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@ -12,7 +12,7 @@
> The items listed here will prepare you well for a technical interview at just about any software company,
> including the giants: Amazon, Facebook, Google, and Microsoft.
>
> *Best of luck to you!*
> _Best of luck to you!_
<details>
<summary>Translations:</summary>
@ -68,9 +68,10 @@
This is my multi-month study plan for becoming a software engineer for a large company.
**Required:**
* A little experience with coding (variables, loops, methods/functions, etc)
* Patience
* Time
- A little experience with coding (variables, loops, methods/functions, etc)
- Patience
- Time
Note this is a study plan for **software engineering**, not frontend engineering or full-stack development. There are really
super roadmaps and coursework for those career paths elsewhere (see https://roadmap.sh/ for more info).
@ -174,7 +175,7 @@ For a complete CS self-taught program, the resources for my study plan have been
- [Garbage collection](#garbage-collection)
- [Parallel Programming](#parallel-programming)
- [Messaging, Serialization, and Queueing Systems](#messaging-serialization-and-queueing-systems)
- [A*](#a)
- [A\*](#a)
- [Fast Fourier Transform](#fast-fourier-transform)
- [Bloom Filter](#bloom-filter)
- [HyperLogLog](#hyperloglog)
@ -225,7 +226,8 @@ It's a long plan. It may take you months. If you are familiar with a lot of this
Everything below is an outline, and you should tackle the items in order from top to bottom.
I'm using GitHub's special markdown flavor, including tasks lists to track progress.
- [More about GitHub-flavored markdown](https://guides.github.com/features/mastering-markdown/#GitHub-flavored-markdown)
- [More about GitHub-flavored markdown](https://guides.github.com/features/mastering-markdown/#GitHub-flavored-markdown)
### If you don't want to use git
@ -239,7 +241,7 @@ If you're open in a code editor that understands markdown, you'll see everything
Create a new branch so you can check items like this, just put an x in the brackets: [x]
1. ***Fork the GitHub repo:*** `https://github.com/jwasham/coding-interview-university` by clicking on the Fork button.
1. **_Fork the GitHub repo:_** `https://github.com/jwasham/coding-interview-university` by clicking on the Fork button.
![Fork the GitHub repo](https://d3j2pkmjtin6ou.cloudfront.net/fork-button.png)
@ -288,19 +290,20 @@ Preferably the language would be the same, so that you only need to be proficien
When I did the study plan, I used 2 languages for most of it: C and Python
* C: Very low level. Allows you to deal with pointers and memory allocation/deallocation, so you feel the data structures
- C: Very low level. Allows you to deal with pointers and memory allocation/deallocation, so you feel the data structures
and algorithms in your bones. In higher-level languages like Python or Java, these are hidden from you. In day-to-day work, that's terrific,
but when you're learning how these low-level data structures are built, it's great to feel close to the metal.
- C is everywhere. You'll see examples in books, lectures, videos, *everywhere* while you're studying.
- C is everywhere. You'll see examples in books, lectures, videos, _everywhere_ while you're studying.
- [The C Programming Language, 2nd Edition](https://www.amazon.com/Programming-Language-Brian-W-Kernighan/dp/0131103628)
- This is a short book, but it will give you a great handle on the C language and if you practice it a little
you'll quickly get proficient. Understanding C helps you understand how programs and memory work.
- You don't need to go super deep in the book (or even finish it). Just get to where you're comfortable reading and writing in C.
* Python: Modern and very expressive, I learned it because it's just super useful and also allows me to write less code in an interview.
- Python: Modern and very expressive, I learned it because it's just super useful and also allows me to write less code in an interview.
This is my preference. You do what you like, of course.
You may not need it, but here are some sites for learning a new language:
- [Exercism](https://exercism.org/tracks)
- [Codewars](http://www.codewars.com)
- [HackerEarth](https://www.hackerearth.com/for-developers/)
@ -326,6 +329,7 @@ This is the original article my post was based on: [Choosing a Programming Langu
You need to be very comfortable in the language and be knowledgeable.
Read more about choices:
- [Choose the Right Language for Your Coding Interview](http://www.byte-by-byte.com/choose-the-right-language-for-your-coding-interview/)
[See language-specific resources here](programming-language-resources.md)
@ -391,8 +395,7 @@ Choose one:
- [Elements of Programming Interviews (C++ version)](https://www.amazon.com/Elements-Programming-Interviews-Insiders-Guide/dp/1479274836)
- [Elements of Programming Interviews in Python](https://www.amazon.com/Elements-Programming-Interviews-Python-Insiders/dp/1537713949/)
- [Elements of Programming Interviews (Java version)](https://www.amazon.com/Elements-Programming-Interviews-Java-Insiders/dp/1517435803/)
- [Companion Project - Method Stub and Test Cases for Every Problem in the Book](https://github.com/gardncl/elements-of-programming-interviews)
- [Elements of Programming Interviews (Java version)](https://www.amazon.com/Elements-Programming-Interviews-Java-Insiders/dp/1517435803/) - [Companion Project - Method Stub and Test Cases for Every Problem in the Book](https://github.com/gardncl/elements-of-programming-interviews)
## Don't Make My Mistakes
@ -421,6 +424,7 @@ Make your own for free:
**I DON'T RECOMMEND using my flashcards.** There are too many and most of them are trivia that you don't need.
But if you don't want to listen to me, here you go:
- [My flash cards database (1200 cards)](https://github.com/jwasham/computer-science-flash-cards/blob/main/cards-jwasham.db):
- [My flash cards database (extreme - 1800 cards)](https://github.com/jwasham/computer-science-flash-cards/blob/main/cards-jwasham-extreme.db):
@ -448,6 +452,7 @@ Start doing coding interview questions while you're learning data structures and
You need to apply what you're learning to solve problems, or you'll forget. I made this mistake.
Once you've learned a topic, and feel somewhat comfortable with it, for example, **linked lists**:
1. Open one of the [coding interview books](#interview-prep-books) (or coding problem websites, listed below)
1. Do 2 or 3 questions regarding linked lists.
1. Move on to the next learning topic.
@ -481,9 +486,10 @@ Each day, take the next subject in the list, watch some videos about that subjec
of that data structure or algorithm in the language you chose for this course.
You can see my code here:
- [C](https://github.com/jwasham/practice-c)
- [C++](https://github.com/jwasham/practice-cpp)
- [Python](https://github.com/jwasham/practice-python)
- [C](https://github.com/jwasham/practice-c)
- [C++](https://github.com/jwasham/practice-cpp)
- [Python](https://github.com/jwasham/practice-python)
You don't need to memorize every algorithm. You just need to be able to understand it enough to be able to write your own implementation.
@ -494,6 +500,7 @@ You don't need to memorize every algorithm. You just need to be able to understa
[Then go back and read this.](#3-do-coding-interview-questions-while-youre-learning)
Why you need to practice doing programming problems:
- Problem recognition, and where the right data structures and algorithms fit in
- Gathering requirements for the problem
- Talking your way through the problem like you will in the interview
@ -520,10 +527,12 @@ Gets messy quickly. **I use a pencil and eraser.**
Don't forget your key coding interview books [here](#interview-prep-books).
Solving Problems:
- [How to Find a Solution](https://www.topcoder.com/thrive/articles/How%20To%20Find%20a%20Solution)
- [How to Dissect a Topcoder Problem Statement](https://www.topcoder.com/thrive/articles/How%20To%20Dissect%20a%20Topcoder%20Problem%20Statement%20Content)
Coding Interview Question Videos:
- [IDeserve (88 videos)](https://www.youtube.com/playlist?list=PLamzFoFxwoNjPfxzaWqs7cZGsPYy0x_gI)
- [Tushar Roy (5 playlists)](https://www.youtube.com/user/tusharroy2525/playlists?shelf_id=2&view=50&sort=dd)
- Super for walkthroughs of problem solutions
@ -533,6 +542,7 @@ Coding Interview Question Videos:
- [FisherCoder - LeetCode Solutions](https://youtube.com/FisherCoder)
Challenge/Practice sites:
- [LeetCode](https://leetcode.com/)
- My favorite coding problem site. It's worth the subscription money for the 1-2 months you'll likely be preparing.
- See Nick White and FisherCoder Videos above for code walk-throughs.
@ -558,7 +568,7 @@ But don't forget to do coding problems from above while you learn!
- 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)](https://www.youtube.com/watch?v=iOq5kSKqeR4)
- [x] [Harvard CS50 - Asymptotic Notation (video)](https://www.youtube.com/watch?v=iOq5kSKqeR4)
- [ ] [Big O Notations (general quick tutorial) (video)](https://www.youtube.com/watch?v=V6mKVRU1evU)
- [ ] [Big O Notation (and Omega and Theta) - best mathematical explanation (video)](https://www.youtube.com/watch?v=ei-A_wy5Yxw&index=2&list=PL1BaGV1cIH4UhkL8a9bJGG356covJ76qN)
- [ ] [Skiena (video)](https://www.youtube.com/watch?v=z1mkCe3kVUA)
@ -578,6 +588,7 @@ if you can identify the runtime complexity of different algorithms. It's a super
## Data Structures
- ### Arrays
- [ ] About Arrays:
- [Arrays CS50 Harvard University](https://www.youtube.com/watch?v=tI_tIZFyKBw&t=3009s)
- [Arrays (video)](https://www.coursera.org/lecture/data-structures/arrays-OsBSF)
@ -608,17 +619,17 @@ if you can identify the runtime complexity of different algorithms. It's a super
- 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)
- 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](https://www.youtube.com/watch?v=2T-A_GFuoTo&t=650s) - this builds the intuition.
- [ ] [Singly Linked Lists (video)](https://www.coursera.org/lecture/data-structures/singly-linked-lists-kHhgK)
- [ ] [CS 61B - Linked Lists 1 (video)](https://archive.org/details/ucberkeley_webcast_htzJdKoEmO0)
- [ ] [CS 61B - Linked Lists 2 (video)](https://archive.org/details/ucberkeley_webcast_-c4I3gFYe3w)
- [ ] [[Review] Linked lists in 4 minutes (video)](https://youtu.be/F8AbOfQwl1c)
- [ ] [C Code (video)](https://www.youtube.com/watch?v=QN6FPiD0Gzo)
- not the whole video, just portions about Node struct and memory allocation
- [ ] [C Code (video)](https://www.youtube.com/watch?v=QN6FPiD0Gzo) - not the whole video, just portions about Node struct and memory allocation
- [ ] Linked List vs Arrays:
- [Core Linked Lists Vs Arrays (video)](https://www.coursera.org/lecture/data-structures-optimizing-performance/core-linked-lists-vs-arrays-rjBs9)
- [In The Real World Linked Lists Vs Arrays (video)](https://www.coursera.org/lecture/data-structures-optimizing-performance/in-the-real-world-lists-vs-arrays-QUaUd)
@ -647,11 +658,13 @@ if you can identify the runtime complexity of different algorithms. It's a super
- No need to implement
- ### Stack
- [ ] [Stacks (video)](https://www.coursera.org/lecture/data-structures/stacks-UdKzQ)
- [ ] [[Review] Stacks in 3 minutes (video)](https://youtu.be/KcT3aVgrrpU)
- [ ] Will not implement. Implementing with the array is trivial
- ### Queue
- [ ] [Queue (video)](https://www.coursera.org/lecture/data-structures/queues-EShpq)
- [ ] [Circular buffer/FIFO](https://en.wikipedia.org/wiki/Circular_buffer)
- [ ] [[Review] Queues in 3 minutes (video)](https://youtu.be/D6gu-_tmEpQ)
@ -672,7 +685,9 @@ if you can identify the runtime complexity of different algorithms. It's a super
- empty: O(1) (linked list and array)
- ### Hash table
- [ ] Videos:
- [ ] [Hashing with Chaining (video)](https://www.youtube.com/watch?v=0M_kIqhwbFo&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=8)
- [ ] [Table Doubling, Karp-Rabin (video)](https://www.youtube.com/watch?v=BRO7mVIFt08&index=9&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb)
- [ ] [Open Addressing, Cryptographic Hashing (video)](https://www.youtube.com/watch?v=rvdJDijO2Ro&index=10&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb)
@ -683,6 +698,7 @@ if you can identify the runtime complexity of different algorithms. It's a super
- [ ] [[Review] Hash tables in 4 minutes (video)](https://youtu.be/knV86FlSXJ8)
- [ ] Online Courses:
- [ ] [Core Hash Tables (video)](https://www.coursera.org/lecture/data-structures-optimizing-performance/core-hash-tables-m7UuP)
- [ ] [Data Structures (video)](https://www.coursera.org/learn/data-structures/home/week/4)
- [ ] [Phone Book Problem (video)](https://www.coursera.org/lecture/data-structures/phone-book-problem-NYZZP)
@ -700,6 +716,7 @@ if you can identify the runtime complexity of different algorithms. It's a super
## More Knowledge
- ### Binary search
- [ ] [Binary Search (video)](https://www.youtube.com/watch?v=D5SrAga1pno)
- [ ] [Binary Search (video)](https://www.khanacademy.org/computing/computer-science/algorithms/binary-search/a/binary-search)
- [ ] [detail](https://www.topcoder.com/thrive/articles/Binary%20Search)
@ -713,7 +730,7 @@ if you can identify the runtime complexity of different algorithms. It's a super
- [ ] [Bits cheat sheet](https://github.com/jwasham/coding-interview-university/blob/main/extras/cheat%20sheets/bits-cheat-sheet.pdf)
- 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](https://en.wikipedia.org/wiki/Word_(computer_architecture))
- [ ] [words](<https://en.wikipedia.org/wiki/Word_(computer_architecture)>)
- [ ] Good intro:
[Bit Manipulation (video)](https://www.youtube.com/watch?v=7jkIUgLC29I)
- [ ] [C Programming Tutorial 2-10: Bitwise Operators (video)](https://www.youtube.com/watch?v=d0AwjSpNXR0)
@ -740,6 +757,7 @@ if you can identify the runtime complexity of different algorithms. It's a super
## Trees
- ### Trees - Intro
- [ ] [Intro to Trees (video)](https://www.coursera.org/lecture/data-structures/trees-95qda)
- [ ] [Tree Traversal (video)](https://www.coursera.org/lecture/data-structures/tree-traversal-fr51b)
- [ ] [BFS(breadth-first search) and DFS(depth-first search) (video)](https://www.youtube.com/watch?v=uWL6FJhq5fM)
@ -760,6 +778,7 @@ if you can identify the runtime complexity of different algorithms. It's a super
- [ ] [[Review] Tree Traversal (playlist) in 11 minutes (video)](https://www.youtube.com/playlist?list=PL9xmBV_5YoZO1JC2RgEi04nLy6D-rKk6b)
- ### Binary search trees: BSTs
- [ ] [Binary Search Tree Review (video)](https://www.youtube.com/watch?v=x6At0nzX92o&index=1&list=PLA5Lqm4uh9Bbq-E0ZnqTIa8LRaL77ica6)
- [ ] [Introduction (video)](https://www.coursera.org/learn/data-structures/lecture/E7cXP/introduction)
- [ ] [MIT (video)](https://www.youtube.com/watch?v=76dhtgZt38A&ab_channel=MITOpenCourseWare)
@ -789,7 +808,7 @@ if you can identify the runtime complexity of different algorithms. It's a super
- ### Heap / Priority Queue / Binary Heap
- visualized as a tree, but is usually linear in storage (array, linked list)
- [ ] [Heap](https://en.wikipedia.org/wiki/Heap_(data_structure))
- [ ] [Heap](<https://en.wikipedia.org/wiki/Heap_(data_structure)>)
- [ ] [Introduction (video)](https://www.coursera.org/lecture/data-structures/introduction-2OpTs)
- [ ] [Binary Trees (video)](https://www.coursera.org/learn/data-structures/lecture/GRV2q/binary-trees)
- [ ] [Tree Height Remark (video)](https://www.coursera.org/learn/data-structures/supplement/S5xxz/tree-height-remark)
@ -818,6 +837,7 @@ if you can identify the runtime complexity of different algorithms. It's a super
## 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?")
@ -832,6 +852,7 @@ if you can identify the runtime complexity of different algorithms. It's a super
- For heapsort, see the Heap data structure above. Heap sort is great, but not stable
- [ ] [Sedgewick - Mergesort (5 videos)](https://www.coursera.org/learn/algorithms-part1/home/week/3)
- [ ] [1. Mergesort](https://www.coursera.org/lecture/algorithms-part1/mergesort-ARWDq)
- [ ] [2. Bottom-up Mergesort](https://www.coursera.org/learn/algorithms-part1/lecture/PWNEl/bottom-up-mergesort)
- [ ] [3. Sorting Complexity](https://www.coursera.org/lecture/algorithms-part1/sorting-complexity-xAltF)
@ -839,12 +860,14 @@ if you can identify the runtime complexity of different algorithms. It's a super
- [ ] [5. Stability](https://www.coursera.org/learn/algorithms-part1/lecture/pvvLZ/stability)
- [ ] [Sedgewick - Quicksort (4 videos)](https://www.coursera.org/learn/algorithms-part1/home/week/3)
- [ ] [1. Quicksort](https://www.coursera.org/lecture/algorithms-part1/quicksort-vjvnC)
- [ ] [2. Selection](https://www.coursera.org/lecture/algorithms-part1/selection-UQxFT)
- [ ] [3. Duplicate Keys](https://www.coursera.org/lecture/algorithms-part1/duplicate-keys-XvjPd)
- [ ] [4. System Sorts](https://www.coursera.org/lecture/algorithms-part1/system-sorts-QBNZ7)
- [ ] UC Berkeley:
- [ ] [CS 61B Lecture 29: Sorting I (video)](https://archive.org/details/ucberkeley_webcast_EiUvYS2DT6I)
- [ ] [CS 61B Lecture 30: Sorting II (video)](https://archive.org/details/ucberkeley_webcast_2hTY3t80Qsk)
- [ ] [CS 61B Lecture 32: Sorting III (video)](https://archive.org/details/ucberkeley_webcast_Y6LOLpxg6Dc)
@ -864,11 +887,13 @@ if you can identify the runtime complexity of different algorithms. It's a super
- [ ] [Using output array (Python)](https://github.com/jwasham/practice-python/blob/master/merge_sort/merge_sort.py)
- [ ] [In-place (C++)](https://github.com/jwasham/practice-cpp/blob/master/merge_sort/merge_sort.cc)
- [ ] Quick sort code:
- [ ] [Implementation (C)](http://www.cs.yale.edu/homes/aspnes/classes/223/examples/randomization/quick.c)
- [ ] [Implementation (C)](https://github.com/jwasham/practice-c/blob/master/quick_sort/quick_sort.c)
- [ ] [Implementation (Python)](https://github.com/jwasham/practice-python/blob/master/quick_sort/quick_sort.py)
- [ ] [[Review] Sorting (playlist) in 18 minutes](https://www.youtube.com/playlist?list=PL9xmBV_5YoZOZSbGAXAPIq1BeUf4j20pl)
- [ ] [Quick sort in 4 minutes (video)](https://youtu.be/Hoixgm4-P4M)
- [ ] [Heap sort in 4 minutes (video)](https://youtu.be/2DmK_H7IdTo)
- [ ] [Merge sort in 3 minutes (video)](https://youtu.be/4VqmGXwpLqc)
@ -877,6 +902,7 @@ if you can identify the runtime complexity of different algorithms. It's a super
- [ ] [Insertion sort in 2 minutes (video)](https://youtu.be/JU767SDMDvA)
- [ ] 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
@ -904,6 +930,7 @@ If you need more detail on this subject, see the "Sorting" section in [Additiona
Graphs can be used to represent many problems in computer science, so this section is long, like trees and sorting.
- Notes:
- There are 4 basic ways to represent a graph in memory:
- objects and pointers
- adjacency matrix
@ -914,10 +941,12 @@ Graphs can be used to represent many problems in computer science, so this secti
- When asked a question, look for a graph-based solution first, then move on if none
- [ ] MIT(videos):
- [ ] [Breadth-First Search](https://www.youtube.com/watch?v=oFVYVzlvk9c&t=14s&ab_channel=MITOpenCourseWare)
- [ ] [Depth-First Search](https://www.youtube.com/watch?v=IBfWDYSffUU&t=32s&ab_channel=MITOpenCourseWare)
- [ ] Skiena Lectures - great intro:
- [ ] [CSE373 2020 - Lecture 10 - Graph Data Structures (video)](https://www.youtube.com/watch?v=Sjk0xqWWPCc&list=PLOtl7M3yp-DX6ic0HGT0PUX_wiNmkWkXx&index=10)
- [ ] [CSE373 2020 - Lecture 11 - Graph Traversal (video)](https://www.youtube.com/watch?v=ZTwjXj81NVY&list=PLOtl7M3yp-DX6ic0HGT0PUX_wiNmkWkXx&index=11)
- [ ] [CSE373 2020 - Lecture 12 - Depth First Search (video)](https://www.youtube.com/watch?v=KyordYB3BOs&list=PLOtl7M3yp-DX6ic0HGT0PUX_wiNmkWkXx&index=12)
@ -931,8 +960,8 @@ Graphs can be used to represent many problems in computer science, so this secti
- [ ] [6.006 Dijkstra (video)](https://www.youtube.com/watch?v=NSHizBK9JD8&t=1731s&ab_channel=MITOpenCourseWare)
- [ ] [6.006 Bellman-Ford (video)](https://www.youtube.com/watch?v=f9cVS_URPc0&ab_channel=MITOpenCourseWare)
- [ ] [6.006 Speeding Up Dijkstra (video)](https://www.youtube.com/watch?v=CHvQ3q_gJ7E&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=18)
- [ ] [Aduni: Graph Algorithms I - Topological Sorting, Minimum Spanning Trees, Prim's Algorithm - Lecture 6 (video)]( https://www.youtube.com/watch?v=i_AQT_XfvD8&index=6&list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm)
- [ ] [Aduni: Graph Algorithms II - DFS, BFS, Kruskal's Algorithm, Union Find Data Structure - Lecture 7 (video)]( https://www.youtube.com/watch?v=ufj5_bppBsA&list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm&index=7)
- [ ] [Aduni: Graph Algorithms I - Topological Sorting, Minimum Spanning Trees, Prim's Algorithm - Lecture 6 (video)](https://www.youtube.com/watch?v=i_AQT_XfvD8&index=6&list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm)
- [ ] [Aduni: Graph Algorithms II - DFS, BFS, Kruskal's Algorithm, Union Find Data Structure - Lecture 7 (video)](https://www.youtube.com/watch?v=ufj5_bppBsA&list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm&index=7)
- [ ] [Aduni: Graph Algorithms III: Shortest Path - Lecture 8 (video)](https://www.youtube.com/watch?v=DiedsPsMKXc&list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm&index=8)
- [ ] [Aduni: Graph Alg. IV: Intro to geometric algorithms - Lecture 9 (video)](https://www.youtube.com/watch?v=XIAQRlNkJAw&list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm&index=9)
- [ ] [CS 61B 2014: Weighted graphs (video)](https://archive.org/details/ucberkeley_webcast_zFbq8vOZ_0k)
@ -942,6 +971,7 @@ Graphs can be used to represent many problems in computer science, so this secti
- [ ] [[Review] Minimum Spanning Trees (playlist) in 4 minutes (video)](https://www.youtube.com/playlist?list=PL9xmBV_5YoZObEi3Hf6lmyW-CBfs7nkOV)
- Full Coursera Course:
- [ ] [Algorithms on Graphs (video)](https://www.coursera.org/learn/algorithms-on-graphs/home/welcome)
- I'll implement:
@ -963,6 +993,7 @@ Graphs can be used to represent many problems in computer science, so this secti
## Even More Knowledge
- ### Recursion
- [ ] Stanford lectures on recursion & backtracking:
- [ ] [Lecture 8 | Programming Abstractions (video)](https://www.youtube.com/watch?v=gl3emqCuueQ&list=PLFE6E58F856038C69&index=8)
- [ ] [Lecture 9 | Programming Abstractions (video)](https://www.youtube.com/watch?v=uFJhEPrbycQ&list=PLFE6E58F856038C69&index=9)
@ -974,9 +1005,11 @@ Graphs can be used to represent many problems in computer science, so this secti
- [ ] [Tail Recursion (video)](https://www.coursera.org/lecture/programming-languages/tail-recursion-YZic1)
- [ ] [5 Simple Steps for Solving Any Recursive Problem(video)](https://youtu.be/ngCos392W4w)
Backtracking Blueprint: [Java](https://leetcode.com/problems/combination-sum/discuss/16502/A-general-approach-to-backtracking-questions-in-Java-(Subsets-Permutations-Combination-Sum-Palindrome-Partitioning))
Backtracking Blueprint: [Java](<https://leetcode.com/problems/combination-sum/discuss/16502/A-general-approach-to-backtracking-questions-in-Java-(Subsets-Permutations-Combination-Sum-Palindrome-Partitioning)>)
[Python](https://leetcode.com/problems/combination-sum/discuss/429538/General-Backtracking-questions-solutions-in-Python-for-reference-%3A)
- ### 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.
@ -1004,6 +1037,7 @@ Graphs can be used to represent many problems in computer science, so this secti
- [ ] [Local pairwise sequence alignment (video)](https://www.coursera.org/learn/algorithmic-thinking-2/lecture/WnNau/local-pairwise-sequence-alignment)
- ### Design patterns
- [ ] [Quick UML review (video)](https://www.youtube.com/watch?v=3cmzqZzwNDM&list=PLGLfVvz_LVvQ5G-LdJ8RLqe-ndo7QITYc&index=3)
- [ ] Learn these patterns:
- [ ] strategy
@ -1029,6 +1063,7 @@ Graphs can be used to represent many problems in computer science, so this secti
- [Handy reference: 101 Design Patterns & Tips for Developers](https://sourcemaking.com/design-patterns-and-tips)
- ### Combinatorics (n choose k) & Probability
- [ ] [Math Skills: How to find Factorial, Permutation, and Combination (Choose) (video)](https://www.youtube.com/watch?v=8RRo6Ti9d0U)
- [ ] [Make School: Probability (video)](https://www.youtube.com/watch?v=sZkAAk9Wwa4)
- [ ] [Make School: More Probability and Markov Chains (video)](https://www.youtube.com/watch?v=dNaJg-mLobQ)
@ -1039,6 +1074,7 @@ Graphs can be used to represent many problems in computer science, so this secti
- [ ] [Probability Explained (video)](https://www.youtube.com/watch?v=uzkc-qNVoOk&list=PLC58778F28211FA19)
- ### NP, NP-Complete and Approximation Algorithms
- Know about the most famous classes of NP-complete problems, such as the 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.
@ -1067,7 +1103,9 @@ Graphs can be used to represent many problems in computer science, so this secti
- [ ] [Registers and RAM (video)](https://youtu.be/fpnE6UAfbtU)
- [ ] [The Central Processing Unit (CPU) (video)](https://youtu.be/FZGugFqdr60)
- [ ] [Instructions and Programs (video)](https://youtu.be/zltgXvg6r3k)
- ### Caches
- [ ] LRU cache:
- [ ] [The Magic of LRU Cache (100 Days of Google Dev) (video)](https://www.youtube.com/watch?v=R5ON3iwx78M)
- [ ] [Implementing LRU (video)](https://www.youtube.com/watch?v=bq6N7Ym81iI)
@ -1077,6 +1115,7 @@ Graphs can be used to represent many problems in computer science, so this secti
- [ ] [MIT 6.004 L16: Cache Issues (video)](https://www.youtube.com/watch?v=ajgC3-pyGlk&index=25&list=PLrRW1w6CGAcXbMtDFj205vALOGmiRc82-)
- ### Processes and Threads
- [ ] Computer Science 162 - Operating Systems (25 videos):
- for processes and threads see videos 1-11
- [Operating Systems and System Programming (video)](https://archive.org/details/ucberkeley-webcast-PL-XXv-cvA_iBDyz-ba4yDskqMDY6A1w_c)
@ -1114,6 +1153,7 @@ Graphs can be used to represent many problems in computer science, so this secti
- [ ] [Mutex in Python](https://www.youtube.com/watch?v=0zaPs8OtyKY)
- ### Testing
- To cover:
- how unit testing works
- what are mock objects
@ -1129,6 +1169,7 @@ Graphs can be used to represent many problems in computer science, so this secti
- [ ] [How to write tests](http://jasonpolites.github.io/tao-of-testing/ch4-1.1.html)
- ### String searching & manipulations
- [ ] [Sedgewick - Suffix Arrays (video)](https://www.coursera.org/learn/algorithms-part2/lecture/TH18W/suffix-arrays)
- [ ] [Sedgewick - Substring Search (videos)](https://www.coursera.org/learn/algorithms-part2/home/week/4)
- [ ] [1. Introduction to Substring Search](https://www.coursera.org/lecture/algorithms-part2/introduction-to-substring-search-n3ZpG)
@ -1141,6 +1182,7 @@ Graphs can be used to represent many problems in computer science, so this secti
If you need more detail on this subject, see the "String Matching" section in [Additional Detail on Some Subjects](#additional-detail-on-some-subjects).
- ### Tries
- Note there are different kinds of tries. Some have prefixes, some don't, and some use strings instead of bits
to track the path
- I read through the code, but will not implement
@ -1159,13 +1201,16 @@ Graphs can be used to represent many problems in computer science, so this secti
- [ ] [MIT, Advanced Data Structures, Strings (can get pretty obscure about halfway through) (video)](https://www.youtube.com/watch?v=NinWEPPrkDQ&index=16&list=PLUl4u3cNGP61hsJNdULdudlRL493b-XZf)
- ### Floating Point Numbers
- [ ] simple 8-bit: [Representation of Floating Point Numbers - 1 (video - there is an error in calculations - see video description)](https://www.youtube.com/watch?v=ji3SfClm8TU)
- ### Unicode
- [ ] [The Absolute Minimum Every Software Developer Absolutely, Positively Must Know About Unicode and Character Sets]( http://www.joelonsoftware.com/articles/Unicode.html)
- [ ] [The Absolute Minimum Every Software Developer Absolutely, Positively Must Know About Unicode and Character Sets](http://www.joelonsoftware.com/articles/Unicode.html)
- [ ] [What Every Programmer Absolutely, Positively Needs To Know About Encodings And Character Sets To Work With Text](http://kunststube.net/encoding/)
- ### Endianness
- [ ] [Big And Little Endian](https://web.archive.org/web/20180107141940/http://www.cs.umd.edu:80/class/sum2003/cmsc311/Notes/Data/endian.html)
- [ ] [Big Endian Vs Little Endian (video)](https://www.youtube.com/watch?v=JrNF0KRAlyo)
- [ ] [Big And Little Endian Inside/Out (video)](https://www.youtube.com/watch?v=oBSuXP-1Tc0)
@ -1240,6 +1285,7 @@ Graphs can be used to represent many problems in computer science, so this secti
- The crash course for LeetCode. Covers all the patterns condensed from thousands of questions.
Mock Interviews:
- [Gainlo.co: Mock interviewers from big companies](http://www.gainlo.co/#!/) - I used this and it helped me relax for the phone screen and on-site interview
- [Pramp: Mock interviews from/with peers](https://www.pramp.com/) - a peer-to-peer model to practice interviews
- [interviewing.io: Practice mock interview with senior engineers](https://interviewing.io) - anonymous algorithmic/systems design interviews with senior engineers from FAANG anonymously
@ -1334,7 +1380,7 @@ You're never really done.
- This book is better as an algorithm reference, and not something you read cover to cover.
- Can rent it on Kindle
- Answers:
- [Solutions](https://web.archive.org/web/20150404194210/http://www.algorithm.cs.sunysb.edu/algowiki/index.php/The_Algorithms_Design_Manual_(Second_Edition))
- [Solutions](<https://web.archive.org/web/20150404194210/http://www.algorithm.cs.sunysb.edu/algowiki/index.php/The_Algorithms_Design_Manual_(Second_Edition)>)
- [Errata](http://www3.cs.stonybrook.edu/~skiena/algorist/book/errata)
- [Algorithm](http://jeffe.cs.illinois.edu/teaching/algorithms/) (Jeff Erickson)
- [Write Great Code: Volume 1: Understanding the Machine](https://www.amazon.com/Write-Great-Code-Understanding-Machine/dp/1593270038)
@ -1420,7 +1466,7 @@ You're never really done.
- [ ] [What Led Amazon to its Own Microservices Architecture](http://thenewstack.io/led-amazon-microservices-architecture/)
- [ ] [To Compress Or Not To Compress, That Was Uber's Question](https://eng.uber.com/trip-data-squeeze/)
- [ ] [When Should Approximate Query Processing Be Used?](http://highscalability.com/blog/2016/2/25/when-should-approximate-query-processing-be-used.html)
- [ ] [Google's Transition From Single Datacenter To Failover, To A Native Multihomed Architecture]( http://highscalability.com/blog/2016/2/23/googles-transition-from-single-datacenter-to-failover-to-a-n.html)
- [ ] [Google's Transition From Single Datacenter To Failover, To A Native Multihomed Architecture](http://highscalability.com/blog/2016/2/23/googles-transition-from-single-datacenter-to-failover-to-a-n.html)
- [ ] [The Image Optimization Technology That Serves Millions Of Requests Per Day](http://highscalability.com/blog/2016/6/15/the-image-optimization-technology-that-serves-millions-of-re.html)
- [ ] [A Patreon Architecture Short](http://highscalability.com/blog/2016/2/1/a-patreon-architecture-short.html)
- [ ] [Tinder: How Does One Of The Largest Recommendation Engines Decide Who You'll See Next?](http://highscalability.com/blog/2016/1/27/tinder-how-does-one-of-the-largest-recommendation-engines-de.html)
@ -1474,12 +1520,14 @@ You're never really done.
technologies and algorithms, so you'll have a bigger toolbox.
- ### Compilers
- [How a Compiler Works in ~1 minute (video)](https://www.youtube.com/watch?v=IhC7sdYe-Jg)
- [Harvard CS50 - Compilers (video)](https://www.youtube.com/watch?v=CSZLNYF4Klo)
- [C++ (video)](https://www.youtube.com/watch?v=twodd1KFfGk)
- [Understanding Compiler Optimization (C++) (video)](https://www.youtube.com/watch?v=FnGCDLhaxKU)
- ### Emacs and vi(m)
- Familiarize yourself with a UNIX-based code editor
- vi(m):
- [Editing With Vim 01 - Installation, Setup, and The Modes (video)](https://www.youtube.com/watch?v=5givLEMcINQ&index=1&list=PL13bz4SHGmRxlZVmWQ9DvXo1fEg4UdGkr)
@ -1502,6 +1550,7 @@ You're never really done.
- [The Absolute Beginner's Guide to Emacs (notes by David Wilson)](https://systemcrafters.net/emacs-essentials/absolute-beginners-guide-to-emacs/)
- ### Unix command line tools
- I filled in the list below from good tools.
- bash
- cat
@ -1516,6 +1565,7 @@ You're never really done.
- [tcpdump](https://danielmiessler.com/study/tcpdump/)
- ### Information theory (videos)
- [Khan Academy](https://www.khanacademy.org/computing/computer-science/informationtheory)
- More about Markov processes:
- [Core Markov Text Generation](https://www.coursera.org/learn/data-structures-optimizing-performance/lecture/waxgx/core-markov-text-generation)
@ -1524,6 +1574,7 @@ You're never really done.
- See more in the MIT 6.050J Information and Entropy series below
- ### Parity & Hamming Code (videos)
- [Intro](https://www.youtube.com/watch?v=q-3BctoUpHE)
- [Parity](https://www.youtube.com/watch?v=DdMcAUlxh1M)
- Hamming Code:
@ -1532,11 +1583,13 @@ You're never really done.
- [Error Checking](https://www.youtube.com/watch?v=wbH2VxzmoZk)
- ### Entropy
- Also see the videos below
- Make sure to watch information theory videos first
- [Information Theory, Claude Shannon, Entropy, Redundancy, Data Compression & Bits (video)](https://youtu.be/JnJq3Py0dyM?t=176)
- ### Cryptography
- Also see the videos below
- Make sure to watch information theory videos first
- [Khan Academy Series](https://www.khanacademy.org/computing/computer-science/cryptography)
@ -1544,6 +1597,7 @@ You're never really done.
- [Cryptography: Encryption](https://www.youtube.com/watch?v=9TNI2wHmaeI&index=31&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp)
- ### Compression
- Make sure to watch information theory videos first
- Computerphile (videos):
- [Compression](https://www.youtube.com/watch?v=Lto-ajuqW3w)
@ -1556,6 +1610,7 @@ You're never really done.
- [(optional) Google Developers Live: GZIP is not enough!](https://www.youtube.com/watch?v=whGwm0Lky2s)
- ### Computer Security
- [MIT (23 videos)](https://www.youtube.com/playlist?list=PLUl4u3cNGP62K2DjQLRxDNRi0z2IRWnNh)
- [Introduction, Threat Models](https://www.youtube.com/watch?v=GqmQg-cszw4&index=1&list=PLUl4u3cNGP62K2DjQLRxDNRi0z2IRWnNh)
- [Control Hijacking Attacks](https://www.youtube.com/watch?v=6bwzNg5qQ0o&list=PLUl4u3cNGP62K2DjQLRxDNRi0z2IRWnNh&index=2)
@ -1571,15 +1626,18 @@ You're never really done.
- [Side-Channel Attacks](https://www.youtube.com/watch?v=PuVMkSEcPiI&index=15&list=PLUl4u3cNGP62K2DjQLRxDNRi0z2IRWnNh)
- ### Garbage collection
- [GC in Python (video)](https://www.youtube.com/watch?v=iHVs_HkjdmI)
- [Deep Dive Java: Garbage Collection is Good!](https://www.infoq.com/presentations/garbage-collection-benefits)
- [Deep Dive Python: Garbage Collection in CPython (video)](https://www.youtube.com/watch?v=P-8Z0-MhdQs&list=PLdzf4Clw0VbOEWOS_sLhT_9zaiQDrS5AR&index=3)
- ### Parallel Programming
- [Coursera (Scala)](https://www.coursera.org/learn/parprog1/home/week/1)
- [Efficient Python for High-Performance Parallel Computing (video)](https://www.youtube.com/watch?v=uY85GkaYzBk)
- ### Messaging, Serialization, and Queueing Systems
- [Thrift](https://thrift.apache.org/)
- [Tutorial](http://thrift-tutorial.readthedocs.io/en/latest/intro.html)
- [Protocol Buffers](https://developers.google.com/protocol-buffers/)
@ -1601,11 +1659,13 @@ You're never really done.
- [MessagePack](http://msgpack.org/index.html)
- [Avro](https://avro.apache.org/)
- ### A*
- ### A\*
- [A Search Algorithm](https://en.wikipedia.org/wiki/A*_search_algorithm)
- [A* Pathfinding (E01: algorithm explanation) (video)](https://www.youtube.com/watch?v=-L-WgKMFuhE)
- [A\* Pathfinding (E01: algorithm explanation) (video)](https://www.youtube.com/watch?v=-L-WgKMFuhE)
- ### Fast Fourier Transform
- [An Interactive Guide To The Fourier Transform](https://betterexplained.com/articles/an-interactive-guide-to-the-fourier-transform/)
- [What is a Fourier transform? What is it used for?](http://www.askamathematician.com/2012/09/q-what-is-a-fourier-transform-what-is-it-used-for/)
- [What is the Fourier Transform? (video)](https://www.youtube.com/watch?v=Xxut2PN-V8Q)
@ -1613,6 +1673,7 @@ You're never really done.
- [Understanding The FFT](http://jakevdp.github.io/blog/2013/08/28/understanding-the-fft/)
- ### Bloom Filter
- Given a Bloom filter with m bits and k hashing functions, both insertion and membership testing are O(k)
- [Bloom Filters (video)](https://www.youtube.com/watch?v=-SuTGoFYjZs)
- [Bloom Filters | Mining of Massive Datasets | Stanford University (video)](https://www.youtube.com/watch?v=qBTdukbzc78)
@ -1620,21 +1681,26 @@ You're never really done.
- [How To Write A Bloom Filter App](http://blog.michaelschmatz.com/2016/04/11/how-to-write-a-bloom-filter-cpp/)
- ### HyperLogLog
- [How To Count A Billion Distinct Objects Using Only 1.5KB Of Memory](http://highscalability.com/blog/2012/4/5/big-data-counting-how-to-count-a-billion-distinct-objects-us.html)
- ### Locality-Sensitive Hashing
- Used to determine the similarity of documents
- The opposite of MD5 or SHA which are used to determine if 2 documents/strings are exactly the same
- [Simhashing (hopefully) made simple](http://ferd.ca/simhashing-hopefully-made-simple.html)
- ### van Emde Boas Trees
- [Divide & Conquer: van Emde Boas Trees (video)](https://www.youtube.com/watch?v=hmReJCupbNU&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp&index=6)
- [MIT Lecture Notes](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-046j-design-and-analysis-of-algorithms-spring-2012/lecture-notes/MIT6_046JS12_lec15.pdf)
- ### Augmented Data Structures
- [CS 61B Lecture 39: Augmenting Data Structures](https://archive.org/details/ucberkeley_webcast_zksIj9O8_jc)
- ### Balanced search trees
- Know at least one type of balanced binary tree (and know how it's implemented):
- "Among balanced search trees, AVL and 2/3 trees are now passé and red-black trees seem to be more popular.
A particularly interesting self-organizing data structure is the splay tree, which uses rotations
@ -1649,6 +1715,7 @@ You're never really done.
- [Self-balancing binary search tree](https://en.wikipedia.org/wiki/Self-balancing_binary_search_tree)
- **AVL trees**
- In practice:
From what I can tell, these aren't used much in practice, but I could see where they would be:
The AVL tree is another structure supporting O(log n) search, insertion, and removal. It is more rigidly
@ -1662,6 +1729,7 @@ You're never really done.
- [[Review] AVL Trees (playlist) in 19 minutes (video)](https://www.youtube.com/playlist?list=PL9xmBV_5YoZOUFgdIeOPuH6cfSnNRMau-)
- **Splay trees**
- In practice:
Splay trees are typically used in the implementation of caches, memory allocators, routers, garbage collectors,
data compression, ropes (replacement of string used for long text strings), in Windows NT (in the virtual memory,
@ -1672,6 +1740,7 @@ You're never really done.
- [Video](https://www.youtube.com/watch?v=QnPl_Y6EqMo)
- **Red/black trees**
- These are a translation of a 2-3 tree (see below).
- In practice:
Red–black trees offer worst-case guarantees for insertion time, deletion time, and search time.
@ -1688,6 +1757,7 @@ You're never really done.
- [[Review] Red-Black Trees (playlist) in 30 minutes (video)](https://www.youtube.com/playlist?list=PL9xmBV_5YoZNqDI8qfOZgzbqahCUmUEin)
- **2-3 search trees**
- In practice:
2-3 trees have faster inserts at the expense of slower searches (since height is more compared to AVL trees).
- You would use 2-3 trees very rarely because its implementation involves different types of nodes. Instead, people use Red-Black trees.
@ -1696,6 +1766,7 @@ You're never really done.
- [2-3 Trees (student recitation) (video)](https://www.youtube.com/watch?v=TOb1tuEZ2X4&index=5&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp)
- **2-3-4 Trees (aka 2-4 trees)**
- In practice:
For every 2-4 trees, there are corresponding red–black trees with data elements in the same order. The insertion and deletion
operations on 2-4 trees are also equivalent to color-flipping and rotations in red–black trees. This makes 2-4 trees an
@ -1706,6 +1777,7 @@ You're never really done.
- [Top Down 234-Trees (video)](https://www.youtube.com/watch?v=2679VQ26Fp4&list=PLA5Lqm4uh9Bbq-E0ZnqTIa8LRaL77ica6&index=5)
- **N-ary (K-ary, M-ary) trees**
- note: the N or K is the branching factor (max branches)
- binary trees are a 2-ary tree, with branching factor = 2
- 2-3 trees are 3-ary
@ -1723,48 +1795,53 @@ You're never really done.
- [Introduction to B-Trees (video)](https://www.youtube.com/watch?v=I22wEC1tTGo&list=PLA5Lqm4uh9Bbq-E0ZnqTIa8LRaL77ica6&index=6)
- [B-Tree Definition and Insertion (video)](https://www.youtube.com/watch?v=s3bCdZGrgpA&index=7&list=PLA5Lqm4uh9Bbq-E0ZnqTIa8LRaL77ica6)
- [B-Tree Deletion (video)](https://www.youtube.com/watch?v=svfnVhJOfMc&index=8&list=PLA5Lqm4uh9Bbq-E0ZnqTIa8LRaL77ica6)
- [MIT 6.851 - Memory Hierarchy Models (video)](https://www.youtube.com/watch?v=V3omVLzI0WE&index=7&list=PLUl4u3cNGP61hsJNdULdudlRL493b-XZf)
- covers cache-oblivious B-Trees, very interesting data structures
- the first 37 minutes are very technical, and may be skipped (B is block size, cache line size)
- [MIT 6.851 - Memory Hierarchy Models (video)](https://www.youtube.com/watch?v=V3omVLzI0WE&index=7&list=PLUl4u3cNGP61hsJNdULdudlRL493b-XZf) - covers cache-oblivious B-Trees, very interesting data structures - the first 37 minutes are very technical, and may be skipped (B is block size, cache line size)
- [[Review] B-Trees (playlist) in 26 minutes (video)](https://www.youtube.com/playlist?list=PL9xmBV_5YoZNFPPv98DjTdD9X6UI9KMHz)
- ### k-D Trees
- Great for finding a number of points in a rectangle or higher-dimensional object
- A good fit for k-nearest neighbors
- [kNN K-d tree algorithm (video)](https://www.youtube.com/watch?v=Y4ZgLlDfKDg)
- ### Skip lists
- "These are somewhat of a cult data structure" - Skiena
- [Randomization: Skip Lists (video)](https://www.youtube.com/watch?v=2g9OSRKJuzM&index=10&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp)
- [For animations and a little more detail](https://en.wikipedia.org/wiki/Skip_list)
- ### Network Flows
- [Ford-Fulkerson in 5 minutes — Step by step example (video)](https://www.youtube.com/watch?v=Tl90tNtKvxs)
- [Ford-Fulkerson Algorithm (video)](https://www.youtube.com/watch?v=v1VgJmkEJW0)
- [Network Flows (video)](https://www.youtube.com/watch?v=2vhN4Ice5jI)
- ### Disjoint Sets & Union Find
- [UCB 61B - Disjoint Sets; Sorting & selection (video)](https://archive.org/details/ucberkeley_webcast_MAEGXTwmUsI)
- [Sedgewick Algorithms - Union-Find (6 videos)](https://www.coursera.org/learn/algorithms-part1/home/week/1)
- ### Math for Fast Processing
- [Integer Arithmetic, Karatsuba Multiplication (video)](https://www.youtube.com/watch?v=eCaXlAaN2uE&index=11&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb)
- [The Chinese Remainder Theorem (used in cryptography) (video)](https://www.youtube.com/watch?v=ru7mWZJlRQg)
- ### Treap
- Combination of a binary search tree and a heap
- [Treap](https://en.wikipedia.org/wiki/Treap)
- [Data Structures: Treaps explained (video)](https://www.youtube.com/watch?v=6podLUYinH8)
- [Applications in set operations](https://www.cs.cmu.edu/~scandal/papers/treaps-spaa98.pdf)
- ### Linear Programming (videos)
- [Linear Programming](https://www.youtube.com/watch?v=M4K6HYLHREQ)
- [Finding minimum cost](https://www.youtube.com/watch?v=2ACJ9ewUC6U)
- [Finding maximum value](https://www.youtube.com/watch?v=8AA_81xI3ik)
- [Solve Linear Equations with Python - Simplex Algorithm](https://www.youtube.com/watch?v=44pAWI7v5Zk)
- ### Geometry, Convex hull (videos)
- [Graph Alg. IV: Intro to geometric algorithms - Lecture 9](https://youtu.be/XIAQRlNkJAw?list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm&t=3164)
- [Geometric Algorithms: Graham & Jarvis - Lecture 10](https://www.youtube.com/watch?v=J5aJEcOr6Eo&index=10&list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm)
- [Divide & Conquer: Convex Hull, Median Finding](https://www.youtube.com/watch?v=EzeYI7p9MjU&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp&index=2)
@ -1783,6 +1860,7 @@ You're never really done.
You want to get hired in this century, right?
- **SOLID**
- [ ] [Bob Martin SOLID Principles of Object Oriented and Agile Design (video)](https://www.youtube.com/watch?v=TMuno5RZNeE)
- [ ] S - [Single Responsibility Principle](http://www.oodesign.com/single-responsibility-principle.html) | [Single responsibility to each Object](http://www.javacodegeeks.com/2011/11/solid-single-responsibility-principle.html)
- [more flavor](https://docs.google.com/open?id=0ByOwmqah_nuGNHEtcU5OekdDMkk)
@ -1797,8 +1875,8 @@ You're never really done.
- [Why Is The Dependency Inversion Principle And Why Is It Important](http://stackoverflow.com/questions/62539/what-is-the-dependency-inversion-principle-and-why-is-it-important)
- [more flavor](http://docs.google.com/a/cleancoder.com/viewer?a=v&pid=explorer&chrome=true&srcid=0BwhCYaYDn8EgMjdlMWIzNGUtZTQ0NC00ZjQ5LTkwYzQtZjRhMDRlNTQ3ZGMz&hl=en)
- **Union-Find**
- [Overview](https://www.coursera.org/learn/data-structures/lecture/JssSY/overview)
- [Naive Implementation](https://www.coursera.org/learn/data-structures/lecture/EM5D0/naive-implementations)
- [Trees](https://www.coursera.org/learn/data-structures/lecture/Mxu0w/trees)
@ -1807,6 +1885,7 @@ You're never really done.
- [Analysis Options](https://www.coursera.org/learn/data-structures/lecture/GQQLN/analysis-optional)
- **More Dynamic Programming** (videos)
- [6.006: Dynamic Programming I: Fibonacci, Shortest Paths](https://www.youtube.com/watch?v=r4-cftqTcdI&ab_channel=MITOpenCourseWare)
- [6.006: Dynamic Programming II: Text Justification, Blackjack](https://www.youtube.com/watch?v=KLBCUx1is2c&ab_channel=MITOpenCourseWare)
- [6.006: DP III: Parenthesization, Edit Distance, Knapsack](https://www.youtube.com/watch?v=TDo3r5M1LNo&ab_channel=MITOpenCourseWare)
@ -1816,10 +1895,12 @@ You're never really done.
- [6.046: Dynamic Programming (student recitation)](https://www.youtube.com/watch?v=krZI60lKPek&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp&index=12)
- **Advanced Graph Processing** (videos)
- [Synchronous Distributed Algorithms: Symmetry-Breaking. Shortest-Paths Spanning Trees](https://www.youtube.com/watch?v=mUBmcbbJNf4&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp&index=27)
- [Asynchronous Distributed Algorithms: Shortest-Paths Spanning Trees](https://www.youtube.com/watch?v=kQ-UQAzcnzA&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp&index=28)
- MIT **Probability** (mathy, and go slowly, which is good for mathy things) (videos):
- [MIT 6.042J - Probability Introduction](https://www.youtube.com/watch?v=SmFwFdESMHI&index=18&list=PLB7540DEDD482705B)
- [MIT 6.042J - Conditional Probability](https://www.youtube.com/watch?v=E6FbvM-FGZ8&index=19&list=PLB7540DEDD482705B)
- [MIT 6.042J - Independence](https://www.youtube.com/watch?v=l1BCv3qqW4A&index=20&list=PLB7540DEDD482705B)
@ -1832,6 +1913,7 @@ You're never really done.
- [Simonson: Approximation Algorithms (video)](https://www.youtube.com/watch?v=oDniZCmNmNw&list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm&index=19)
- **String Matching**
- Rabin-Karp (videos):
- [Rabin Karps Algorithm](https://www.coursera.org/lecture/data-structures/rabin-karps-algorithm-c0Qkw)
- [Precomputing](https://www.coursera.org/learn/data-structures/lecture/nYrc8/optimization-precomputation)
@ -1911,6 +1993,7 @@ Sit back and enjoy.
- [Stanford: Programming Paradigms (27 videos)](https://www.youtube.com/playlist?list=PL9D558D49CA734A02)
- [Introduction to Cryptography by Christof Paar](https://www.youtube.com/playlist?list=PL6N5qY2nvvJE8X75VkXglSrVhLv1tVcfy)
- [Course Website along with Slides and Problem Sets](http://www.crypto-textbook.com/)
- [Mining Massive Datasets - Stanford University (94 videos)](https://www.youtube.com/playlist?list=PLLssT5z_DsK9JDLcT8T62VtzwyW9LNepV)
@ -1926,7 +2009,6 @@ Sit back and enjoy.
- [Multiple Algorithms implementation by Princeton University](https://algs4.cs.princeton.edu/code)
## Papers
- [Love classic papers?](https://www.cs.cmu.edu/~crary/819-f09/)
@ -1934,7 +2016,7 @@ Sit back and enjoy.
- [implemented in Go](https://godoc.org/github.com/thomas11/csp)
- [2003: The Google File System](http://static.googleusercontent.com/media/research.google.com/en//archive/gfs-sosp2003.pdf)
- replaced by Colossus in 2012
- [2004: MapReduce: Simplified Data Processing on Large Clusters]( http://static.googleusercontent.com/media/research.google.com/en//archive/mapreduce-osdi04.pdf)
- [2004: MapReduce: Simplified Data Processing on Large Clusters](http://static.googleusercontent.com/media/research.google.com/en//archive/mapreduce-osdi04.pdf)
- mostly replaced by Cloud Dataflow?
- [2006: Bigtable: A Distributed Storage System for Structured Data](https://static.googleusercontent.com/media/research.google.com/en//archive/bigtable-osdi06.pdf)
- [2006: The Chubby Lock Service for Loosely-Coupled Distributed Systems](https://research.google.com/archive/chubby-osdi06.pdf)