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A complete computer science study plan to become a software engineer.

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Coding Interview University

I originally created this as a short to-do list of study topics for becoming a software engineer,

but it grew to the large list you see today. After going through this study plan, [I got hired

as a Software Development Engineer at Amazon](https://startupnextdoor.com/ive-been-acquired-by-amazon/?src=ciu)!

You probably won't have to study as much as I did. Anyway, everything you need is here.

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

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.

Best of luck to you!

<details> <summary>Translations:</summary>

  • Bahasa Indonesia
  • Bulgarian
  • Español
  • German
  • Japanese (日本語)
  • Marathi
  • Polish
  • Português Brasileiro
  • Russian
  • Tiếng Việt - Vietnamese
  • Urdu - اردو
  • Uzbek
  • বাংলা - Bangla
  • ខ្មែរ - Khmer
  • 简体中文
  • 繁體中文

</details>

<details> <summary>Translations in progress:</summary>

</details>

What is it?

Coding at the whiteboard - from HBO's Silicon Valley

This is my multi-month study plan for becoming a software engineer for a large company.

Required:

  • A little experience with coding (variables, loops, methods/functions, etc)
  • Patience
  • Time

Note this is a study plan for software engineering, not frontend engineering or full-stack development. There are really super roadmaps and coursework for those career paths elsewhere (see https://roadmap.sh/ for more info).

There is a lot to learn in a university Computer Science program, but only knowing about 75% is good enough for an interview, so that's what I cover here. For a complete CS self-taught program, the resources for my study plan have been included in Kamran Ahmed's Computer Science Roadmap: https://roadmap.sh/computer-science

---

Table of Contents

The Study Plan

  • What is it?
  • Why use it?
  • How to use it
  • Don't feel you aren't smart enough
  • A Note About Video Resources
  • Choose a Programming Language
  • Books for Data Structures and Algorithms
  • Interview Prep Books
  • Don't Make My Mistakes
  • What you Won't See Covered
  • The Daily Plan
  • Coding Question Practice
  • Coding Problems

Topics of Study

  • Algorithmic complexity / Big-O / Asymptotic analysis
  • Data Structures
  • Arrays
  • Linked Lists
  • Stack
  • Queue
  • Hash table
  • More Knowledge
  • Binary search
  • Bitwise operations
  • Trees
  • Trees - Intro
  • Binary search trees: BSTs
  • Heap / Priority Queue / Binary Heap
  • balanced search trees (general concept, not details)
  • traversals: preorder, inorder, postorder, BFS, DFS
  • Sorting
  • selection
  • insertion
  • heapsort
  • quicksort
  • mergesort
  • Graphs
  • directed
  • undirected
  • adjacency matrix
  • adjacency list
  • traversals: BFS, DFS
  • Even More Knowledge
  • Recursion
  • Dynamic Programming
  • Design Patterns
  • Combinatorics (n choose k) & Probability
  • NP, NP-Complete and Approximation Algorithms
  • How computers process a program
  • Caches
  • Processes and Threads
  • Testing
  • String searching & manipulations
  • Tries
  • Floating Point Numbers
  • Unicode
  • Endianness
  • Networking
  • Final Review

Getting the Job

  • Update Your Resume
  • Find a Job
  • Interview Process & General Interview Prep
  • Be thinking of for when the interview comes
  • Have questions for the interviewer
  • Once You've Got The Job

---------------- Everything below this point is optional ----------------

Optional Extra Topics & Resources

  • Additional Books
  • System Design, Scalability, Data Handling (if you have 4+ years experience)
  • Additional Learning
  • Compilers
  • Emacs and vi(m)
  • Unix command line tools
  • Information theory
  • Parity & Hamming Code
  • Entropy
  • Cryptography
  • Compression
  • Computer Security
  • Garbage collection
  • Parallel Programming
  • Messaging, Serialization, and Queueing Systems
  • A*
  • Fast Fourier Transform
  • Bloom Filter
  • HyperLogLog
  • Locality-Sensitive Hashing
  • van Emde Boas Trees
  • Augmented Data Structures
  • Balanced search trees
  • AVL trees
  • Splay trees
  • Red/black trees
  • 2-3 search trees
  • 2-3-4 Trees (aka 2-4 trees)
  • N-ary (K-ary, M-ary) trees
  • B-Trees
  • k-D Trees
  • Skip lists
  • Network Flows
  • Disjoint Sets & Union Find
  • Math for Fast Processing
  • Treap
  • Linear Programming
  • Geometry, Convex hull
  • Discrete math
  • Additional Detail on Some Subjects
  • Video Series
  • Computer Science Courses
  • Papers

---

Why use it?

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.

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 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 memory" error, and then I'd have to find a workaround. I used a few multidimensional arrays in my life and thousands of associative arrays, but I never created data structures from scratch.

It's a long plan. It may take you months. If you are familiar with a lot of this already it will take you a lot less time.

[⬆ back to top](#table-of-contents)

How to use it

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.

If you don't want to use git

On this page, click the Code button near the top, then click "Download ZIP". Unzip the file and you can work with the text files.

If you're open in a code editor that understands markdown, you'll see everything formatted nicely.

How to download the repo as a zip file

If you're comfortable with git

Create a new branch so you can check items like this, just put an x in the brackets: [x]

  1. *Fork the GitHub repo:* https://github.com/jwasham/coding-interview-university by clicking on the Fork button.

Fork the GitHub repo

  1. Clone to your local repo:
bash
    git clone https://github.com/<YOUR_GITHUB_USERNAME>/coding-interview-university.git
    cd coding-interview-university
    git remote add upstream https://github.com/jwasham/coding-interview-university.git
    git remote set-url --push upstream DISABLE  # so that you don't push your personal progress back to the original repo
  1. Mark all boxes with X after you completed your changes:
bash
    git commit -am "Marked personal progress"
    git pull upstream main  # keep your fork up-to-date with changes from the original repo

    git push # just pushes to your fork

[⬆ back to top](#table-of-contents)

Don't feel you aren't smart enough

[⬆ back to top](#table-of-contents)

A Note About Video Resources

Some videos are available only by enrolling in a Coursera or EdX class. These are called MOOCs. Sometimes the classes are not in session so you have to wait a couple of months, so you have no access.

It would be great to replace the online course resources with free and always-available public sources, such as YouTube videos (preferably university lectures), so that you people can study these anytime, not just when a specific online course is in session.

[⬆ back to top](#table-of-contents)

Choose a Programming Language

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.

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, 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.
  • The C Programming Language, 2nd Edition
  • This is a short book, but it will give you a great handle on the C language and if you practice it a little

you'll quickly get proficient. Understanding C helps you understand how programs and memory work.

  • You don't need to go super deep in the book (or even finish it). Just get to where you're comfortable reading and writing in C.
  • Python: Modern and very expressive, I learned it because it's just super useful and also allows me to write less code in an interview.

This is my preference. You do what you like, of course.

You may not need it, but here are some sites for learning a new language:

For your Coding Interview

You can use a language you are comfortable in to do the coding part of the interview, but for large companies, these are solid choices:

  • C++
  • Java
  • Python

You could also use these, but read around first. There may be caveats:

  • JavaScript
  • Ruby

Here is an article I wrote about choosing a language for the interview: Pick One Language for the Coding Interview. This is the original article my post was based on: Choosing a Programming Language for Interviews

You need to be very comfortable in the language and be knowledgeable.

Read more about choices:

See language-specific resources here

[⬆ back to top](#table-of-contents)

Books for Data Structures and Algorithms

This book will form your foundation for computer science.

Just choose one, in a language that you will be comfortable with. You'll be doing a lot of reading and coding.

Python

  • Coding Interview Patterns: Nail Your Next Coding Interview (Main Recommendation)
  • An insider’s perspective on what interviewers are truly looking for and why.
  • 101 real coding interview problems with detailed solutions.
  • Intuitive explanations that guide you through each problem as if you were solving it in a live interview.
  • 1000+ diagrams to illustrate key concepts and patterns.

C

Java

Your choice:

C++

Your choice:

[⬆ back to top](#table-of-contents)

Interview Prep Books

Here are some recommended books to supplement your learning.

If you have tons of extra time:

Choose one:

[⬆ back to top](#table-of-contents)

Don't Make My Mistakes

This list grew over many months, and yes, it got out of hand.

Here are some mistakes I made so you'll have a better experience. And you'll save months of time.

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.

Please, read so you won't make my mistakes:

Retaining Computer Science Knowledge.

2. Use Flashcards

To solve the problem, I made a little flashcard site where I could add flashcards of 2 types: general and code. Each card has a different formatting. I made a mobile-first website, so I could review on my phone or tablet, wherever I am.

Make your own for free:

I DON'T RECOMMEND using my flashcards. There are too many and most of them are trivia that you don't need.

But if you don't want to listen to me, here you go:

Keep in mind I went overboard and have cards covering everything from assembly language and Python trivia to machine learning and statistics. It's way too much for what's required.

Note on flashcards: The first time you recognize you know the answer, don't mark it as known. You have to see the same card and answer it several times correctly before you really know it. Repetition will put that knowledge deeper in your brain.

An alternative to using my flashcard site is Anki, 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.

My flashcard database in Anki format: https://ankiweb.net/shared/info/25173560 (thanks @xiewenya).

Some students have mentioned formatting issues with white space that can be fixed by doing the following: open the deck, edit the 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

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 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 (or coding problem websites, listed below)
  2. Do 2 or 3 questions regarding linked lists.
  3. Move on to the next learning topic.
  4. Later, go back and do another 2 or 3 linked list problems.
  5. Do this with each new topic you learn.

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.

There are many resources for this, listed below. Keep going.

4. Focus

There are a lot of distractions that can take up valuable time. Focus and concentration are hard. Turn on some music without lyrics and you'll be able to focus pretty well.

[⬆ back to top](#table-of-contents)

What you won't see covered

These are prevalent technologies but not part of this study plan:

  • Javascript
  • HTML, CSS, and other front-end technologies
  • SQL

[⬆ back to top](#table-of-contents)

The Daily 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.

You can see my code here:

You don't need to memorize every algorithm. You just need to be able to understand it enough to be able to write your own implementation.

[⬆ back to top](#table-of-contents)

Coding Question Practice

Why is this here? I'm not ready to interview.

Then go back and read this.

Why you need to practice doing programming problems:

  • Problem recognition, and where the right data structures and algorithms fit in
  • Gathering requirements for the problem
  • Talking your way through the problem like you will in the interview
  • Coding on a whiteboard or paper, not a computer
  • 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 interview books, too, but I found this outstanding: Algorithm design canvas

Write code on a whiteboard or paper, not a computer. Test with some sample inputs. Then type it and test it out on a computer.

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 quickly. I use a pencil and eraser.

my sofa whiteboard

Coding question practice is not about memorizing answers to programming problems.

[⬆ back to top](#table-of-contents)

Coding Problems

Don't forget your key coding interview books here.

Solving Problems:

Coding Interview Question Videos:

Challenge/Practice sites:

[⬆ back to top](#table-of-contents)

Let's Get Started

Alright, enough talk, let's learn!

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

Algorithmic complexity / Big-O / Asymptotic analysis

Well, that's about enough of that.

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

[⬆ back to top](#table-of-contents)

Data Structures

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

(for when you pass a pointer to a function that may change the address where that pointer points) This page is just to get a grasp on ptr to ptr. I don't recommend this list traversal style. Readability and maintainability suffer due to cleverness.

  • Pointers to Pointers
  • [ ] Implement (I did with tail pointer & without):
  • [ ] size() - returns the number of data elements in the list
  • [ ] empty() - bool returns true if empty
  • [ ] value_at(index) - returns the value of the nth item (starting at 0 for first)
  • [ ] push_front(value) - adds an item to the front of the list
  • [ ] pop_front() - remove the front item and return its value
  • [ ] push_back(value) - adds an item at the end
  • [ ] pop_back() - removes end item and returns its value
  • [ ] front() - get the value of the front item
  • [ ] back() - get the value of the end item
  • [ ] insert(index, value) - insert value at index, so the current item at that index is pointed to by the new item at the index
  • [ ] erase(index) - removes node at given index
  • [ ] value_n_from_end(n) - returns the value of the node at the nth position from the end of the list
  • [ ] reverse() - reverses the list
  • [ ] remove_value(value) - removes the first item in the list with this value
  • [ ] Doubly-linked List
  • Description (video)
  • No need to implement
  • ### Stack
  • [ ] Stacks (video)
  • [ ] [[Review] Stacks in 3 minutes (video)](https://youtu.be/KcT3aVgrrpU)
  • [ ] Will not implement. Implementing with the array is trivial
  • ### Queue
  • [ ] Queue (video)
  • [ ] Circular buffer/FIFO
  • [ ] [[Review] Queues in 3 minutes (video)](https://youtu.be/D6gu-_tmEpQ)
  • [ ] Implement using linked-list, with tail pointer:
  • enqueue(value) - adds value at a position at the tail
  • dequeue() - returns value and removes least recently added element (front)
  • empty()
  • [ ] Implement using a fixed-sized array:
  • enqueue(value) - adds item at end of available storage
  • dequeue() - returns value and removes least recently added element
  • empty()
  • full()
  • [ ] Cost:
  • a bad implementation using 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)
  • [ ] Implement with array using linear probing
  • hash(k, m) - m is the size of the hash table
  • add(key, value) - if the key already exists, update value
  • exists(key)
  • get(key)
  • remove(key)

[⬆ back to top](#table-of-contents)

More Knowledge

  • ### Bitwise operations
  • [ ] Bits cheat sheet
  • you should know many of the powers of 2 from (2^1 to 2^16 and 2^32)
  • [ ] Get a really good understanding of manipulating bits with: &, |, ^, ~, >>, <<
  • [ ] words)
  • [ ] Good intro:

Bit Manipulation (video)

[⬆ back to top](#table-of-contents)

Trees

best: O(log n) - avg. height of tree worst: O(n)

  • inorder (DFS: left, self, right)
  • postorder (DFS: left, right, self)
  • preorder (DFS: self, left, right)
  • [ ] [[Review] Breadth-first search in 4 minutes (video)](https://youtu.be/HZ5YTanv5QE)
  • [ ] [[Review] Depth-first search in 4 minutes (video)](https://youtu.be/Urx87-NMm6c)
  • [ ] [[Review] Tree Traversal (playlist) in 11 minutes (video)](https://www.youtube.com/playlist?list=PL9xmBV_5YoZO1JC2RgEi04nLy6D-rKk6b)

[⬆ back to top](#table-of-contents)

Sorting

  • For heapsort, see the Heap data structure above. Heap sort is great, but not stable
  • [ ] Implement:
  • [ ] Mergesort: O(n log n) average and worst case
  • [ ] Quicksort O(n log n) average case
  • Selection sort and insertion sort are both O(n^2) average and worst-case
  • For heapsort, see Heap data structure above

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

[⬆ back to top](#table-of-contents)

Graphs

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

  • Notes:
  • There are 4 basic ways to represent a graph in memory:
  • objects and pointers
  • adjacency matrix
  • adjacency list
  • adjacency map
  • Familiarize yourself with each representation and its pros & cons
  • BFS and DFS - know their computational complexity, their trade-offs, and how to implement them in real code
  • When asked a question, look for a graph-based solution first, then move on if none
  • [ ] Graphs (review and more):
  • I'll implement:
  • [ ] DFS with adjacency list (recursive)
  • [ ] DFS with adjacency list (iterative with stack)
  • [ ] DFS with adjacency matrix (recursive)
  • [ ] DFS with adjacency matrix (iterative with stack)
  • [ ] BFS with adjacency list
  • [ ] BFS with adjacency matrix
  • [ ] single-source shortest path (Dijkstra)
  • [ ] minimum spanning tree
  • DFS-based algorithms (see Aduni videos above):
  • [ ] check for a 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
  • [ ] check for bipartite graph

[⬆ back to top](#table-of-contents)

Even More Knowledge

Backtracking Blueprint: Java) Python

  • ### Dynamic Programming
  • You probably won't see any dynamic programming problems in your interview, but it's worth being able to recognize a

problem as being a candidate for dynamic programming.

Dynamic Programming (video)

  • ### NP, NP-Complete and Approximation Algorithms
  • Know about the most famous classes of NP-complete problems, such as the traveling salesman and the knapsack problem,

and be able to recognize them when an interviewer asks you them in disguise.

  • ### How computers process a program

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

  • ### Tries
  • Note there are different kinds of tries. Some have prefixes, some don't, and some use strings instead of bits

to track the path

---

[⬆ back to top](#table-of-contents)

Final Review

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

---

[⬆ back to top](#table-of-contents)

Update Your Resume

[⬆ back to top](#table-of-contents)

Interview Process & General Interview Prep

Mock Interviews:

[⬆ back to top](#table-of-contents)

Be thinking of for when the interview comes

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

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

[⬆ back to top](#table-of-contents)

Have questions for the interviewer

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

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

[⬆ back to top](#table-of-contents)

Once You've Got The Job

Congratulations!

Keep learning.

You're never really done.

---

Everything below this point is optional. It is NOT needed for an entry-level interview. However, by studying these, you'll get greater exposure to more CS concepts and will be better prepared for any software engineering job. You'll be a much more well-rounded software engineer.

---

[⬆ back to top](#table-of-contents)

Additional Books

These are here so you can dive into a topic you find interesting.

  • The Unix Programming Environment
  • An oldie but a goodie
  • The Linux Command Line: A Complete Introduction
  • A modern option
  • TCP/IP Illustrated Series
  • Head First Design Patterns
  • A gentle introduction to design patterns
  • Design Patterns: Elements of Reusable Object-Oriented Software
  • AKA the "Gang Of Four" book or GOF
  • The canonical design patterns book
  • Algorithm Design Manual (Skiena)
  • As a review and problem-recognition
  • The algorithm catalog portion is well beyond the scope of difficulty you'll get in an interview
  • This book has 2 parts:
  • Class textbook on data structures and algorithms
  • Pros:
  • Is a good review as any algorithms textbook would be
  • Nice stories from his experiences solving problems in industry and academia
  • 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, 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.
  • This book is better as an algorithm reference, and not something you read cover to cover.
  • Can rent it on Kindle
  • Answers:
  • Solutions)
  • Errata
  • Algorithm (Jeff Erickson)
  • Write Great Code: Volume 1: Understanding the Machine
  • The book was published in 2004, and is somewhat outdated, but it's a terrific resource for understanding a computer in brief
  • The author invented HLA, so take mentions and examples in HLA with a grain of salt. Not widely used, but decent examples of what assembly looks like
  • These chapters are worth the read to give you a nice foundation:
  • Chapter 2 - Numeric Representation
  • Chapter 3 - Binary Arithmetic and Bit Operations
  • Chapter 4 - Floating-Point Representation
  • Chapter 5 - Character Representation
  • Chapter 6 - Memory Organization and Access
  • Chapter 7 - Composite Data Types and Memory Objects
  • Chapter 9 - CPU Architecture
  • Chapter 10 - Instruction Set Architecture
  • Chapter 11 - Memory Architecture and Organization
  • Introduction to Algorithms
  • 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
  • For a richer, more up-to-date (2017), but longer treatment

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System Design, Scalability, Data Handling

You can expect system design questions if you have 4+ years of experience.

  • Scalability and System Design are very large topics with many topics and resources, since

there is a lot to consider when designing a software/hardware system that can scale. Expect to spend quite a bit of time on this

  1. Understand the problem and scope:
  • Define the use cases, with the interviewer's help
  • Suggest additional features
  • Remove items that the interviewer deems out of scope
  • Assume high availability is required, add as a use case
  1. 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 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
  1. Abstract design:

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Additional Learning

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.

  • ### Locality-Sensitive Hashing
  • Used to determine the similarity of documents
  • The opposite of MD5 or SHA which are used to determine if 2 documents/strings are exactly the same
  • Simhashing (hopefully) made simple
  • ### Balanced search trees
  • Know at least one type of balanced binary tree (and know how it's implemented):
  • "Among balanced search trees, AVL and 2/3 trees are now passé and red-black trees seem to be more popular.

A particularly interesting self-organizing data structure is the splay tree, which uses rotations to move any accessed key to the root." - Skiena

  • Of these, I chose to implement a splay tree. From what I've read, you won't implement a

balanced search tree in your interview. But I wanted exposure to coding one up and let's face it, splay trees are the bee's knees. I did read a lot of red-black tree code

  • Splay tree: insert, search, delete functions

If you end up implementing a red/black tree try just these:

  • Search and insertion functions, skipping delete
  • I want to learn more about B-Tree since it's used so widely with very large data sets
  • Self-balancing binary search tree
  • AVL trees
  • In practice:

From what I can tell, these aren't used much in practice, but I could see where they would be: The AVL tree is another structure supporting O(log n) search, insertion, and removal. It is more rigidly 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)

  • Splay trees
  • In practice:

Splay trees are typically used in the implementation of caches, memory allocators, routers, garbage collectors, data compression, ropes (replacement of string used for long text strings), in Windows NT (in the virtual memory, networking and file system code) etc

  • Red/black trees
  • These are a translation of a 2-3 tree (see below).
  • In practice:

Red–black trees offer worst-case guarantees for insertion time, deletion time, and search time. 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 that 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 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

  • 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).

  • 2-3-4 Trees (aka 2-4 trees)
  • In practice:

For every 2-4 trees, there are corresponding red–black trees with data elements in the same order. The insertion and deletion operations on 2-4 trees are also equivalent to color-flipping and rotations in red–black trees. This makes 2-4 trees an 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.

  • N-ary (K-ary, M-ary) trees
  • note: the N or K is the branching factor (max branches)
  • binary trees are a 2-ary tree, with branching factor = 2
  • 2-3 trees are 3-ary
  • K-Ary Tree
  • B-Trees
  • Fun fact: it's a mystery, but the B could stand for Boeing, Balanced, or Bayer (co-inventor).
  • In Practice:

B-trees are widely used in databases. Most modern filesystems use B-trees (or Variants). In addition to its use in databases, the B-tree is also used in filesystems to allow quick random access to an arbitrary block in a particular file. The basic problem is turning the file block address into a disk block (or perhaps to a cylinder head sector) address

  • ### k-D Trees
  • Great for finding a number of points in a rectangle or higher-dimensional object
  • A good fit for k-nearest neighbors
  • kNN K-d tree algorithm (video)

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Additional Detail on Some Subjects

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?

  • Sorting

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Video Series

Sit back and enjoy.

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Computer Science Courses

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Algorithms implementation

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Papers

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LICENSE

CC-BY-SA-4.0