Question Videos:

Challenge sites:

Challenge repos:

Interactive Coding Interview Challenges in Python

Mock Interviews:

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Study Guide

Two guys curate a study guide

Mathematics

Physics

Programming Languages

C++

Java

Python

Data Structures && Algorithms

Algorithmic complexity / Big-O / Asymptotic analysis

Algorithmic complexity / Big-O / Asymptotic analysis

Data Structures

Arrays

Linked Lists

Stack

Queue

Hash table

More Knowledge

Binary search

Bitwise operations

Trees

Trees - Notes & Background

Binary search trees: BSTs

Heap / Priority Queue / Binary Heap

Sorting

Graphs

Even More Knowledge

Recursion

Dynamic Programming

Object-Oriented Programming

Design patterns

Combinatorics (n choose k) & Probability

NP, NP-Complete and Approximation Algorithms

Caches

Processes and Threads

Testing

Scheduling

String searching & manipulations

Tries

Floating Point Numbers

Unicode

Endianness

Networking

Systems Design

Coding exercises/challenges

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