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@ -7,7 +7,7 @@
>
> I studied about 8-12 hours a day, for several months. This is my story: [Why I studied full-time for 8 months for a Google interview](https://medium.freecodecamp.org/why-i-studied-full-time-for-8-months-for-a-google-interview-cc662ce9bb13)
>
> **Please Note:** You won't need to study as much as I did. I wasted a lot of time on things I didn't need to know. More info about that below. I'll help you get there without wasting your precious time.
> **Please Note:** You won't need to study as much as I did. I wasted a lot of time on things I didn't need to know. More info about that is below. I'll help you get there without wasting your precious time.
>
> 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.
@ -228,9 +228,9 @@ software development/engineering roles.
If you want to work as a software engineer for a large company, these are the things you have to know.
If you missed out on getting a degree in computer science, like I did, this will catch you up and save four years of your life.
If you missed out on getting a degree in computer science, as I did, this will catch you up and save four years of your life.
When I started this project, I didn't know a stack from a heap, didn't know Big-O anything, or anything about trees, or how to
When I started this project, I didn't know a stack from a heap, didn't know Big-O anything, anything about trees, or how to
traverse a graph. If I had to code a sorting algorithm, I can tell ya it would have been terrible.
Every data structure I had ever used was built into the language, and I didn't know how they worked
under the hood at all. I never had to manage memory unless a process I was running would give an "out of
@ -243,7 +243,7 @@ It's a long plan. It may take you months. If you are familiar with a lot of this
Everything below is an outline, and you should tackle the items in order from top to bottom.
I'm using GitHub's special markdown flavor, including tasks lists to track progress.
I'm using GitHub's special markdown flavor, including task lists to track progress.
- [More about GitHub-flavored markdown](https://guides.github.com/features/mastering-markdown/#GitHub-flavored-markdown)
### If you don't want to use git
@ -303,16 +303,16 @@ not just when a specific online course is in session.
You'll need to choose a programming language for the coding interviews you do,
but you'll also need to find a language that you can use to study computer science concepts.
Preferably the language would be the same, so that you only need to be proficient in one.
Preferably the language would be the same so that you only need to be proficient in one.
### For this Study Plan
When I did the study plan, I used 2 languages for most of it: C and Python
* C: Very low level. Allows you to deal with pointers and memory allocation/deallocation, so you feel the data structures
and algorithms in your bones. In higher level languages like Python or Java, these are hidden from you. In day to day work, that's terrific,
and algorithms in your bones. In higher-level languages like Python or Java, these are hidden from you. In day-to-day work, that's terrific,
but when you're learning how these low-level data structures are built, it's great to feel close to the metal.
- C is everywhere. You'll see examples in books, lectures, videos, *everywhere* while you're studying.
- C is everywhere. You'll see examples in books, lectures, and videos *everywhere* while you're studying.
- [The 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
you'll quickly get proficient. Understanding C helps you understand how programs and memory work.
@ -340,7 +340,7 @@ You can use a language you are comfortable in to do the coding part of the inter
- Java
- Python
You could also use these, but read around first. There may be caveats:
You could also use these but read around first. There may be caveats:
- JavaScript
- Ruby
@ -349,7 +349,7 @@ Here is an article I wrote about choosing a language for the interview:
[Pick One Language for the Coding Interview](https://startupnextdoor.com/important-pick-one-language-for-the-coding-interview/).
This is the original article my post was based on: [Choosing a Programming Language for Interviews](https://web.archive.org/web/20210516054124/http://blog.codingforinterviews.com/best-programming-language-jobs/)
You need to be very comfortable in the language and be knowledgeable.
You need to be very comfortable with the language and be knowledgeable.
Read more about choices:
- [Choose the Right Language for Your Coding Interview](http://www.byte-by-byte.com/choose-the-right-language-for-your-coding-interview/)
@ -429,7 +429,7 @@ Here are some mistakes I made so you'll have a better experience. And you'll sav
### 1. You Won't Remember it All
I watched hours of videos and took copious notes, and months later there was much I didn't remember. I spent 3 days going
through my notes and making flashcards, so I could review. I didn't need all of that knowledge.
through my notes and making flashcards, so I could review them. I didn't need all of that knowledge.
Please, read so you won't make my mistakes:
@ -458,12 +458,12 @@ same card and answer it several times correctly before you really know it. Repet
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. It's user-friendly, available on all platforms and has a cloud sync system.
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.
My flashcard database in Anki format: https://ankiweb.net/shared/info/25173560 (thanks [@xiewenya](https://github.com/xiewenya)).
Some students have mentioned formatting issues with white space that can be fixed by doing the following: open deck, edit card, click cards, select the "styling" radio button, add the member "white-space: pre;" to the card class.
Some students have mentioned formatting issues with white space that can be fixed by doing the following: open deck, edit card, click cards, select the "styling" radio button, and add the member "white-space: pre;" to the card class.
### 3. Do Coding Interview Questions While You're Learning
@ -471,7 +471,7 @@ THIS IS VERY IMPORTANT.
Start doing coding interview questions while you're learning data structures and algorithms.
You need to apply what you're learning to solving problems, or you'll forget. I made this mistake.
You need to apply what you're learning to solve problems, or you'll forget. I made this mistake.
Once you've learned a topic, and feel somewhat comfortable with it, for example, **linked lists**:
1. Open one of the [coding interview books](#interview-prep-books) (or coding problem websites, listed below)
@ -482,7 +482,7 @@ Once you've learned a topic, and feel somewhat comfortable with it, for example,
**Keep doing problems while you're learning all this stuff, not after.**
You're not being hired for knowledge, but how you apply the knowledge.
You're not being hired for knowledge, but for how you apply the knowledge.
There are many resources for this, listed below. Keep going.
@ -504,7 +504,7 @@ These are prevalent technologies but not part of this study plan:
This course goes over a lot of subjects. Each will probably take you a few days, or maybe even a week or more. It depends on your schedule.
Each day, take the next subject in the list, watch some videos about that subject, and then write an implementation
of that data structure or algorithm in the language you chose for this course.
of that data structure or algorithm in the language, you chose for this course.
You can see my code here:
- [C](https://github.com/jwasham/practice-c)
@ -527,7 +527,7 @@ Why you need to practice doing programming problems:
- Coming up with time and space complexity for your solutions (see Big-O below)
- Testing your solutions
There is a great intro for methodical, communicative problem solving in an interview. You'll get this from the programming
There is a great intro for methodical, communicative problem-solving in an interview. You'll get this from the programming
interview books, too, but I found this outstanding:
[Algorithm design canvas](http://www.hiredintech.com/algorithm-design/)
@ -535,7 +535,7 @@ Write code on a whiteboard or paper, not a computer. Test with some sample input
If you don't have a whiteboard at home, pick up a large drawing pad from an art store. You can sit on the couch and practice.
This is my "sofa whiteboard". I added the pen in the photo just for scale. If you use a pen, you'll wish you could erase.
Gets messy quick. **I use a pencil and eraser.**
Gets messy quickly. **I use a pencil and eraser.**
![my sofa whiteboard](https://d3j2pkmjtin6ou.cloudfront.net/art_board_sm_2.jpg)
@ -554,12 +554,12 @@ Coding Interview Question Videos:
- [Tushar Roy (5 playlists)](https://www.youtube.com/user/tusharroy2525/playlists?shelf_id=2&view=50&sort=dd)
- Super for walkthroughs of problem solutions
- [Nick White - LeetCode Solutions (187 Videos)](https://www.youtube.com/playlist?list=PLU_sdQYzUj2keVENTP0a5rdykRSgg9Wp-)
- Good explanations of solution and the code
- Good explanations of the solution and the code
- You can watch several in a short time
- [FisherCoder - LeetCode Solutions](https://youtube.com/FisherCoder)
- [Neetcode - BLIND 75 LeetCode Solutions](https://www.youtube.com/watch?v=KLlXCFG5TnA&list=PLot-Xpze53ldVwtstag2TL4HQhAnC8ATf)
- Good explanations of solution and the python code
- Also checkout [excel sheet](https://docs.google.com/spreadsheets/d/1A2PaQKcdwO_lwxz9bAnxXnIQayCouZP6d-ENrBz_NXc/edit#gid=0) for all question list
- Good explanations of the solution and the python code
- Also check out [excel sheet](https://docs.google.com/spreadsheets/d/1A2PaQKcdwO_lwxz9bAnxXnIQayCouZP6d-ENrBz_NXc/edit#gid=0) for all question list
- [Github links](https://github.com/neetcode-gh/leetcode) for all solutions code
- [Neetcode 150](https://neetcode.io/)
@ -600,7 +600,7 @@ But don't forget to do coding problems from above while you learn!
Well, that's about enough of that.
When you go through "Cracking the Coding Interview", there is a chapter on this, and at the end there is a quiz to see
When you go through "Cracking the Coding Interview", there is a chapter on this, and at the end, there is a quiz to see
if you can identify the runtime complexity of different algorithms. It's a super review and test.
## Data Structures
@ -615,13 +615,13 @@ if you can identify the runtime complexity of different algorithms. It's a super
- [ ] Practice coding using arrays and pointers, and pointer math to jump to an index instead of using indexing.
- [ ] New raw data array with allocated memory
- can allocate int array under the hood, just not use its features
- start with 16, or if starting number is greater, use power of 2 - 16, 32, 64, 128
- start with 16, or if starting number is greater, use the power of 2 - 16, 32, 64, 128
- [ ] size() - number of items
- [ ] capacity() - number of items it can hold
- [ ] is_empty()
- [ ] at(index) - returns item at given index, blows up if index out of bounds
- [ ] push(item)
- [ ] insert(index, item) - inserts item at index, shifts that index's value and trailing elements to the right
- [ ] insert(index, item) - inserts an item at index, shifts that index's value and trailing elements to the right
- [ ] prepend(item) - can use insert above at index 0
- [ ] pop() - remove from end, return value
- [ ] delete(index) - delete item at index, shifting all trailing elements left
@ -629,9 +629,9 @@ if you can identify the runtime complexity of different algorithms. It's a super
- [ ] find(item) - looks for value and returns first index with that value, -1 if not found
- [ ] resize(new_capacity) // private function
- when you reach capacity, resize to double the size
- when popping an item, if size is 1/4 of capacity, resize to half
- when popping an item, if the size is 1/4 of capacity, resize to half
- [ ] Time
- O(1) to add/remove at end (amortized for allocations for more space), index, or update
- O(1) to add/remove at the end (amortized for allocations for more space), index, or update
- O(n) to insert/remove elsewhere
- [ ] Space
- contiguous in memory, so proximity helps performance
@ -648,7 +648,7 @@ if you can identify the runtime complexity of different algorithms. It's a super
- [Core Linked Lists Vs Arrays (video)](https://www.coursera.org/lecture/data-structures-optimizing-performance/core-linked-lists-vs-arrays-rjBs9)
- [In The Real World Linked Lists Vs Arrays (video)](https://www.coursera.org/lecture/data-structures-optimizing-performance/in-the-real-world-lists-vs-arrays-QUaUd)
- [ ] [Why you should avoid linked lists (video)](https://www.youtube.com/watch?v=YQs6IC-vgmo)
- [ ] Gotcha: you need pointer to pointer knowledge:
- [ ] Gotcha: you need a pointer to pointer knowledge:
(for when you pass a pointer to a function that may change the address where that pointer points)
This page is just to get a grasp on ptr to ptr. I don't recommend this list traversal style. Readability and maintainability suffer due to cleverness.
- [Pointers to Pointers](https://www.eskimo.com/~scs/cclass/int/sx8.html)
@ -662,7 +662,7 @@ if you can identify the runtime complexity of different algorithms. It's a super
- [ ] pop_back() - removes end item and returns its value
- [ ] front() - get value of front item
- [ ] back() - get value of end item
- [ ] insert(index, value) - insert value at index, so current item at that index is pointed to by new item at index
- [ ] insert(index, value) - insert value at index, so a current item at that index is pointed to by a new item at index
- [ ] erase(index) - removes node at given index
- [ ] value_n_from_end(n) - returns the value of the node at nth position from the end of the list
- [ ] reverse() - reverses the list
@ -673,12 +673,12 @@ if you can identify the runtime complexity of different algorithms. It's a super
- ### Stack
- [ ] [Stacks (video)](https://www.coursera.org/lecture/data-structures/stacks-UdKzQ)
- [ ] Will not implement. Implementing with array is trivial
- [ ] Will not implement. Implementing with the array is trivial
- ### Queue
- [ ] [Queue (video)](https://www.coursera.org/lecture/data-structures/queues-EShpq)
- [ ] [Circular buffer/FIFO](https://en.wikipedia.org/wiki/Circular_buffer)
- [ ] Implement using linked-list, with tail pointer:
- [ ] Implement using linked-list, with a tail pointer:
- enqueue(value) - adds value at position at tail
- dequeue() - returns value and removes least recently added element (front)
- empty()
@ -688,8 +688,8 @@ if you can identify the runtime complexity of different algorithms. It's a super
- empty()
- full()
- [ ] Cost:
- a bad implementation using linked list where you enqueue at head and dequeue at tail would be O(n)
because you'd need the next to last element, causing a full traversal each dequeue
- a bad implementation using a linked list where you enqueue at the head and dequeue at the tail would be O(n)
because you'd need the next to last element, causing a full traversal of each dequeue
- enqueue: O(1) (amortized, linked list and array [probing])
- dequeue: O(1) (linked list and array)
- empty: O(1) (linked list and array)
@ -712,8 +712,8 @@ if you can identify the runtime complexity of different algorithms. It's a super
- [Instant Uploads And Storage Optimization In Dropbox (video)](https://www.coursera.org/lecture/data-structures/instant-uploads-and-storage-optimization-in-dropbox-DvaIb)
- [Distributed Hash Tables (video)](https://www.coursera.org/lecture/data-structures/distributed-hash-tables-tvH8H)
- [ ] Implement with array using linear probing
- hash(k, m) - m is size of hash table
- [ ] Implement with the array using linear probing
- hash(k, m) - m is the size of the hash table
- add(key, value) - if key already exists, update value
- exists(key)
- get(key)
@ -727,7 +727,7 @@ if you can identify the runtime complexity of different algorithms. It's a super
- [ ] [detail](https://www.topcoder.com/thrive/articles/Binary%20Search)
- [ ] [blueprint](https://leetcode.com/discuss/general-discussion/786126/python-powerful-ultimate-binary-search-template-solved-many-problems)
- [ ] Implement:
- binary search (on sorted array of integers)
- binary search (on a sorted array of integers)
- binary search using recursion
- ### Bitwise operations
@ -848,11 +848,11 @@ if you can identify the runtime complexity of different algorithms. It's a super
- I wouldn't recommend sorting a linked list, but merge sort is doable.
- [Merge Sort For Linked List](http://www.geeksforgeeks.org/merge-sort-for-linked-list/)
- For heapsort, see Heap data structure above. Heap sort is great, but not stable
- For heapsort, see the Heap data structure above. Heap sort is great, but not stable
- [ ] [Sedgewick - Mergesort (5 videos)](https://www.coursera.org/learn/algorithms-part1/home/week/3)
- [ ] [1. Mergesort](https://www.coursera.org/lecture/algorithms-part1/mergesort-ARWDq)
- [ ] [2. Bottom up Mergesort](https://www.coursera.org/learn/algorithms-part1/lecture/PWNEl/bottom-up-mergesort)
- [ ] [2. Bottom-up Mergesort](https://www.coursera.org/learn/algorithms-part1/lecture/PWNEl/bottom-up-mergesort)
- [ ] [3. Sorting Complexity](https://www.coursera.org/lecture/algorithms-part1/sorting-complexity-xAltF)
- [ ] [4. Comparators](https://www.coursera.org/lecture/algorithms-part1/comparators-9FYhS)
- [ ] [5. Stability](https://www.coursera.org/learn/algorithms-part1/lecture/pvvLZ/stability)
@ -890,7 +890,7 @@ if you can identify the runtime complexity of different algorithms. It's a super
- [ ] Mergesort: O(n log n) average and worst case
- [ ] Quicksort O(n log n) average case
- Selection sort and insertion sort are both O(n^2) average and worst case
- For heapsort, see Heap data structure above
- For heapsort, see the Heap data structure above
- [ ] Not required, but I recommended them:
- [ ] [Sedgewick - Radix Sorts (6 videos)](https://www.coursera.org/learn/algorithms-part2/home/week/3)
@ -911,7 +911,7 @@ If you need more detail on this subject, see "Sorting" section in [Additional De
## Graphs
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:
- There are 4 basic ways to represent a graph in memory:
@ -920,7 +920,7 @@ Graphs can be used to represent many problems in computer science, so this secti
- adjacency list
- adjacency map
- Familiarize yourself with each representation and its pros & cons
- BFS and DFS - know their computational complexity, their trade offs, and how to implement them in real code
- BFS and DFS - know their computational complexity, their trade-offs, and how to implement them in real code
- When asked a question, look for a graph-based solution first, then move on if none
- [ ] MIT(videos):
@ -962,7 +962,7 @@ Graphs can be used to represent many problems in computer science, so this secti
- [ ] single-source shortest path (Dijkstra)
- [ ] minimum spanning tree
- DFS-based algorithms (see Aduni videos above):
- [ ] check for cycle (needed for topological sort, since we'll check for cycle before starting)
- [ ] check for the cycle (needed for topological sort, since we'll check for the cycle before starting)
- [ ] topological sort
- [ ] count connected components in a graph
- [ ] list strongly connected components
@ -1043,10 +1043,10 @@ Graphs can be used to represent many problems in computer science, so this secti
- [ ] Khan Academy:
- Course layout:
- [ ] [Basic Theoretical Probability](https://www.khanacademy.org/math/probability/probability-and-combinatorics-topic)
- Just the videos - 41 (each are simple and each are short):
- Just the videos - 41 (each is simple and each is short):
- [ ] [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,
and be able to recognize them when an interviewer asks you them in disguise.
- Know what NP-complete means.
@ -1064,7 +1064,7 @@ Graphs can be used to represent many problems in computer science, so this secti
- [ ] [Complexity: P, NP, NP-completeness, Reductions (video)](https://www.youtube.com/watch?v=eHZifpgyH_4&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp&index=22)
- [ ] [Complexity: Approximation Algorithms (video)](https://www.youtube.com/watch?v=MEz1J9wY2iM&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp&index=24)
- [ ] [Complexity: Fixed-Parameter Algorithms (video)](https://www.youtube.com/watch?v=4q-jmGrmxKs&index=25&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp)
- Peter Norvig discusses near-optimal solutions to traveling salesman problem:
- Peter Norvig discusses near-optimal solutions to the traveling salesman problem:
- [Jupyter Notebook](http://nbviewer.jupyter.org/url/norvig.com/ipython/TSP.ipynb)
- Pages 1048 - 1140 in CLRS if you have it.
@ -1099,7 +1099,7 @@ Graphs can be used to represent many problems in computer science, so this secti
- Mutexes
- Semaphores
- Monitors
- How they work?
- How do they work?
- Deadlock
- Livelock
- CPU activity, interrupts, context switching
@ -1107,8 +1107,8 @@ Graphs can be used to represent many problems in computer science, so this secti
- [Paging, segmentation and virtual memory (video)](https://youtu.be/O4nwUqQodAg)
- [Interrupts (video)](https://youtu.be/iKlAWIKEyuw)
- Process resource needs (memory: code, static storage, stack, heap, and also file descriptors, i/o)
- Thread resource needs (shares above (minus stack) with other threads in the same process but each has its own pc, stack counter, registers, and stack)
- Forking is really copy on write (read-only) until the new process writes to memory, then it does a full copy.
- Thread resource needs (shares above (minus stack) with other threads in the same process but each has its pc, stack counter, registers, and stack)
- Forking is copying on write (read-only) until the new process writes to memory, then it does a full copy.
- Context switching
- How context switching is initiated by the operating system and underlying hardware?
- [ ] [threads in C++ (series - 10 videos)](https://www.youtube.com/playlist?list=PL5jc9xFGsL8E12so1wlMS0r0hTQoJL74M)
@ -1147,12 +1147,12 @@ Graphs can be used to represent many problems in computer science, so this secti
- [ ] [5. Rabin-Karp](https://www.coursera.org/lecture/algorithms-part2/rabin-karp-3KiqT)
- [ ] [Search pattern in text (video)](https://www.coursera.org/learn/data-structures/lecture/tAfHI/search-pattern-in-text)
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 the "String Matching" section in [Additional Detail on Some Subjects](#additional-detail-on-some-subjects).
- ### Tries
- Note there are different kinds of tries. Some have prefixes, some don't, and some use string instead of bits
to track the path
- I read through code, but will not implement
- I read through the code, but will not implement
- [ ] [Sedgewick - Tries (3 videos)](https://www.coursera.org/learn/algorithms-part2/home/week/4)
- [ ] [1. R Way Tries](https://www.coursera.org/learn/algorithms-part2/lecture/CPVdr/r-way-tries)
- [ ] [2. Ternary Search Tries](https://www.coursera.org/learn/algorithms-part2/lecture/yQM8K/ternary-search-tries)
@ -1164,7 +1164,7 @@ Graphs can be used to represent many problems in computer science, so this secti
- [ ] [Implementing A Trie (video)](https://www.coursera.org/learn/data-structures-optimizing-performance/lecture/DFvd3/core-implementing-a-trie)
- [ ] [The Trie: A Neglected Data Structure](https://www.toptal.com/java/the-trie-a-neglected-data-structure)
- [ ] [TopCoder - Using Tries](https://www.topcoder.com/thrive/articles/Using%20Tries)
- [ ] [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)
- ### Floating Point Numbers
@ -1178,7 +1178,7 @@ Graphs can be used to represent many problems in computer science, so this secti
- [ ] [Big And Little Endian](https://web.archive.org/web/20180107141940/http://www.cs.umd.edu:80/class/sum2003/cmsc311/Notes/Data/endian.html)
- [ ] [Big Endian Vs Little Endian (video)](https://www.youtube.com/watch?v=JrNF0KRAlyo)
- [ ] [Big And Little Endian Inside/Out (video)](https://www.youtube.com/watch?v=oBSuXP-1Tc0)
- 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 are over your head.
- The first half is enough.
- ### Networking
@ -1217,7 +1217,7 @@ Graphs can be used to represent many problems in computer science, so this secti
## Update Your Resume
- See Resume prep information in the books: "Cracking The Coding Interview" and "Programming Interviews Exposed"
- I don't know how important this is (you can do your own research) but here is an article on making your resume ATS Compliant:
- I don't know how important this is (you can do your research) but here is an article on making your resume ATS Compliant:
- [How to Create or Check if your Resume is ATS Compliant](https://ayedot.com/97/MiniBlog/Meaning-of-ATS-compliant-resume-and-How-to-create-ATS-Resume-for-Free)
- ["This Is What A GOOD Resume Should Look Like" by Gayle McDowell (author of Cracking the Coding Interview)](https://www.careercup.com/resume),
- Note by the author: "This is for a US-focused resume. CVs for India and other countries have different expectations, although many of the points will be the same."
@ -1246,13 +1246,13 @@ Graphs can be used to represent many problems in computer science, so this secti
- [Software Engineer Interview Unleashed (paid course)](https://www.udemy.com/software-engineer-interview-unleashed):
- Learn how to make yourself ready for software engineer interviews from a former Google interviewer.
- [Python for Data Structures, Algorithms, and Interviews (paid course)](https://www.udemy.com/python-for-data-structures-algorithms-and-interviews/):
- A Python centric interview prep course which covers data structures, algorithms, mock interviews and much more.
- A Python-centric interview prep course that covers data structures, algorithms, mock interviews, and much more.
- [Intro to Data Structures and Algorithms using Python (Udacity free course)](https://www.udacity.com/course/data-structures-and-algorithms-in-python--ud513):
- A free Python centric data structures and algorithms course.
- A free Python-centric data structures and algorithms course.
- [Data Structures and Algorithms Nanodegree! (Udacity paid Nanodegree)](https://www.udacity.com/course/data-structures-and-algorithms-nanodegree--nd256):
- Get hands-on practice with over 100 data structures and algorithm exercises and guidance from a dedicated mentor to help prepare you for interviews and on-the-job scenarios.
- [Grokking the Behavioral Interview (Educative free course)](https://www.educative.io/courses/grokking-the-behavioral-interview):
- Many times, it’s not your technical competency that holds you back from landing your dream job, it’s how you perform on the behavioral interview.
- Many times, it’s not your technical competency that holds you back from landing your dream job, it’s how you perform in the behavioral interview.
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
@ -1277,7 +1277,7 @@ Have a story, not just data, about something you accomplished.
- What did you learn at [job x / project y]?
- What would you have done better at [job x / project y]?
- If you find it hard to come up with good answers of these types of interview questions, here are some ideas:
- If you find it hard to come up with good answers to these types of interview questions, here are some ideas:
- [General Interview Questions and their Answers](https://ayedot.com/119/MiniBlog/General-Interview-Questions-and-their-Answers-for-Tech-Jobs)
## Have questions for the interviewer
@ -1292,7 +1292,7 @@ Some of mine (I already may know the answers, but want their opinion or team per
- Do you feel your work environment helps you concentrate?
- What are you working on?
- What do you like about it?
- What is the work life like?
- What is work-life like?
- How is the work/life balance?
## Once You've Got The Job
@ -1342,7 +1342,7 @@ You're never really done.
- Code examples in C
- Cons:
- Can be as dense or impenetrable as CLRS, and in some cases, CLRS may be a better alternative for some subjects
- Chapters 7, 8, 9 can be painful to try to follow, as some items are not explained well or require more brain than I have
- Chapters 7, 8, and 9 can be painful to try to follow, as some items are not explained well or require more brain than I have
- Don't get me wrong: I like Skiena, his teaching style, and mannerisms, but I may not be Stony Brook material
- Algorithm catalog:
- This is the real reason you buy this book.
@ -1365,7 +1365,7 @@ You're never really done.
- Chapter 10 - Instruction Set Architecture
- Chapter 11 - Memory Architecture and Organization
- [Introduction to Algorithms](https://www.amazon.com/Introduction-Algorithms-fourth-Thomas-Cormen/dp/026204630X)
- **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
- [Computer Architecture, Sixth Edition: A Quantitative Approach](https://www.amazon.com/dp/0128119055)
- For a richer, more up-to-date (2017), but longer treatment
@ -1457,16 +1457,16 @@ You're never really done.
- [cheat sheet](https://github.com/jwasham/coding-interview-university/blob/main/extras/cheat%20sheets/system-design.pdf)
- flow:
1. Understand the problem and scope:
- Define the use cases, with interviewer's help
- Define the use cases, with the interviewer's help
- Suggest additional features
- Remove items that interviewer deems out of scope
- Remove items that the interviewer deems out of scope
- Assume high availability is required, add as a use case
2. Think about constraints:
- Ask how many requests per month
- Ask how many requests per second (they may volunteer it or make you do the math)
- Estimate reads vs. writes percentage
- Keep 80/20 rule in mind when estimating
- How much data written per second
- Keep the 80/20 rule in mind when estimating
- How much data is written per second
- Total storage required over 5 years
- How much data read per second
3. Abstract design:
@ -1484,7 +1484,7 @@ You're never really done.
## Additional Learning
I added them to help you become a well-rounded software engineer, and to be aware of certain
I added them to help you become a well-rounded software engineer and to be aware of certain
technologies and algorithms, so you'll have a bigger toolbox.
- ### Compilers
@ -1546,12 +1546,12 @@ You're never really done.
- [Error Checking](https://www.youtube.com/watch?v=wbH2VxzmoZk)
- ### Entropy
- Also see videos below
- Also see the videos below
- Make sure to watch information theory videos first
- [Information Theory, Claude Shannon, Entropy, Redundancy, Data Compression & Bits (video)](https://youtu.be/JnJq3Py0dyM?t=176)
- ### Cryptography
- Also see videos below
- Also see the videos below
- Make sure to watch information theory videos first
- [Khan Academy Series](https://www.khanacademy.org/computing/computer-science/cryptography)
- [Cryptography: Hash Functions](https://www.youtube.com/watch?v=KqqOXndnvic&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp&index=30)
@ -1627,7 +1627,7 @@ You're never really done.
- [Understanding The FFT](http://jakevdp.github.io/blog/2013/08/28/understanding-the-fft/)
- ### Bloom Filter
- Given a Bloom filter with m bits and k hashing functions, both insertion and membership testing are O(k)
- Given a Bloom filter with m bits and k hashing functions, both insertion, and membership testing are O(k)
- [Bloom Filters (video)](https://www.youtube.com/watch?v=-SuTGoFYjZs)
- [Bloom Filters | Mining of Massive Datasets | Stanford University (video)](https://www.youtube.com/watch?v=qBTdukbzc78)
- [Tutorial](http://billmill.org/bloomfilter-tutorial/)
@ -1638,7 +1638,7 @@ You're never really done.
- ### Locality-Sensitive Hashing
- Used to determine the similarity of documents
- The opposite of MD5 or SHA which are used to determine if 2 documents/strings are exactly the same
- The opposite of MD5 or SHA which are used to determine if 2 documents/strings are the same
- [Simhashing (hopefully) made simple](http://ferd.ca/simhashing-hopefully-made-simple.html)
- ### van Emde Boas Trees
@ -1666,7 +1666,7 @@ You're never really done.
- In practice:
From what I can tell, these aren't used much in practice, but I could see where they would be:
The AVL tree is another structure supporting O(log n) search, insertion, and removal. It is more rigidly
balanced than red–black trees, leading to slower insertion and removal but faster retrieval. This makes it
balanced than red-black trees, leading to slower insertion and removal but faster retrieval. This makes it
attractive for data structures that may be built once and loaded without reconstruction, such as language
dictionaries (or program dictionaries, such as the opcodes of an assembler or interpreter)
- [MIT AVL Trees / AVL Sort (video)](https://www.youtube.com/watch?v=FNeL18KsWPc&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=6)
@ -1690,8 +1690,8 @@ You're never really done.
Red–black trees offer worst-case guarantees for insertion time, deletion time, and search time.
Not only does this make them valuable in time-sensitive applications such as real-time applications,
but it makes them valuable building blocks in other data structures which provide worst-case guarantees;
for example, many data structures used in computational geometry can be based on red–black trees, and
the Completely Fair Scheduler used in current Linux kernels uses red–black trees. In the version 8 of Java,
for example, many data structures used in computational geometry can be based on red-black trees, and
the Completely Fair Scheduler used in current Linux kernels uses red-black trees. In version 8 of Java,
the Collection HashMap has been modified such that instead of using a LinkedList to store identical elements with poor
hashcodes, a Red-Black tree is used
- [Aduni - Algorithms - Lecture 4 (link jumps to starting point) (video)](https://youtu.be/1W3x0f_RmUo?list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm&t=3871)
@ -1702,24 +1702,24 @@ You're never really done.
- **2-3 search trees**
- In practice:
2-3 trees have faster inserts at the expense of slower searches (since height is more compared to AVL trees).
- You would use 2-3 tree very rarely because its implementation involves different types of nodes. Instead, people use Red Black trees.
- You would use 2-3 trees very rarely because its implementation involves different types of nodes. Instead, people use Red-Black trees.
- [23-Tree Intuition and Definition (video)](https://www.youtube.com/watch?v=C3SsdUqasD4&list=PLA5Lqm4uh9Bbq-E0ZnqTIa8LRaL77ica6&index=2)
- [Binary View of 23-Tree](https://www.youtube.com/watch?v=iYvBtGKsqSg&index=3&list=PLA5Lqm4uh9Bbq-E0ZnqTIa8LRaL77ica6)
- [2-3 Trees (student recitation) (video)](https://www.youtube.com/watch?v=TOb1tuEZ2X4&index=5&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp)
- **2-3-4 Trees (aka 2-4 trees)**
- In practice:
For every 2-4 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
important tool for understanding the logic behind red–black trees, and this is why many introductory algorithm texts introduce
2-4 trees just before red–black trees, even though **2-4 trees are not often used in practice**.
For every 2-4 trees, there are corresponding red-black trees with data elements in the same order. The insertion and deletion
operations on 2-4 trees are also equivalent to color-flipping and rotations in red-black trees. This makes 2-4 trees an
important tool for understanding the logic behind red-black trees, and this is why many introductory algorithm texts introduce
2-4 trees just before red-black trees, even though **2-4 trees are not often used in practice**.
- [CS 61B Lecture 26: Balanced Search Trees (video)](https://archive.org/details/ucberkeley_webcast_zqrqYXkth6Q)
- [Bottom Up 234-Trees (video)](https://www.youtube.com/watch?v=DQdMYevEyE4&index=4&list=PLA5Lqm4uh9Bbq-E0ZnqTIa8LRaL77ica6)
- [Top Down 234-Trees (video)](https://www.youtube.com/watch?v=2679VQ26Fp4&list=PLA5Lqm4uh9Bbq-E0ZnqTIa8LRaL77ica6&index=5)
- **N-ary (K-ary, M-ary) trees**
- note: the N or K is the branching factor (max branches)
- binary trees are a 2-ary tree, with branching factor = 2
- binary trees are 2-ary trees, with branching factor = 2
- 2-3 trees are 3-ary
- [K-Ary Tree](https://en.wikipedia.org/wiki/K-ary_tree)
@ -1737,11 +1737,11 @@ You're never really done.
- [B-Tree Deletion (video)](https://www.youtube.com/watch?v=svfnVhJOfMc&index=8&list=PLA5Lqm4uh9Bbq-E0ZnqTIa8LRaL77ica6)
- [MIT 6.851 - Memory Hierarchy Models (video)](https://www.youtube.com/watch?v=V3omVLzI0WE&index=7&list=PLUl4u3cNGP61hsJNdULdudlRL493b-XZf)
- covers cache-oblivious B-Trees, very interesting data structures
- the first 37 minutes are very technical, may be skipped (B is block size, cache line size)
- the first 37 minutes are very technical and may be skipped (B is block size, cache line size)
- ### k-D Trees
- Great for finding number of points in a rectangle or higher dimension object
- Great for finding a number of points in a rectangle or higher dimension object
- A good fit for k-nearest neighbors
- [kNN K-d tree algorithm (video)](https://www.youtube.com/watch?v=Y4ZgLlDfKDg)
@ -1789,7 +1789,7 @@ You're never really done.
## Additional Detail on Some Subjects
I added these to reinforce some ideas already presented above, but didn't want to include them
I added these to reinforce some ideas already presented above but didn't want to include them
above because it's just too much. It's easy to overdo it on a subject.
You want to get hired in this century, right?
@ -1949,7 +1949,7 @@ Sit back and enjoy.
- [2006: The Chubby Lock Service for Loosely-Coupled Distributed Systems](https://research.google.com/archive/chubby-osdi06.pdf)
- [2007: Dynamo: Amazon’s Highly Available Key-value Store](http://s3.amazonaws.com/AllThingsDistributed/sosp/amazon-dynamo-sosp2007.pdf)
- The Dynamo paper kicked off the NoSQL revolution
- [2007: What Every Programmer Should Know About Memory (very long, and the author encourages skipping of some sections)](https://www.akkadia.org/drepper/cpumemory.pdf)
- [2007: What Every Programmer Should Know About Memory (very long, and the author encourages skipping some sections)](https://www.akkadia.org/drepper/cpumemory.pdf)
- 2012: AddressSanitizer: A Fast Address Sanity Checker:
- [paper](http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/37752.pdf)
- [video](https://www.usenix.org/conference/atc12/technical-sessions/presentation/serebryany)