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@ -10,7 +10,7 @@
> The items listed here will prepare you well for a technical interview at just about any software company, > 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. > including the giants: Amazon, Facebook, Google, and Microsoft.
> >
> *Best of luck to you!* > _Best of luck to you!_
<details> <details>
<summary>Translations:</summary> <summary>Translations:</summary>
@ -149,7 +149,7 @@ If you want to be a reliability engineer or operations engineer, study more from
- [Garbage collection](#garbage-collection) - [Garbage collection](#garbage-collection)
- [Parallel Programming](#parallel-programming) - [Parallel Programming](#parallel-programming)
- [Messaging, Serialization, and Queueing Systems](#messaging-serialization-and-queueing-systems) - [Messaging, Serialization, and Queueing Systems](#messaging-serialization-and-queueing-systems)
- [A*](#a) - [A\*](#a)
- [Fast Fourier Transform](#fast-fourier-transform) - [Fast Fourier Transform](#fast-fourier-transform)
- [Bloom Filter](#bloom-filter) - [Bloom Filter](#bloom-filter)
- [HyperLogLog](#hyperloglog) - [HyperLogLog](#hyperloglog)
@ -200,7 +200,6 @@ I'm using Github's special markdown flavor, including tasks lists to check progr
**Create a new branch so you can check items like this, just put an x in the brackets: [x]** **Create a new branch so you can check items like this, just put an x in the brackets: [x]**
Fork a branch and follow the commands below Fork a branch and follow the commands below
`git checkout -b progress` `git checkout -b progress`
@ -221,7 +220,6 @@ I'm using Github's special markdown flavor, including tasks lists to check progr
[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)
## Don't feel you aren't smart enough ## Don't feel you aren't smart enough
- Successful software engineers are smart, but many have an insecurity that they aren't smart enough. - Successful software engineers are smart, but many have an insecurity that they aren't smart enough.
@ -236,7 +234,6 @@ Sometimes the classes are not in session so you have to wait a couple of months,
I'd appreciate your help to add free and always-available public sources, such as YouTube videos to accompany the online course videos. I'd appreciate your help to add free and always-available public sources, such as YouTube videos to accompany the online course videos.
I like using university lectures. I like using university lectures.
## Interview Process & General Interview Prep ## Interview Process & General Interview Prep
- [ ] [ABC: Always Be Coding](https://medium.com/always-be-coding/abc-always-be-coding-d5f8051afce2#.4heg8zvm4) - [ ] [ABC: Always Be Coding](https://medium.com/always-be-coding/abc-always-be-coding-d5f8051afce2#.4heg8zvm4)
@ -279,6 +276,7 @@ Here is an article I wrote about choosing a language for the interview: [Pick On
You need to be very comfortable in the language and be knowledgeable. You need to be very comfortable in the language and be knowledgeable.
Read more about choices: Read more about choices:
- http://www.byte-by-byte.com/choose-the-right-language-for-your-coding-interview/ - http://www.byte-by-byte.com/choose-the-right-language-for-your-coding-interview/
- http://blog.codingforinterviews.com/best-programming-language-jobs/ - http://blog.codingforinterviews.com/best-programming-language-jobs/
@ -351,7 +349,6 @@ OR:
- Pythonic code - Pythonic code
- my glowing book report: https://startupnextdoor.com/book-report-data-structures-and-algorithms-in-python/ - my glowing book report: https://startupnextdoor.com/book-report-data-structures-and-algorithms-in-python/
## Before you Get Started ## Before you Get Started
This list grew over many months, and yes, it kind of got out of hand. This list grew over many months, and yes, it kind of got out of hand.
@ -389,7 +386,7 @@ same card and answer it several times correctly before you really know it. Repet
your brain. your brain.
An alternative to using my flashcard site is [Anki](http://ankisrs.net/), which has been recommended to me numerous times. It uses a repetition system to help you remember. An alternative to using my flashcard site is [Anki](http://ankisrs.net/), which has been recommended to me numerous times. It uses a repetition system to help you remember.
It's user-friendly, available on all platforms and has a cloud sync system. It costs $25 on iOS but is free on other platforms. It's user-friendly, available on all platforms and has a cloud sync system. It costs \$25 on iOS but is free on other platforms.
My flashcard database in Anki format: https://ankiweb.net/shared/info/25173560 (thanks [@xiewenya](https://github.com/xiewenya)) My flashcard database in Anki format: https://ankiweb.net/shared/info/25173560 (thanks [@xiewenya](https://github.com/xiewenya))
@ -426,7 +423,8 @@ These are prevalent technologies but not part of this study plan:
Some subjects take one day, and some will take multiple days. Some are just learning with nothing to implement. Some subjects take one day, and some will take multiple days. Some are just learning with nothing to implement.
Each day I take one subject from the list below, watch videos about that subject, and write an implementation in: Each day I take one subject from the list below, watch videos about that subject, and write an implementation in:
- C - using structs and functions that take a struct * and something else as args.
- C - using structs and functions that take a struct \* and something else as args.
- C++ - without using built-in types - C++ - without using built-in types
- C++ - using built-in types, like STL's std::list for a linked list - C++ - using built-in types, like STL's std::list for a linked list
- Python - using built-in types (to keep practicing Python) - Python - using built-in types (to keep practicing Python)
@ -436,6 +434,7 @@ Each day I take one subject from the list below, watch videos about that subject
You don't need all these. You need only [one language for the interview](#pick-one-language-for-the-interview). You don't need all these. You need only [one language for the interview](#pick-one-language-for-the-interview).
Why code in all of these? Why code in all of these?
- Practice, practice, practice, until I'm sick of it, and can do it with no problem (some have many edge cases and bookkeeping details to remember) - Practice, practice, practice, until I'm sick of it, and can do it with no problem (some have many edge cases and bookkeeping details to remember)
- Work within the raw constraints (allocating/freeing memory without help of garbage collection (except Python or Java)) - Work within the raw constraints (allocating/freeing memory without help of garbage collection (except Python or Java))
- Make use of built-in types so I have experience using the built-in tools for real-world use (not going to write my own linked list implementation in production) - Make use of built-in types so I have experience using the built-in tools for real-world use (not going to write my own linked list implementation in production)
@ -443,9 +442,10 @@ Why code in all of these?
I may not have time to do all of these for every subject, but I'll try. I may not have time to do all of these for every subject, but I'll try.
You can see my code here: You can see my code here:
- [C](https://github.com/jwasham/practice-c)
- [C++](https://github.com/jwasham/practice-cpp) - [C](https://github.com/jwasham/practice-c)
- [Python](https://github.com/jwasham/practice-python) - [C++](https://github.com/jwasham/practice-cpp)
- [Python](https://github.com/jwasham/practice-python)
You don't need to memorize the guts of every algorithm. You don't need to memorize the guts of every algorithm.
@ -454,7 +454,8 @@ Write code on a whiteboard or paper, not a computer. Test with some sample input
## Prerequisite Knowledge ## Prerequisite Knowledge
- [ ] **Learn C** - [ ] **Learn C**
- 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.
- [ ] [C Programming Language, Vol 2](https://www.amazon.com/Programming-Language-Brian-W-Kernighan/dp/0131103628) - [ ] [C Programming Language, Vol 2](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 - 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'll quickly get proficient. Understanding C helps you understand how programs and memory work.
@ -493,6 +494,7 @@ Write code on a whiteboard or paper, not a computer. Test with some sample input
## Data Structures ## Data Structures
- ### Arrays - ### Arrays
- Implement an automatically resizing vector. - Implement an automatically resizing vector.
- [ ] Description: - [ ] Description:
- [Arrays (video)](https://www.coursera.org/learn/data-structures/lecture/OsBSF/arrays) - [Arrays (video)](https://www.coursera.org/learn/data-structures/lecture/OsBSF/arrays)
@ -527,15 +529,15 @@ Write code on a whiteboard or paper, not a computer. Test with some sample input
- O(n) to insert/remove elsewhere - O(n) to insert/remove elsewhere
- [ ] Space - [ ] Space
- contiguous in memory, so proximity helps performance - 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 - ### Linked Lists
- [ ] Description: - [ ] Description:
- [ ] [Singly Linked Lists (video)](https://www.coursera.org/learn/data-structures/lecture/kHhgK/singly-linked-lists) - [ ] [Singly Linked Lists (video)](https://www.coursera.org/learn/data-structures/lecture/kHhgK/singly-linked-lists)
- [ ] [CS 61B - Linked Lists 1 (video)](https://archive.org/details/ucberkeley_webcast_htzJdKoEmO0) - [ ] [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) - [ ] [CS 61B - Linked Lists 2 (video)](https://archive.org/details/ucberkeley_webcast_-c4I3gFYe3w)
- [ ] [C Code (video)](https://www.youtube.com/watch?v=QN6FPiD0Gzo) - [ ] [C Code (video)](https://www.youtube.com/watch?v=QN6FPiD0Gzo) - not the whole video, just portions about Node struct and memory allocation.
- not the whole video, just portions about Node struct and memory allocation.
- [ ] Linked List vs Arrays: - [ ] Linked List vs Arrays:
- [Core Linked Lists Vs Arrays (video)](https://www.coursera.org/learn/data-structures-optimizing-performance/lecture/rjBs9/core-linked-lists-vs-arrays) - [Core Linked Lists Vs Arrays (video)](https://www.coursera.org/learn/data-structures-optimizing-performance/lecture/rjBs9/core-linked-lists-vs-arrays)
- [In The Real World Linked Lists Vs Arrays (video)](https://www.coursera.org/learn/data-structures-optimizing-performance/lecture/QUaUd/in-the-real-world-lists-vs-arrays) - [In The Real World Linked Lists Vs Arrays (video)](https://www.coursera.org/learn/data-structures-optimizing-performance/lecture/QUaUd/in-the-real-world-lists-vs-arrays)
@ -564,11 +566,13 @@ Write code on a whiteboard or paper, not a computer. Test with some sample input
- No need to implement - No need to implement
- ### Stack - ### Stack
- [ ] [Stacks (video)](https://www.coursera.org/learn/data-structures/lecture/UdKzQ/stacks) - [ ] [Stacks (video)](https://www.coursera.org/learn/data-structures/lecture/UdKzQ/stacks)
- [ ] [Using Stacks Last-In First-Out (video)](https://archive.org/details/0102WhatYouShouldKnow/05_01-usingStacksForLast-inFirst-out.mp4) - [ ] [Using Stacks Last-In First-Out (video)](https://archive.org/details/0102WhatYouShouldKnow/05_01-usingStacksForLast-inFirst-out.mp4)
- [ ] Will not implement. Implementing with array is trivial. - [ ] Will not implement. Implementing with array is trivial.
- ### Queue - ### Queue
- [ ] [Using Queues First-In First-Out(video)](https://archive.org/details/0102WhatYouShouldKnow/05_03-usingQueuesForFirst-inFirst-out.mp4) - [ ] [Using Queues First-In First-Out(video)](https://archive.org/details/0102WhatYouShouldKnow/05_03-usingQueuesForFirst-inFirst-out.mp4)
- [ ] [Queue (video)](https://www.coursera.org/lecture/data-structures/queues-EShpq) - [ ] [Queue (video)](https://www.coursera.org/lecture/data-structures/queues-EShpq)
- [ ] [Circular buffer/FIFO](https://en.wikipedia.org/wiki/Circular_buffer) - [ ] [Circular buffer/FIFO](https://en.wikipedia.org/wiki/Circular_buffer)
@ -590,7 +594,9 @@ Write code on a whiteboard or paper, not a computer. Test with some sample input
- empty: O(1) (linked list and array) - empty: O(1) (linked list and array)
- ### Hash table - ### Hash table
- [ ] Videos: - [ ] Videos:
- [ ] [Hashing with Chaining (video)](https://www.youtube.com/watch?v=0M_kIqhwbFo&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=8) - [ ] [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) - [ ] [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) - [ ] [Open Addressing, Cryptographic Hashing (video)](https://www.youtube.com/watch?v=rvdJDijO2Ro&index=10&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb)
@ -599,6 +605,7 @@ Write code on a whiteboard or paper, not a computer. Test with some sample input
- [ ] [(Advanced) Perfect hashing (video)](https://www.youtube.com/watch?v=N0COwN14gt0&list=PL2B4EEwhKD-NbwZ4ezj7gyc_3yNrojKM9&index=4) - [ ] [(Advanced) Perfect hashing (video)](https://www.youtube.com/watch?v=N0COwN14gt0&list=PL2B4EEwhKD-NbwZ4ezj7gyc_3yNrojKM9&index=4)
- [ ] Online Courses: - [ ] Online Courses:
- [ ] [Understanding Hash Functions (video)](https://archive.org/details/0102WhatYouShouldKnow/06_02-understandingHashFunctions.mp4) - [ ] [Understanding Hash Functions (video)](https://archive.org/details/0102WhatYouShouldKnow/06_02-understandingHashFunctions.mp4)
- [ ] [Using Hash Tables (video)](https://archive.org/details/0102WhatYouShouldKnow/06_03-usingHashTables.mp4) - [ ] [Using Hash Tables (video)](https://archive.org/details/0102WhatYouShouldKnow/06_03-usingHashTables.mp4)
- [ ] [Supporting Hashing (video)](https://archive.org/details/0102WhatYouShouldKnow/06_04-supportingHashing.mp4) - [ ] [Supporting Hashing (video)](https://archive.org/details/0102WhatYouShouldKnow/06_04-supportingHashing.mp4)
@ -620,6 +627,7 @@ Write code on a whiteboard or paper, not a computer. Test with some sample input
## More Knowledge ## More Knowledge
- ### Binary search - ### Binary search
- [ ] [Binary Search (video)](https://www.youtube.com/watch?v=D5SrAga1pno) - [ ] [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) - [ ] [Binary Search (video)](https://www.khanacademy.org/computing/computer-science/algorithms/binary-search/a/binary-search)
- [ ] [detail](https://www.topcoder.com/community/competitive-programming/tutorials/binary-search/) - [ ] [detail](https://www.topcoder.com/community/competitive-programming/tutorials/binary-search/)
@ -630,7 +638,7 @@ Write code on a whiteboard or paper, not a computer. Test with some sample input
- ### Bitwise operations - ### Bitwise operations
- [ ] [Bits cheat sheet](https://github.com/jwasham/coding-interview-university/blob/master/extras/cheat%20sheets/bits-cheat-cheet.pdf) - you should know many of the powers of 2 from (2^1 to 2^16 and 2^32) - [ ] [Bits cheat sheet](https://github.com/jwasham/coding-interview-university/blob/master/extras/cheat%20sheets/bits-cheat-cheet.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: &, |, ^, ~, >>, << - [ ] 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: - [ ] Good intro:
[Bit Manipulation (video)](https://www.youtube.com/watch?v=7jkIUgLC29I) [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) - [ ] [C Programming Tutorial 2-10: Bitwise Operators (video)](https://www.youtube.com/watch?v=d0AwjSpNXR0)
@ -655,6 +663,7 @@ Write code on a whiteboard or paper, not a computer. Test with some sample input
## Trees ## Trees
- ### Trees - Notes & Background - ### Trees - Notes & Background
- [ ] [Series: Core Trees (video)](https://www.coursera.org/learn/data-structures-optimizing-performance/lecture/ovovP/core-trees) - [ ] [Series: Core Trees (video)](https://www.coursera.org/learn/data-structures-optimizing-performance/lecture/ovovP/core-trees)
- [ ] [Series: Trees (video)](https://www.coursera.org/learn/data-structures/lecture/95qda/trees) - [ ] [Series: Trees (video)](https://www.coursera.org/learn/data-structures/lecture/95qda/trees)
- basic tree construction - basic tree construction
@ -675,6 +684,7 @@ Write code on a whiteboard or paper, not a computer. Test with some sample input
- preorder (DFS: self, left, right) - preorder (DFS: self, left, right)
- ### Binary search trees: BSTs - ### Binary search trees: BSTs
- [ ] [Binary Search Tree Review (video)](https://www.youtube.com/watch?v=x6At0nzX92o&index=1&list=PLA5Lqm4uh9Bbq-E0ZnqTIa8LRaL77ica6) - [ ] [Binary Search Tree Review (video)](https://www.youtube.com/watch?v=x6At0nzX92o&index=1&list=PLA5Lqm4uh9Bbq-E0ZnqTIa8LRaL77ica6)
- [ ] [Series (video)](https://www.coursera.org/learn/data-structures-optimizing-performance/lecture/p82sw/core-introduction-to-binary-search-trees) - [ ] [Series (video)](https://www.coursera.org/learn/data-structures-optimizing-performance/lecture/p82sw/core-introduction-to-binary-search-trees)
- starts with symbol table and goes through BST applications - starts with symbol table and goes through BST applications
@ -706,7 +716,7 @@ Write code on a whiteboard or paper, not a computer. Test with some sample input
- ### Heap / Priority Queue / Binary Heap - ### Heap / Priority Queue / Binary Heap
- visualized as a tree, but is usually linear in storage (array, linked list) - 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/learn/data-structures/lecture/2OpTs/introduction) - [ ] [Introduction (video)](https://www.coursera.org/learn/data-structures/lecture/2OpTs/introduction)
- [ ] [Naive Implementations (video)](https://www.coursera.org/learn/data-structures/lecture/z3l9N/naive-implementations) - [ ] [Naive Implementations (video)](https://www.coursera.org/learn/data-structures/lecture/z3l9N/naive-implementations)
- [ ] [Binary Trees (video)](https://www.coursera.org/learn/data-structures/lecture/GRV2q/binary-trees) - [ ] [Binary Trees (video)](https://www.coursera.org/learn/data-structures/lecture/GRV2q/binary-trees)
@ -736,6 +746,7 @@ Write code on a whiteboard or paper, not a computer. Test with some sample input
## Sorting ## Sorting
- [ ] Notes: - [ ] Notes:
- Implement sorts & know best case/worst case, average complexity of each: - Implement sorts & know best case/worst case, average complexity of each:
- no bubble sort - it's terrible - O(n^2), except when n <= 16 - no bubble sort - it's terrible - O(n^2), except when n <= 16
- [ ] stability in sorting algorithms ("Is Quicksort stable?") - [ ] stability in sorting algorithms ("Is Quicksort stable?")
@ -750,6 +761,7 @@ Write code on a whiteboard or paper, not a computer. Test with some sample input
- For heapsort, see Heap data structure above. Heap sort is great, but not stable. - For heapsort, see 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) - [ ] [Sedgewick - Mergesort (5 videos)](https://www.coursera.org/learn/algorithms-part1/home/week/3)
- [ ] [1. Mergesort](https://www.coursera.org/learn/algorithms-part1/lecture/ARWDq/mergesort) - [ ] [1. Mergesort](https://www.coursera.org/learn/algorithms-part1/lecture/ARWDq/mergesort)
- [ ] [2. Bottom up Mergesort](https://www.coursera.org/learn/algorithms-part1/lecture/PWNEl/bottom-up-mergesort) - [ ] [2. Bottom up Mergesort](https://www.coursera.org/learn/algorithms-part1/lecture/PWNEl/bottom-up-mergesort)
- [ ] [3. Sorting Complexity](https://www.coursera.org/learn/algorithms-part1/lecture/xAltF/sorting-complexity) - [ ] [3. Sorting Complexity](https://www.coursera.org/learn/algorithms-part1/lecture/xAltF/sorting-complexity)
@ -757,12 +769,14 @@ Write code on a whiteboard or paper, not a computer. Test with some sample input
- [ ] [5. Stability](https://www.coursera.org/learn/algorithms-part1/lecture/pvvLZ/stability) - [ ] [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) - [ ] [Sedgewick - Quicksort (4 videos)](https://www.coursera.org/learn/algorithms-part1/home/week/3)
- [ ] [1. Quicksort](https://www.coursera.org/learn/algorithms-part1/lecture/vjvnC/quicksort) - [ ] [1. Quicksort](https://www.coursera.org/learn/algorithms-part1/lecture/vjvnC/quicksort)
- [ ] [2. Selection](https://www.coursera.org/learn/algorithms-part1/lecture/UQxFT/selection) - [ ] [2. Selection](https://www.coursera.org/learn/algorithms-part1/lecture/UQxFT/selection)
- [ ] [3. Duplicate Keys](https://www.coursera.org/learn/algorithms-part1/lecture/XvjPd/duplicate-keys) - [ ] [3. Duplicate Keys](https://www.coursera.org/learn/algorithms-part1/lecture/XvjPd/duplicate-keys)
- [ ] [4. System Sorts](https://www.coursera.org/learn/algorithms-part1/lecture/QBNZ7/system-sorts) - [ ] [4. System Sorts](https://www.coursera.org/learn/algorithms-part1/lecture/QBNZ7/system-sorts)
- [ ] UC Berkeley: - [ ] UC Berkeley:
- [ ] [CS 61B Lecture 29: Sorting I (video)](https://archive.org/details/ucberkeley_webcast_EiUvYS2DT6I) - [ ] [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 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) - [ ] [CS 61B Lecture 32: Sorting III (video)](https://archive.org/details/ucberkeley_webcast_Y6LOLpxg6Dc)
@ -781,11 +795,13 @@ Write code on a whiteboard or paper, not a computer. Test with some sample input
- [ ] [Using output array (Python)](https://github.com/jwasham/practice-python/blob/master/merge_sort/merge_sort.py) - [ ] [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) - [ ] [In-place (C++)](https://github.com/jwasham/practice-cpp/blob/master/merge_sort/merge_sort.cc)
- [ ] Quick sort code: - [ ] Quick sort code:
- [ ] [Implementation (C)](http://www.cs.yale.edu/homes/aspnes/classes/223/examples/randomization/quick.c) - [ ] [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 (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) - [ ] [Implementation (Python)](https://github.com/jwasham/practice-python/blob/master/quick_sort/quick_sort.py)
- [ ] Implement: - [ ] Implement:
- [ ] Mergesort: O(n log n) average and worst case - [ ] Mergesort: O(n log n) average and worst case
- [ ] Quicksort O(n log n) average case - [ ] Quicksort O(n log n) average case
- Selection sort and insertion sort are both O(n^2) average and worst case - Selection sort and insertion sort are both O(n^2) average and worst case
@ -813,6 +829,7 @@ If you need more detail on this subject, see "Sorting" section in [Additional De
Graphs can be used to represent many problems in computer science, so this section is long, like trees and sorting were. Graphs can be used to represent many problems in computer science, so this section is long, like trees and sorting were.
- Notes: - Notes:
- There are 4 basic ways to represent a graph in memory: - There are 4 basic ways to represent a graph in memory:
- objects and pointers - objects and pointers
- adjacency matrix - adjacency matrix
@ -823,10 +840,12 @@ 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. - When asked a question, look for a graph-based solution first, then move on if none.
- [ ] MIT(videos): - [ ] MIT(videos):
- [ ] [Breadth-First Search](https://www.youtube.com/watch?v=s-CYnVz-uh4&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=13) - [ ] [Breadth-First Search](https://www.youtube.com/watch?v=s-CYnVz-uh4&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=13)
- [ ] [Depth-First Search](https://www.youtube.com/watch?v=AfSk24UTFS8&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=14) - [ ] [Depth-First Search](https://www.youtube.com/watch?v=AfSk24UTFS8&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=14)
- [ ] Skiena Lectures - great intro: - [ ] Skiena Lectures - great intro:
- [ ] [CSE373 2012 - Lecture 11 - Graph Data Structures (video)](https://www.youtube.com/watch?v=OiXxhDrFruw&list=PLOtl7M3yp-DV69F32zdK7YJcNXpTunF2b&index=11) - [ ] [CSE373 2012 - Lecture 11 - Graph Data Structures (video)](https://www.youtube.com/watch?v=OiXxhDrFruw&list=PLOtl7M3yp-DV69F32zdK7YJcNXpTunF2b&index=11)
- [ ] [CSE373 2012 - Lecture 12 - Breadth-First Search (video)](https://www.youtube.com/watch?v=g5vF8jscteo&list=PLOtl7M3yp-DV69F32zdK7YJcNXpTunF2b&index=12) - [ ] [CSE373 2012 - Lecture 12 - Breadth-First Search (video)](https://www.youtube.com/watch?v=g5vF8jscteo&list=PLOtl7M3yp-DV69F32zdK7YJcNXpTunF2b&index=12)
- [ ] [CSE373 2012 - Lecture 13 - Graph Algorithms (video)](https://www.youtube.com/watch?v=S23W6eTcqdY&list=PLOtl7M3yp-DV69F32zdK7YJcNXpTunF2b&index=13) - [ ] [CSE373 2012 - Lecture 13 - Graph Algorithms (video)](https://www.youtube.com/watch?v=S23W6eTcqdY&list=PLOtl7M3yp-DV69F32zdK7YJcNXpTunF2b&index=13)
@ -840,8 +859,8 @@ Graphs can be used to represent many problems in computer science, so this secti
- [ ] [6.006 Dijkstra (video)](https://www.youtube.com/watch?v=2E7MmKv0Y24&index=16&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb) - [ ] [6.006 Dijkstra (video)](https://www.youtube.com/watch?v=2E7MmKv0Y24&index=16&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb)
- [ ] [6.006 Bellman-Ford (video)](https://www.youtube.com/watch?v=ozsuci5pIso&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=17) - [ ] [6.006 Bellman-Ford (video)](https://www.youtube.com/watch?v=ozsuci5pIso&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=17)
- [ ] [6.006 Speeding Up Dijkstra (video)](https://www.youtube.com/watch?v=CHvQ3q_gJ7E&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=18) - [ ] [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 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 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 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) - [ ] [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 (starting at 58:09) (video)](https://youtu.be/dgjX4HdMI-Q?list=PL-XXv-cvA_iAlnI-BQr9hjqADPBtujFJd&t=3489)~~ - [ ] ~~[CS 61B 2014 (starting at 58:09) (video)](https://youtu.be/dgjX4HdMI-Q?list=PL-XXv-cvA_iAlnI-BQr9hjqADPBtujFJd&t=3489)~~
@ -850,6 +869,7 @@ Graphs can be used to represent many problems in computer science, so this secti
- [ ] [Strongly Connected Components Kosaraju's Algorithm Graph Algorithm (video)](https://www.youtube.com/watch?v=RpgcYiky7uw) - [ ] [Strongly Connected Components Kosaraju's Algorithm Graph Algorithm (video)](https://www.youtube.com/watch?v=RpgcYiky7uw)
- Full Coursera Course: - Full Coursera Course:
- [ ] [Algorithms on Graphs (video)](https://www.coursera.org/learn/algorithms-on-graphs/home/welcome) - [ ] [Algorithms on Graphs (video)](https://www.coursera.org/learn/algorithms-on-graphs/home/welcome)
- I'll implement: - I'll implement:
@ -871,6 +891,7 @@ Graphs can be used to represent many problems in computer science, so this secti
## Even More Knowledge ## Even More Knowledge
- ### Recursion - ### Recursion
- [ ] Stanford lectures on recursion & backtracking: - [ ] Stanford lectures on recursion & backtracking:
- [ ] [Lecture 8 | Programming Abstractions (video)](https://www.youtube.com/watch?v=gl3emqCuueQ&list=PLFE6E58F856038C69&index=8) - [ ] [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) - [ ] [Lecture 9 | Programming Abstractions (video)](https://www.youtube.com/watch?v=uFJhEPrbycQ&list=PLFE6E58F856038C69&index=9)
@ -882,6 +903,7 @@ Graphs can be used to represent many problems in computer science, so this secti
- [ ] [Tail Recursion (video)](https://www.youtube.com/watch?v=L1jjXGfxozc) - [ ] [Tail Recursion (video)](https://www.youtube.com/watch?v=L1jjXGfxozc)
- ### Dynamic Programming - ### 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. - 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. - 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. - I suggest looking at many examples of DP problems until you have a solid understanding of the pattern involved.
@ -908,6 +930,7 @@ 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) - [ ] [Local pairwise sequence alignment (video)](https://www.coursera.org/learn/algorithmic-thinking-2/lecture/WnNau/local-pairwise-sequence-alignment)
- ### Object-Oriented Programming - ### Object-Oriented Programming
- [ ] [Optional: UML 2.0 Series (video)](https://www.youtube.com/watch?v=OkC7HKtiZC0&list=PLGLfVvz_LVvQ5G-LdJ8RLqe-ndo7QITYc) - [ ] [Optional: UML 2.0 Series (video)](https://www.youtube.com/watch?v=OkC7HKtiZC0&list=PLGLfVvz_LVvQ5G-LdJ8RLqe-ndo7QITYc)
- [ ] SOLID OOP Principles: [SOLID Principles (video)](https://www.youtube.com/playlist?list=PL4CE9F710017EA77A) - [ ] SOLID OOP Principles: [SOLID Principles (video)](https://www.youtube.com/playlist?list=PL4CE9F710017EA77A)
@ -940,8 +963,8 @@ 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) - [ ] [Handy reference: 101 Design Patterns & Tips for Developers](https://sourcemaking.com/design-patterns-and-tips)
- [ ] [Design patterns for humans](https://github.com/kamranahmedse/design-patterns-for-humans#structural-design-patterns) - [ ] [Design patterns for humans](https://github.com/kamranahmedse/design-patterns-for-humans#structural-design-patterns)
* ### Combinatorics (n choose k) & Probability
- ### Combinatorics (n choose k) & Probability
- [ ] [Math Skills: How to find Factorial, Permutation and Combination (Choose) (video)](https://www.youtube.com/watch?v=8RRo6Ti9d0U) - [ ] [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: 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) - [ ] [Make School: More Probability and Markov Chains (video)](https://www.youtube.com/watch?v=dNaJg-mLobQ)
@ -951,7 +974,8 @@ Graphs can be used to represent many problems in computer science, so this secti
- Just the videos - 41 (each are simple and each are short): - Just the videos - 41 (each are simple and each are short):
- [ ] [Probability Explained (video)](https://www.youtube.com/watch?v=uzkc-qNVoOk&list=PLC58778F28211FA19) - [ ] [Probability Explained (video)](https://www.youtube.com/watch?v=uzkc-qNVoOk&list=PLC58778F28211FA19)
- ### NP, NP-Complete and Approximation Algorithms * ### NP, NP-Complete and Approximation Algorithms
- Know about the most famous classes of NP-complete problems, such as traveling salesman and the knapsack problem, - 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. and be able to recognize them when an interviewer asks you them in disguise.
- Know what NP-complete means. - Know what NP-complete means.
@ -972,7 +996,8 @@ Graphs can be used to represent many problems in computer science, so this secti
- [Jupyter Notebook](http://nbviewer.jupyter.org/url/norvig.com/ipython/TSP.ipynb) - [Jupyter Notebook](http://nbviewer.jupyter.org/url/norvig.com/ipython/TSP.ipynb)
- Pages 1048 - 1140 in CLRS if you have it. - Pages 1048 - 1140 in CLRS if you have it.
- ### Caches * ### Caches
- [ ] LRU cache: - [ ] LRU cache:
- [ ] [The Magic of LRU Cache (100 Days of Google Dev) (video)](https://www.youtube.com/watch?v=R5ON3iwx78M) - [ ] [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) - [ ] [Implementing LRU (video)](https://www.youtube.com/watch?v=bq6N7Ym81iI)
@ -981,7 +1006,8 @@ Graphs can be used to represent many problems in computer science, so this secti
- [ ] [MIT 6.004 L15: The Memory Hierarchy (video)](https://www.youtube.com/watch?v=vjYF_fAZI5E&list=PLrRW1w6CGAcXbMtDFj205vALOGmiRc82-&index=24) - [ ] [MIT 6.004 L15: The Memory Hierarchy (video)](https://www.youtube.com/watch?v=vjYF_fAZI5E&list=PLrRW1w6CGAcXbMtDFj205vALOGmiRc82-&index=24)
- [ ] [MIT 6.004 L16: Cache Issues (video)](https://www.youtube.com/watch?v=ajgC3-pyGlk&index=25&list=PLrRW1w6CGAcXbMtDFj205vALOGmiRc82-) - [ ] [MIT 6.004 L16: Cache Issues (video)](https://www.youtube.com/watch?v=ajgC3-pyGlk&index=25&list=PLrRW1w6CGAcXbMtDFj205vALOGmiRc82-)
- ### Processes and Threads * ### Processes and Threads
- [ ] Computer Science 162 - Operating Systems (25 videos): - [ ] Computer Science 162 - Operating Systems (25 videos):
- for processes and threads see videos 1-11 - 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) - [Operating Systems and System Programming (video)](https://archive.org/details/ucberkeley-webcast-PL-XXv-cvA_iBDyz-ba4yDskqMDY6A1w_c)
@ -1002,7 +1028,6 @@ Graphs can be used to represent many problems in computer science, so this secti
- Modern concurrency constructs with multicore processors - Modern concurrency constructs with multicore processors
- [Paging, segmentation and virtual memory (video)](https://www.youtube.com/watch?v=LKe7xK0bF7o&list=PLCiOXwirraUCBE9i_ukL8_Kfg6XNv7Se8&index=2) - [Paging, segmentation and virtual memory (video)](https://www.youtube.com/watch?v=LKe7xK0bF7o&list=PLCiOXwirraUCBE9i_ukL8_Kfg6XNv7Se8&index=2)
- [Interrupts (video)](https://www.youtube.com/watch?v=uFKi2-J-6II&list=PLCiOXwirraUCBE9i_ukL8_Kfg6XNv7Se8&index=3) - [Interrupts (video)](https://www.youtube.com/watch?v=uFKi2-J-6II&list=PLCiOXwirraUCBE9i_ukL8_Kfg6XNv7Se8&index=3)
- [Scheduling (video)](https://www.youtube.com/watch?v=-Gu5mYdKbu4&index=4&list=PLCiOXwirraUCBE9i_ukL8_Kfg6XNv7Se8)
- Process resource needs (memory: code, static storage, stack, heap, and also file descriptors, i/o) - 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) - 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. - Forking is really copy on write (read-only) until the new process writes to memory, then it does a full copy.
@ -1018,7 +1043,8 @@ Graphs can be used to represent many problems in computer science, so this secti
- [ ] [Keynote David Beazley - Topics of Interest (Python Asyncio)](https://www.youtube.com/watch?v=ZzfHjytDceU) - [ ] [Keynote David Beazley - Topics of Interest (Python Asyncio)](https://www.youtube.com/watch?v=ZzfHjytDceU)
- [ ] [Mutex in Python](https://www.youtube.com/watch?v=0zaPs8OtyKY) - [ ] [Mutex in Python](https://www.youtube.com/watch?v=0zaPs8OtyKY)
- ### Testing * ### Testing
- To cover: - To cover:
- how unit testing works - how unit testing works
- what are mock objects - what are mock objects
@ -1033,11 +1059,13 @@ Graphs can be used to represent many problems in computer science, so this secti
- [ ] [Tao Of Testing](http://jasonpolites.github.io/tao-of-testing/ch3-1.1.html) - [ ] [Tao Of Testing](http://jasonpolites.github.io/tao-of-testing/ch3-1.1.html)
- [ ] [How to write tests](http://jasonpolites.github.io/tao-of-testing/ch4-1.1.html) - [ ] [How to write tests](http://jasonpolites.github.io/tao-of-testing/ch4-1.1.html)
- ### Scheduling * ### Scheduling
- in an OS, how it works - in an OS, how it works
- can be gleaned from Operating System videos - can be gleaned from Operating System videos
- ### String searching & manipulations * ### String searching & manipulations
- [ ] [Sedgewick - Suffix Arrays (video)](https://www.coursera.org/learn/algorithms-part2/lecture/TH18W/suffix-arrays) - [ ] [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) - [ ] [Sedgewick - Substring Search (videos)](https://www.coursera.org/learn/algorithms-part2/home/week/4)
- [ ] [1. Introduction to Substring Search](https://www.coursera.org/learn/algorithms-part2/lecture/n3ZpG/introduction-to-substring-search) - [ ] [1. Introduction to Substring Search](https://www.coursera.org/learn/algorithms-part2/lecture/n3ZpG/introduction-to-substring-search)
@ -1049,7 +1077,8 @@ Graphs can be used to represent many problems in computer science, so this secti
If you need more detail on this subject, see "String Matching" section in [Additional Detail on Some Subjects](#additional-detail-on-some-subjects) If you need more detail on this subject, see "String Matching" section in [Additional Detail on Some Subjects](#additional-detail-on-some-subjects)
- ### Tries * ### Tries
- Note there are different kinds of tries. Some have prefixes, some don't, and some use string instead of bits - Note there are different kinds of tries. Some have prefixes, some don't, and some use string instead of bits
to track the path. to track the path.
- I read through code, but will not implement. - I read through code, but will not implement.
@ -1067,22 +1096,25 @@ Graphs can be used to represent many problems in computer science, so this secti
- [ ] [Stanford Lecture (real world use case) (video)](https://www.youtube.com/watch?v=TJ8SkcUSdbU) - [ ] [Stanford Lecture (real world use case) (video)](https://www.youtube.com/watch?v=TJ8SkcUSdbU)
- [ ] [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) - [ ] [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 * ### 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) - [ ] 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)
- [ ] 32 bit: [IEEE754 32-bit floating point binary (video)](https://www.youtube.com/watch?v=50ZYcZebIec) - [ ] 32 bit: [IEEE754 32-bit floating point binary (video)](https://www.youtube.com/watch?v=50ZYcZebIec)
- ### Unicode * ### 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/) - [ ] [What Every Programmer Absolutely, Positively Needs To Know About Encodings And Character Sets To Work With Text](http://kunststube.net/encoding/)
- ### Endianness * ### 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 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 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) - [ ] [Big And Little Endian Inside/Out (video)](https://www.youtube.com/watch?v=oBSuXP-1Tc0)
- Very technical talk for kernel devs. Don't worry if most is over your head. - Very technical talk for kernel devs. Don't worry if most is over your head.
- The first half is enough. - The first half is enough.
- ### Networking * ### Networking
- **if you have networking experience or want to be a reliability engineer or operations engineer, expect questions** - **if you have networking experience or want to be a reliability engineer or operations engineer, expect questions**
- otherwise, this is just good to know - otherwise, this is just good to know
- [ ] [Khan Academy](https://www.khanacademy.org/computing/computer-science/internet-intro) - [ ] [Khan Academy](https://www.khanacademy.org/computing/computer-science/internet-intro)
@ -1167,7 +1199,7 @@ Graphs can be used to represent many problems in computer science, so this secti
- [ ] [To Compress Or Not To Compress, That Was Uber's Question](https://eng.uber.com/trip-data-squeeze/) - [ ] [To Compress Or Not To Compress, That Was Uber's Question](https://eng.uber.com/trip-data-squeeze/)
- [ ] [Asyncio Tarantool Queue, Get In The Queue](http://highscalability.com/blog/2016/3/3/asyncio-tarantool-queue-get-in-the-queue.html) - [ ] [Asyncio Tarantool Queue, Get In The Queue](http://highscalability.com/blog/2016/3/3/asyncio-tarantool-queue-get-in-the-queue.html)
- [ ] [When Should Approximate Query Processing Be Used?](http://highscalability.com/blog/2016/2/25/when-should-approximate-query-processing-be-used.html) - [ ] [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)
- [ ] [Spanner](http://highscalability.com/blog/2012/9/24/google-spanners-most-surprising-revelation-nosql-is-out-and.html) - [ ] [Spanner](http://highscalability.com/blog/2012/9/24/google-spanners-most-surprising-revelation-nosql-is-out-and.html)
- [ ] [Machine Learning Driven Programming: A New Programming For A New World](http://highscalability.com/blog/2016/7/6/machine-learning-driven-programming-a-new-programming-for-a.html) - [ ] [Machine Learning Driven Programming: A New Programming For A New World](http://highscalability.com/blog/2016/7/6/machine-learning-driven-programming-a-new-programming-for-a.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) - [ ] [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)
@ -1249,6 +1281,7 @@ Now that you know all the computer science topics above, it's time to practice a
**Coding question practice is not about memorizing answers to programming problems.** **Coding question practice is not about memorizing answers to programming problems.**
Why you need to practice doing programming problems: Why you need to practice doing programming problems:
- problem recognition, and where the right data structures and algorithms fit in - problem recognition, and where the right data structures and algorithms fit in
- gathering requirements for the problem - gathering requirements for the problem
- talking your way through the problem like you will in the interview - talking your way through the problem like you will in the interview
@ -1283,7 +1316,6 @@ Supplemental:
See [Book List above](#book-list) See [Book List above](#book-list)
## Coding exercises/challenges ## Coding exercises/challenges
Once you've learned your brains out, put those brains to work. Once you've learned your brains out, put those brains to work.
@ -1293,10 +1325,12 @@ Take coding challenges every day, as many as you can.
- [How to Dissect a Topcoder Problem Statement](https://www.topcoder.com/community/competitive-programming/tutorials/how-to-dissect-a-topcoder-problem-statement/) - [How to Dissect a Topcoder Problem Statement](https://www.topcoder.com/community/competitive-programming/tutorials/how-to-dissect-a-topcoder-problem-statement/)
Coding Interview Question Videos: Coding Interview Question Videos:
- [IDeserve (88 videos)](https://www.youtube.com/watch?v=NBcqBddFbZw&list=PLamzFoFxwoNjPfxzaWqs7cZGsPYy0x_gI) - [IDeserve (88 videos)](https://www.youtube.com/watch?v=NBcqBddFbZw&list=PLamzFoFxwoNjPfxzaWqs7cZGsPYy0x_gI)
- [Tushar Roy (5 playlists)](https://www.youtube.com/user/tusharroy2525/playlists?shelf_id=2&view=50&sort=dd) - [Tushar Roy (5 playlists)](https://www.youtube.com/user/tusharroy2525/playlists?shelf_id=2&view=50&sort=dd)
Challenge sites: Challenge sites:
- [LeetCode](https://leetcode.com/) - [LeetCode](https://leetcode.com/)
- [TopCoder](https://www.topcoder.com/) - [TopCoder](https://www.topcoder.com/)
- [Project Euler (math-focused)](https://projecteuler.net/index.php?section=problems) - [Project Euler (math-focused)](https://projecteuler.net/index.php?section=problems)
@ -1311,14 +1345,15 @@ Challenge sites:
- [Codechef](https://www.codechef.com/) - [Codechef](https://www.codechef.com/)
Challenge repos: Challenge repos:
- [Interactive Coding Interview Challenges in Python](https://github.com/donnemartin/interactive-coding-challenges) - [Interactive Coding Interview Challenges in Python](https://github.com/donnemartin/interactive-coding-challenges)
Mock Interviews: 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. - [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/) - peer-to-peer model of practice interviews - [Pramp: Mock interviews from/with peers](https://www.pramp.com/) - peer-to-peer model of practice interviews
- [Refdash: Mock interviews and expedited interviews](https://refdash.com/) - also help candidates fast track by skipping multiple interviews with tech companies. - [Refdash: Mock interviews and expedited interviews](https://refdash.com/) - also help candidates fast track by skipping multiple interviews with tech companies.
## Once you're closer to the interview ## Once you're closer to the interview
- Cracking The Coding Interview Set 2 (videos): - Cracking The Coding Interview Set 2 (videos):
@ -1329,7 +1364,6 @@ Mock Interviews:
- See Resume prep items in Cracking The Coding Interview and back of Programming Interviews Exposed - See Resume prep items in Cracking The Coding Interview and back of Programming Interviews Exposed
## Be thinking of for when the interview comes ## 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 2-3 answers for each. Think of about 20 interview questions you'll get, along with the lines of the items below. Have 2-3 answers for each.
@ -1418,7 +1452,7 @@ You're never really done.
- about to get to this part. Will update here once I've made my way through it. - about to get to this part. Will update here once I've made my way through it.
- Can rent it on kindle - Can rent it on kindle
- Answers: - Answers:
- [Solutions](http://www.algorithm.cs.sunysb.edu/algowiki/index.php/The_Algorithms_Design_Manual_(Second_Edition)) - [Solutions](<http://www.algorithm.cs.sunysb.edu/algowiki/index.php/The_Algorithms_Design_Manual_(Second_Edition)>)
- [Solutions](http://blog.panictank.net/category/algorithmndesignmanualsolutions/page/2/) - [Solutions](http://blog.panictank.net/category/algorithmndesignmanualsolutions/page/2/)
- [Errata](http://www3.cs.stonybrook.edu/~skiena/algorist/book/errata) - [Errata](http://www3.cs.stonybrook.edu/~skiena/algorist/book/errata)
- [Write Great Code: Volume 1: Understanding the Machine](https://www.amazon.com/Write-Great-Code-Understanding-Machine/dp/1593270038) - [Write Great Code: Volume 1: Understanding the Machine](https://www.amazon.com/Write-Great-Code-Understanding-Machine/dp/1593270038)
@ -1435,10 +1469,12 @@ You're never really done.
- Chapter 10 - Instruction Set Architecture - Chapter 10 - Instruction Set Architecture
- Chapter 11 - Memory Architecture and Organization - Chapter 11 - Memory Architecture and Organization
- [Introduction to Algorithms](https://www.amazon.com/Introduction-Algorithms-3rd-MIT-Press/dp/0262033844) - [Introduction to Algorithms](https://www.amazon.com/Introduction-Algorithms-3rd-MIT-Press/dp/0262033844)
- **Important:** Reading this book will only have limited value. This book is a great review of algorithms and data structures, but won't teach you how to write good code. You have to be able to code a decent solution efficiently. - **Important:** Reading this book will only have limited value. This book is a great review of algorithms and data structures, but won't teach you how to write good code. You have to be able to code a decent solution efficiently.
- aka CLR, sometimes CLRS, because Stein was late to the game - aka CLR, sometimes CLRS, because Stein was late to the game
- [Computer Architecture, Sixth Edition: A Quantitative Approach](https://www.amazon.com/dp/0128119055) - [Computer Architecture, Sixth Edition: A Quantitative Approach](https://www.amazon.com/dp/0128119055)
- For a richer, more up-to-date (2017), but longer treatment - For a richer, more up-to-date (2017), but longer treatment
- [Programming Pearls](http://www.amazon.com/Programming-Pearls-2nd-Jon-Bentley/dp/0201657880) - [Programming Pearls](http://www.amazon.com/Programming-Pearls-2nd-Jon-Bentley/dp/0201657880)
@ -1451,12 +1487,14 @@ You're never really done.
technologies and algorithms, so you'll have a bigger toolbox. technologies and algorithms, so you'll have a bigger toolbox.
- ### Compilers - ### Compilers
- [How a Compiler Works in ~1 minute (video)](https://www.youtube.com/watch?v=IhC7sdYe-Jg) - [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) - [Harvard CS50 - Compilers (video)](https://www.youtube.com/watch?v=CSZLNYF4Klo)
- [C++ (video)](https://www.youtube.com/watch?v=twodd1KFfGk) - [C++ (video)](https://www.youtube.com/watch?v=twodd1KFfGk)
- [Understanding Compiler Optimization (C++) (video)](https://www.youtube.com/watch?v=FnGCDLhaxKU) - [Understanding Compiler Optimization (C++) (video)](https://www.youtube.com/watch?v=FnGCDLhaxKU)
- ### Emacs and vi(m) - ### Emacs and vi(m)
- Familiarize yourself with a unix-based code editor - Familiarize yourself with a unix-based code editor
- vi(m): - vi(m):
- [Editing With vim 01 - Installation, Setup, and The Modes (video)](https://www.youtube.com/watch?v=5givLEMcINQ&index=1&list=PL13bz4SHGmRxlZVmWQ9DvXo1fEg4UdGkr) - [Editing With vim 01 - Installation, Setup, and The Modes (video)](https://www.youtube.com/watch?v=5givLEMcINQ&index=1&list=PL13bz4SHGmRxlZVmWQ9DvXo1fEg4UdGkr)
@ -1478,6 +1516,7 @@ You're never really done.
- [(maybe) Org Mode In Depth: Managing Structure (video)](https://www.youtube.com/watch?v=nsGYet02bEk) - [(maybe) Org Mode In Depth: Managing Structure (video)](https://www.youtube.com/watch?v=nsGYet02bEk)
- ### Unix command line tools - ### Unix command line tools
- I filled in the list below from good tools. - I filled in the list below from good tools.
- bash - bash
- cat - cat
@ -1492,6 +1531,7 @@ You're never really done.
- [tcpdump](https://danielmiessler.com/study/tcpdump/) - [tcpdump](https://danielmiessler.com/study/tcpdump/)
- ### Information theory (videos) - ### Information theory (videos)
- [Khan Academy](https://www.khanacademy.org/computing/computer-science/informationtheory) - [Khan Academy](https://www.khanacademy.org/computing/computer-science/informationtheory)
- more about Markov processes: - more about Markov processes:
- [Core Markov Text Generation](https://www.coursera.org/learn/data-structures-optimizing-performance/lecture/waxgx/core-markov-text-generation) - [Core Markov Text Generation](https://www.coursera.org/learn/data-structures-optimizing-performance/lecture/waxgx/core-markov-text-generation)
@ -1500,6 +1540,7 @@ You're never really done.
- See more in MIT 6.050J Information and Entropy series below. - See more in MIT 6.050J Information and Entropy series below.
- ### Parity & Hamming Code (videos) - ### Parity & Hamming Code (videos)
- [Intro](https://www.youtube.com/watch?v=q-3BctoUpHE) - [Intro](https://www.youtube.com/watch?v=q-3BctoUpHE)
- [Parity](https://www.youtube.com/watch?v=DdMcAUlxh1M) - [Parity](https://www.youtube.com/watch?v=DdMcAUlxh1M)
- Hamming Code: - Hamming Code:
@ -1508,11 +1549,13 @@ You're never really done.
- [Error Checking](https://www.youtube.com/watch?v=wbH2VxzmoZk) - [Error Checking](https://www.youtube.com/watch?v=wbH2VxzmoZk)
- ### Entropy - ### Entropy
- also see videos below - also see videos below
- make sure to watch information theory videos first - make sure to watch information theory videos first
- [Information Theory, Claude Shannon, Entropy, Redundancy, Data Compression & Bits (video)](https://youtu.be/JnJq3Py0dyM?t=176) - [Information Theory, Claude Shannon, Entropy, Redundancy, Data Compression & Bits (video)](https://youtu.be/JnJq3Py0dyM?t=176)
- ### Cryptography - ### Cryptography
- also see videos below - also see videos below
- make sure to watch information theory videos first - make sure to watch information theory videos first
- [Khan Academy Series](https://www.khanacademy.org/computing/computer-science/cryptography) - [Khan Academy Series](https://www.khanacademy.org/computing/computer-science/cryptography)
@ -1520,6 +1563,7 @@ You're never really done.
- [Cryptography: Encryption](https://www.youtube.com/watch?v=9TNI2wHmaeI&index=31&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp) - [Cryptography: Encryption](https://www.youtube.com/watch?v=9TNI2wHmaeI&index=31&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp)
- ### Compression - ### Compression
- make sure to watch information theory videos first - make sure to watch information theory videos first
- Computerphile (videos): - Computerphile (videos):
- [Compression](https://www.youtube.com/watch?v=Lto-ajuqW3w) - [Compression](https://www.youtube.com/watch?v=Lto-ajuqW3w)
@ -1532,6 +1576,7 @@ You're never really done.
- [(optional) Google Developers Live: GZIP is not enough!](https://www.youtube.com/watch?v=whGwm0Lky2s) - [(optional) Google Developers Live: GZIP is not enough!](https://www.youtube.com/watch?v=whGwm0Lky2s)
- ### Computer Security - ### Computer Security
- [MIT (23 videos)](https://www.youtube.com/playlist?list=PLUl4u3cNGP62K2DjQLRxDNRi0z2IRWnNh) - [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) - [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) - [Control Hijacking Attacks](https://www.youtube.com/watch?v=6bwzNg5qQ0o&list=PLUl4u3cNGP62K2DjQLRxDNRi0z2IRWnNh&index=2)
@ -1547,15 +1592,18 @@ You're never really done.
- [Side-Channel Attacks](https://www.youtube.com/watch?v=PuVMkSEcPiI&index=15&list=PLUl4u3cNGP62K2DjQLRxDNRi0z2IRWnNh) - [Side-Channel Attacks](https://www.youtube.com/watch?v=PuVMkSEcPiI&index=15&list=PLUl4u3cNGP62K2DjQLRxDNRi0z2IRWnNh)
- ### Garbage collection - ### Garbage collection
- [GC in Python (video)](https://www.youtube.com/watch?v=iHVs_HkjdmI) - [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 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) - [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 - ### Parallel Programming
- [Coursera (Scala)](https://www.coursera.org/learn/parprog1/home/week/1) - [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) - [Efficient Python for High Performance Parallel Computing (video)](https://www.youtube.com/watch?v=uY85GkaYzBk)
- ### Messaging, Serialization, and Queueing Systems - ### Messaging, Serialization, and Queueing Systems
- [Thrift](https://thrift.apache.org/) - [Thrift](https://thrift.apache.org/)
- [Tutorial](http://thrift-tutorial.readthedocs.io/en/latest/intro.html) - [Tutorial](http://thrift-tutorial.readthedocs.io/en/latest/intro.html)
- [Protocol Buffers](https://developers.google.com/protocol-buffers/) - [Protocol Buffers](https://developers.google.com/protocol-buffers/)
@ -1577,12 +1625,14 @@ You're never really done.
- [MessagePack](http://msgpack.org/index.html) - [MessagePack](http://msgpack.org/index.html)
- [Avro](https://avro.apache.org/) - [Avro](https://avro.apache.org/)
- ### A* - ### A\*
- [A Search Algorithm](https://en.wikipedia.org/wiki/A*_search_algorithm) - [A Search Algorithm](https://en.wikipedia.org/wiki/A*_search_algorithm)
- [A* Pathfinding Tutorial (video)](https://www.youtube.com/watch?v=KNXfSOx4eEE) - [A\* Pathfinding Tutorial (video)](https://www.youtube.com/watch?v=KNXfSOx4eEE)
- [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 - ### Fast Fourier Transform
- [An Interactive Guide To The Fourier Transform](https://betterexplained.com/articles/an-interactive-guide-to-the-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 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) - [What is the Fourier Transform? (video)](https://www.youtube.com/watch?v=Xxut2PN-V8Q)
@ -1590,6 +1640,7 @@ You're never really done.
- [Understanding The FFT](http://jakevdp.github.io/blog/2013/08/28/understanding-the-fft/) - [Understanding The FFT](http://jakevdp.github.io/blog/2013/08/28/understanding-the-fft/)
- ### Bloom Filter - ### Bloom Filter
- Given a Bloom filter with m bits and k hashing functions, both insertion and membership testing are O(k) - 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 (video)](https://www.youtube.com/watch?v=-SuTGoFYjZs)
- [Bloom Filters | Mining of Massive Datasets | Stanford University (video)](https://www.youtube.com/watch?v=qBTdukbzc78) - [Bloom Filters | Mining of Massive Datasets | Stanford University (video)](https://www.youtube.com/watch?v=qBTdukbzc78)
@ -1597,21 +1648,26 @@ 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/) - [How To Write A Bloom Filter App](http://blog.michaelschmatz.com/2016/04/11/how-to-write-a-bloom-filter-cpp/)
- ### HyperLogLog - ### 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) - [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 - ### Locality-Sensitive Hashing
- used to determine the similarity of documents - 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. - 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) - [Simhashing (hopefully) made simple](http://ferd.ca/simhashing-hopefully-made-simple.html)
- ### van Emde Boas Trees - ### van Emde Boas Trees
- [Divide & Conquer: van Emde Boas Trees (video)](https://www.youtube.com/watch?v=hmReJCupbNU&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp&index=6) - [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) - [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 - ### Augmented Data Structures
- [CS 61B Lecture 39: Augmenting Data Structures](https://archive.org/details/ucberkeley_webcast_zksIj9O8_jc) - [CS 61B Lecture 39: Augmenting Data Structures](https://archive.org/details/ucberkeley_webcast_zksIj9O8_jc)
- ### Balanced search trees - ### Balanced search trees
- Know at least one type of balanced binary tree (and know how it's implemented): - 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. - "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 A particularly interesting self-organizing data structure is the splay tree, which uses rotations
@ -1626,6 +1682,7 @@ You're never really done.
- [Self-balancing binary search tree](https://en.wikipedia.org/wiki/Self-balancing_binary_search_tree) - [Self-balancing binary search tree](https://en.wikipedia.org/wiki/Self-balancing_binary_search_tree)
- **AVL trees** - **AVL trees**
- In practice: - In practice:
From what I can tell, these aren't used much in practice, but I could see where they would be: 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 The AVL tree is another structure supporting O(log n) search, insertion, and removal. It is more rigidly
@ -1638,6 +1695,7 @@ You're never really done.
- [Split And Merge](https://www.coursera.org/learn/data-structures/lecture/22BgE/split-and-merge) - [Split And Merge](https://www.coursera.org/learn/data-structures/lecture/22BgE/split-and-merge)
- **Splay trees** - **Splay trees**
- In practice: - In practice:
Splay trees are typically used in the implementation of caches, memory allocators, routers, garbage collectors, 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, data compression, ropes (replacement of string used for long text strings), in Windows NT (in the virtual memory,
@ -1648,6 +1706,7 @@ You're never really done.
- [Video](https://www.youtube.com/watch?v=QnPl_Y6EqMo) - [Video](https://www.youtube.com/watch?v=QnPl_Y6EqMo)
- **Red/black trees** - **Red/black trees**
- these are a translation of a 2-3 tree (see below) - these are a translation of a 2-3 tree (see below)
- In practice: - In practice:
Red–black trees offer worst-case guarantees for insertion time, deletion time, and search time. Red–black trees offer worst-case guarantees for insertion time, deletion time, and search time.
@ -1663,6 +1722,7 @@ You're never really done.
- [An Introduction To Binary Search And Red Black Tree](https://www.topcoder.com/community/competitive-programming/tutorials/an-introduction-to-binary-search-and-red-black-trees/) - [An Introduction To Binary Search And Red Black Tree](https://www.topcoder.com/community/competitive-programming/tutorials/an-introduction-to-binary-search-and-red-black-trees/)
- **2-3 search trees** - **2-3 search trees**
- In practice: - In practice:
2-3 trees have faster inserts at the expense of slower searches (since height is more compared to AVL trees). 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 tree very rarely because its implementation involves different types of nodes. Instead, people use Red Black trees. - You would use 2-3 tree very rarely because its implementation involves different types of nodes. Instead, people use Red Black trees.
@ -1671,6 +1731,7 @@ You're never really done.
- [2-3 Trees (student recitation) (video)](https://www.youtube.com/watch?v=TOb1tuEZ2X4&index=5&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp) - [2-3 Trees (student recitation) (video)](https://www.youtube.com/watch?v=TOb1tuEZ2X4&index=5&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp)
- **2-3-4 Trees (aka 2-4 trees)** - **2-3-4 Trees (aka 2-4 trees)**
- In practice: - In practice:
For every 2-4 tree, there are corresponding red–black trees with data elements in the same order. The insertion and deletion For every 2-4 tree, 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 operations on 2-4 trees are also equivalent to color-flipping and rotations in red–black trees. This makes 2-4 trees an
@ -1681,6 +1742,7 @@ You're never really done.
- [Top Down 234-Trees (video)](https://www.youtube.com/watch?v=2679VQ26Fp4&list=PLA5Lqm4uh9Bbq-E0ZnqTIa8LRaL77ica6&index=5) - [Top Down 234-Trees (video)](https://www.youtube.com/watch?v=2679VQ26Fp4&list=PLA5Lqm4uh9Bbq-E0ZnqTIa8LRaL77ica6&index=5)
- **N-ary (K-ary, M-ary) trees** - **N-ary (K-ary, M-ary) trees**
- note: the N or K is the branching factor (max branches) - note: the N or K is the branching factor (max branches)
- binary trees are a 2-ary tree, with branching factor = 2 - binary trees are a 2-ary tree, with branching factor = 2
- 2-3 trees are 3-ary - 2-3 trees are 3-ary
@ -1698,56 +1760,62 @@ You're never really done.
- [Introduction to B-Trees (video)](https://www.youtube.com/watch?v=I22wEC1tTGo&list=PLA5Lqm4uh9Bbq-E0ZnqTIa8LRaL77ica6&index=6) - [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 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) - [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) - [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, may be skipped (B is block size, cache line size)
- covers cache-oblivious B-Trees, very interesting data structures
- the first 37 minutes are very technical, may be skipped (B is block size, cache line size)
* ### k-D Trees
- ### k-D Trees
- great for finding number of points in a rectangle or higher dimension object - great for finding number of points in a rectangle or higher dimension object
- a good fit for k-nearest neighbors - a good fit for k-nearest neighbors
- [Kd Trees (video)](https://www.youtube.com/watch?v=W94M9D_yXKk) - [Kd Trees (video)](https://www.youtube.com/watch?v=W94M9D_yXKk)
- [kNN K-d tree algorithm (video)](https://www.youtube.com/watch?v=Y4ZgLlDfKDg) - [kNN K-d tree algorithm (video)](https://www.youtube.com/watch?v=Y4ZgLlDfKDg)
- ### Skip lists * ### Skip lists
- "These are somewhat of a cult data structure" - Skiena - "These are somewhat of a cult data structure" - Skiena
- [Randomization: Skip Lists (video)](https://www.youtube.com/watch?v=2g9OSRKJuzM&index=10&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp) - [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) - [For animations and a little more detail](https://en.wikipedia.org/wiki/Skip_list)
- ### Network Flows * ### Network Flows
- [Ford-Fulkerson in 5 minutes — Step by step example (video)](https://www.youtube.com/watch?v=Tl90tNtKvxs) - [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) - [Ford-Fulkerson Algorithm (video)](https://www.youtube.com/watch?v=v1VgJmkEJW0)
- [Network Flows (video)](https://www.youtube.com/watch?v=2vhN4Ice5jI) - [Network Flows (video)](https://www.youtube.com/watch?v=2vhN4Ice5jI)
- ### Disjoint Sets & Union Find * ### Disjoint Sets & Union Find
- [UCB 61B - Disjoint Sets; Sorting & selection (video)](https://archive.org/details/ucberkeley_webcast_MAEGXTwmUsI) - [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) - [Sedgewick Algorithms - Union-Find (6 videos)](https://www.coursera.org/learn/algorithms-part1/home/week/1)
- ### Math for Fast Processing * ### Math for Fast Processing
- [Integer Arithmetic, Karatsuba Multiplication (video)](https://www.youtube.com/watch?v=eCaXlAaN2uE&index=11&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb) - [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) - [The Chinese Remainder Theorem (used in cryptography) (video)](https://www.youtube.com/watch?v=ru7mWZJlRQg)
- ### Treap * ### Treap
- Combination of a binary search tree and a heap - Combination of a binary search tree and a heap
- [Treap](https://en.wikipedia.org/wiki/Treap) - [Treap](https://en.wikipedia.org/wiki/Treap)
- [Data Structures: Treaps explained (video)](https://www.youtube.com/watch?v=6podLUYinH8) - [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) - [Applications in set operations](https://www.cs.cmu.edu/~scandal/papers/treaps-spaa98.pdf)
- ### Linear Programming (videos) * ### Linear Programming (videos)
- [Linear Programming](https://www.youtube.com/watch?v=M4K6HYLHREQ) - [Linear Programming](https://www.youtube.com/watch?v=M4K6HYLHREQ)
- [Finding minimum cost](https://www.youtube.com/watch?v=2ACJ9ewUC6U) - [Finding minimum cost](https://www.youtube.com/watch?v=2ACJ9ewUC6U)
- [Finding maximum value](https://www.youtube.com/watch?v=8AA_81xI3ik) - [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) - [Solve Linear Equations with Python - Simplex Algorithm](https://www.youtube.com/watch?v=44pAWI7v5Zk)
- ### Geometry, Convex hull (videos) * ### Geometry, Convex hull (videos)
- [Graph Alg. IV: Intro to geometric algorithms - Lecture 9](https://youtu.be/XIAQRlNkJAw?list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm&t=3164) - [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) - [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) - [Divide & Conquer: Convex Hull, Median Finding](https://www.youtube.com/watch?v=EzeYI7p9MjU&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp&index=2)
- ### Discrete math * ### Discrete math
- see videos below - see videos below
- ### Machine Learning * ### Machine Learning
- Why ML? - Why ML?
- [How Google Is Remaking Itself As A Machine Learning First Company](https://backchannel.com/how-google-is-remaking-itself-as-a-machine-learning-first-company-ada63defcb70) - [How Google Is Remaking Itself As A Machine Learning First Company](https://backchannel.com/how-google-is-remaking-itself-as-a-machine-learning-first-company-ada63defcb70)
- [Large-Scale Deep Learning for Intelligent Computer Systems (video)](https://www.youtube.com/watch?v=QSaZGT4-6EY) - [Large-Scale Deep Learning for Intelligent Computer Systems (video)](https://www.youtube.com/watch?v=QSaZGT4-6EY)
@ -1758,14 +1826,12 @@ You're never really done.
- [Tensorflow Tutorials](https://www.tensorflow.org/versions/r0.11/tutorials/index.html) - [Tensorflow Tutorials](https://www.tensorflow.org/versions/r0.11/tutorials/index.html)
- [Practical Guide to implementing Neural Networks in Python (using Theano)](http://www.analyticsvidhya.com/blog/2016/04/neural-networks-python-theano/) - [Practical Guide to implementing Neural Networks in Python (using Theano)](http://www.analyticsvidhya.com/blog/2016/04/neural-networks-python-theano/)
- Courses: - Courses:
- [Great starter course: Machine Learning](https://www.coursera.org/learn/machine-learning) - [Great starter course: Machine Learning](https://www.coursera.org/learn/machine-learning) - [videos only](https://www.youtube.com/playlist?list=PLZ9qNFMHZ-A4rycgrgOYma6zxF4BZGGPW) - see videos 12-18 for a review of linear algebra (14 and 15 are duplicates)
- [videos only](https://www.youtube.com/playlist?list=PLZ9qNFMHZ-A4rycgrgOYma6zxF4BZGGPW)
- see videos 12-18 for a review of linear algebra (14 and 15 are duplicates)
- [Neural Networks for Machine Learning](https://www.coursera.org/learn/neural-networks) - [Neural Networks for Machine Learning](https://www.coursera.org/learn/neural-networks)
- [Google's Deep Learning Nanodegree](https://www.udacity.com/course/deep-learning--ud730) - [Google's Deep Learning Nanodegree](https://www.udacity.com/course/deep-learning--ud730)
- [Google/Kaggle Machine Learning Engineer Nanodegree](https://www.udacity.com/course/machine-learning-engineer-nanodegree-by-google--nd009) - [Google/Kaggle Machine Learning Engineer Nanodegree](https://www.udacity.com/course/machine-learning-engineer-nanodegree-by-google--nd009)
- [Self-Driving Car Engineer Nanodegree](https://www.udacity.com/drive) - [Self-Driving Car Engineer Nanodegree](https://www.udacity.com/drive)
- [Metis Online Course ($99 for 2 months)](http://www.thisismetis.com/explore-data-science) - [Metis Online Course (\$99 for 2 months)](http://www.thisismetis.com/explore-data-science)
- Resources: - Resources:
- Books: - Books:
- [Python Machine Learning](https://www.amazon.com/Python-Machine-Learning-Sebastian-Raschka/dp/1783555130/) - [Python Machine Learning](https://www.amazon.com/Python-Machine-Learning-Sebastian-Raschka/dp/1783555130/)
@ -1797,8 +1863,8 @@ 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) - [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) - [more flavor](http://docs.google.com/a/cleancoder.com/viewer?a=v&pid=explorer&chrome=true&srcid=0BwhCYaYDn8EgMjdlMWIzNGUtZTQ0NC00ZjQ5LTkwYzQtZjRhMDRlNTQ3ZGMz&hl=en)
* **Union-Find**
- **Union-Find**
- [Overview](https://www.coursera.org/learn/data-structures/lecture/JssSY/overview) - [Overview](https://www.coursera.org/learn/data-structures/lecture/JssSY/overview)
- [Naive Implementation](https://www.coursera.org/learn/data-structures/lecture/EM5D0/naive-implementations) - [Naive Implementation](https://www.coursera.org/learn/data-structures/lecture/EM5D0/naive-implementations)
- [Trees](https://www.coursera.org/learn/data-structures/lecture/Mxu0w/trees) - [Trees](https://www.coursera.org/learn/data-structures/lecture/Mxu0w/trees)
@ -1806,7 +1872,8 @@ You're never really done.
- [Path Compression](https://www.coursera.org/learn/data-structures/lecture/Q9CVI/path-compression) - [Path Compression](https://www.coursera.org/learn/data-structures/lecture/Q9CVI/path-compression)
- [Analysis Options](https://www.coursera.org/learn/data-structures/lecture/GQQLN/analysis-optional) - [Analysis Options](https://www.coursera.org/learn/data-structures/lecture/GQQLN/analysis-optional)
- **More Dynamic Programming** (videos) * **More Dynamic Programming** (videos)
- [6.006: Dynamic Programming I: Fibonacci, Shortest Paths](https://www.youtube.com/watch?v=OQ5jsbhAv_M&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=19) - [6.006: Dynamic Programming I: Fibonacci, Shortest Paths](https://www.youtube.com/watch?v=OQ5jsbhAv_M&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=19)
- [6.006: Dynamic Programming II: Text Justification, Blackjack](https://www.youtube.com/watch?v=ENyox7kNKeY&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=20) - [6.006: Dynamic Programming II: Text Justification, Blackjack](https://www.youtube.com/watch?v=ENyox7kNKeY&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=20)
- [6.006: DP III: Parenthesization, Edit Distance, Knapsack](https://www.youtube.com/watch?v=ocZMDMZwhCY&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=21) - [6.006: DP III: Parenthesization, Edit Distance, Knapsack](https://www.youtube.com/watch?v=ocZMDMZwhCY&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=21)
@ -1815,11 +1882,13 @@ You're never really done.
- [6.046: Dynamic Programming: All-Pairs Shortest Paths](https://www.youtube.com/watch?v=NzgFUwOaoIw&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp&index=15) - [6.046: Dynamic Programming: All-Pairs Shortest Paths](https://www.youtube.com/watch?v=NzgFUwOaoIw&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp&index=15)
- [6.046: Dynamic Programming (student recitation)](https://www.youtube.com/watch?v=krZI60lKPek&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp&index=12) - [6.046: Dynamic Programming (student recitation)](https://www.youtube.com/watch?v=krZI60lKPek&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp&index=12)
- **Advanced Graph Processing** (videos) * **Advanced Graph Processing** (videos)
- [Synchronous Distributed Algorithms: Symmetry-Breaking. Shortest-Paths Spanning Trees](https://www.youtube.com/watch?v=mUBmcbbJNf4&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp&index=27) - [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) - [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 **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 - 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 - 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) - [MIT 6.042J - Independence](https://www.youtube.com/watch?v=l1BCv3qqW4A&index=20&list=PLB7540DEDD482705B)
@ -1829,9 +1898,10 @@ You're never really done.
- [MIT 6.042J - Large Deviations](https://www.youtube.com/watch?v=q4mwO2qS2z4&index=24&list=PLB7540DEDD482705B) - [MIT 6.042J - Large Deviations](https://www.youtube.com/watch?v=q4mwO2qS2z4&index=24&list=PLB7540DEDD482705B)
- [MIT 6.042J - Random Walks](https://www.youtube.com/watch?v=56iFMY8QW2k&list=PLB7540DEDD482705B&index=25) - [MIT 6.042J - Random Walks](https://www.youtube.com/watch?v=56iFMY8QW2k&list=PLB7540DEDD482705B&index=25)
- [Simonson: Approximation Algorithms (video)](https://www.youtube.com/watch?v=oDniZCmNmNw&list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm&index=19) * [Simonson: Approximation Algorithms (video)](https://www.youtube.com/watch?v=oDniZCmNmNw&list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm&index=19)
* **String Matching**
- **String Matching**
- Rabin-Karp (videos): - Rabin-Karp (videos):
- [Rabin Karps Algorithm](https://www.coursera.org/learn/data-structures/lecture/c0Qkw/rabin-karps-algorithm) - [Rabin Karps Algorithm](https://www.coursera.org/learn/data-structures/lecture/c0Qkw/rabin-karps-algorithm)
- [Precomputing](https://www.coursera.org/learn/data-structures/lecture/nYrc8/optimization-precomputation) - [Precomputing](https://www.coursera.org/learn/data-structures/lecture/nYrc8/optimization-precomputation)
@ -1848,7 +1918,7 @@ You're never really done.
- nice explanation of tries - nice explanation of tries
- can be skipped - can be skipped
- **Sorting** * **Sorting**
- Stanford lectures on sorting: - Stanford lectures on sorting:
- [Lecture 15 | Programming Abstractions (video)](https://www.youtube.com/watch?v=ENp00xylP7c&index=15&list=PLFE6E58F856038C69) - [Lecture 15 | Programming Abstractions (video)](https://www.youtube.com/watch?v=ENp00xylP7c&index=15&list=PLFE6E58F856038C69)
@ -1881,6 +1951,7 @@ Sit back and enjoy. "Netflix and skill" :P
- [Discrete Mathematics Part 1 by Sarada Herke (5 videos)](https://www.youtube.com/playlist?list=PLGxuz-nmYlQPOc4w1Kp2MZrdqOOm4Jxeo) - [Discrete Mathematics Part 1 by Sarada Herke (5 videos)](https://www.youtube.com/playlist?list=PLGxuz-nmYlQPOc4w1Kp2MZrdqOOm4Jxeo)
- CSE373 - Analysis of Algorithms (25 videos) - CSE373 - Analysis of Algorithms (25 videos)
- [Skiena lectures from Algorithm Design Manual](https://www.youtube.com/watch?v=ZFjhkohHdAA&list=PLOtl7M3yp-DV69F32zdK7YJcNXpTunF2b&index=1) - [Skiena lectures from Algorithm Design Manual](https://www.youtube.com/watch?v=ZFjhkohHdAA&list=PLOtl7M3yp-DV69F32zdK7YJcNXpTunF2b&index=1)
- [UC Berkeley 61B (Spring 2014): Data Structures (25 videos)](https://archive.org/details/ucberkeley-webcast-PL-XXv-cvA_iAlnI-BQr9hjqADPBtujFJd) - [UC Berkeley 61B (Spring 2014): Data Structures (25 videos)](https://archive.org/details/ucberkeley-webcast-PL-XXv-cvA_iAlnI-BQr9hjqADPBtujFJd)
@ -1920,6 +1991,7 @@ Sit back and enjoy. "Netflix and skill" :P
- [Stanford: Programming Paradigms (27 videos)](https://www.youtube.com/view_play_list?p=9D558D49CA734A02) - [Stanford: Programming Paradigms (27 videos)](https://www.youtube.com/view_play_list?p=9D558D49CA734A02)
- [Introduction to Cryptography by Christof Paar](https://www.youtube.com/playlist?list=PL6N5qY2nvvJE8X75VkXglSrVhLv1tVcfy) - [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/) - [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) - [Mining Massive Datasets - Stanford University (94 videos)](https://www.youtube.com/playlist?list=PLLssT5z_DsK9JDLcT8T62VtzwyW9LNepV)
@ -1938,7 +2010,7 @@ Sit back and enjoy. "Netflix and skill" :P
- [implemented in Go](https://godoc.org/github.com/thomas11/csp) - [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) - [2003: The Google File System](http://static.googleusercontent.com/media/research.google.com/en//archive/gfs-sosp2003.pdf)
- replaced by Colossus in 2012 - 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? - 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: Bigtable: A Distributed Storage System for Structured Data](https://static.googleusercontent.com/media/research.google.com/en//archive/bigtable-osdi06.pdf)
- [An Inside Look at Google BigQuery](https://cloud.google.com/files/BigQueryTechnicalWP.pdf) - [An Inside Look at Google BigQuery](https://cloud.google.com/files/BigQueryTechnicalWP.pdf)
@ -1959,11 +2031,10 @@ Sit back and enjoy. "Netflix and skill" :P
- [2014: Machine Learning: The High-Interest Credit Card of Technical Debt](http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43146.pdf) - [2014: Machine Learning: The High-Interest Credit Card of Technical Debt](http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43146.pdf)
- [2015: Continuous Pipelines at Google](http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43790.pdf) - [2015: Continuous Pipelines at Google](http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43790.pdf)
- [2015: High-Availability at Massive Scale: Building Google’s Data Infrastructure for Ads](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/44686.pdf) - [2015: High-Availability at Massive Scale: Building Google’s Data Infrastructure for Ads](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/44686.pdf)
- [2015: TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](http://download.tensorflow.org/paper/whitepaper2015.pdf ) - [2015: TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](http://download.tensorflow.org/paper/whitepaper2015.pdf)
- [2015: How Developers Search for Code: A Case Study](http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43835.pdf) - [2015: How Developers Search for Code: A Case Study](http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43835.pdf)
- [2016: Borg, Omega, and Kubernetes](http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/44843.pdf) - [2016: Borg, Omega, and Kubernetes](http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/44843.pdf)
## LICENSE ## LICENSE
[CC-BY-SA-4.0](./LICENSE.txt) [CC-BY-SA-4.0](./LICENSE.txt)