Created at: 2016-06-06 10:34:12
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!
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 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!
Translations in progress:
This is my multi-month study plan for becoming a software engineer for a large company.
- A little experience with coding (variables, loops, methods/functions, etc)
Note this is a study plan for software engineering, not frontend engineering or fullstack 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
---------------- Everything below this point is optional ----------------
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.
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.
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.
Create a new branch so you can check items like this, just put an x in the brackets: [x]
Fork the GitHub repo:
https://github.com/jwasham/coding-interview-university by clicking on the Fork button.
Clone to your local repo:
git clone https://github.com/<YOUR_GITHUB_USERNAME>/coding-interview-university.git
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
Mark all boxes with X after you completed your changes:
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
- Successful software engineers are smart, but many have an insecurity that they aren't smart enough.
- Following videos may help you overcome this insecurity:
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.
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.
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.
- Answers to questions in the book
- 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:
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:
You could also use these, but read around first. There may be caveats:
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
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.
- Goodrich, Tamassia, Goldwasser
- Sedgewick and Wayne:
- Free Coursera course that covers the book (taught by the authors!):
- Goodrich, Tamassia, and Mount
- Sedgewick and Wayne
You don't need to buy a bunch of these. Honestly "Cracking the Coding Interview" is probably enough,
but I bought more to give myself more practice. But I always do too much.
I bought both of these. They gave me plenty of practice.
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.
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.
To solve the problem, I made a little flashcards site where I could add flashcards of 2 types: general and code.
Each card has 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
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 deck, edit card, click cards, select the "styling" radio button, add the member "white-space: pre;" to the card class.
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.
Once you've learned a topic, and feel somewhat comfortable with it, for example, linked lists:
- Open one of the coding interview books (or coding problem websites, listed below)
- Do 2 or 3 questions regarding linked lists.
- Move on to the next learning topic.
- Later, go back and do another 2 or 3 linked list problems.
- 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.
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.
These are prevalent technologies but not part of this study plan:
- HTML, CSS, and other front-end technologies
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.
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 quick. I use a pencil and eraser.
Coding question practice is not about memorizing answers to programming problems.
Don't forget your key coding interview books here.
Coding Interview Question Videos:
- My favorite coding problem site. It's worth the subscription money for the 1-2 months you'll likely be preparing.
- See Nick White and FisherCoder Videos above for code walk-throughs.
- Geeks for Geeks
- Created by Google engineers, this is also an excellent resource to hone your skills.
- very math focused, and not really suited for coding interviews
Alright, enough talk, let's learn!
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
if you can identify the runtime complexity of different algorithms. It's a super review and test.
- [ ] About Arrays:
- [ ] Implement a vector (mutable array with automatic resizing):
- [ ] Practice coding using arrays and pointers, and pointer math to jump to an index instead of using indexing.
- [ ] New raw data array with allocated memory
- can allocate int array under the hood, just not use its features
- start with 16, or if starting number is greater, use power of 2 - 16, 32, 64, 128
- [ ] size() - number of items
- [ ] capacity() - number of items it can hold
- [ ] is_empty()
- [ ] at(index) - returns item at given index, blows up if index out of bounds
- [ ] push(item)
- [ ] insert(index, item) - inserts item at index, shifts that index's value and trailing elements to the right
- [ ] prepend(item) - can use insert above at index 0
- [ ] pop() - remove from end, return value
- [ ] delete(index) - delete item at index, shifting all trailing elements left
- [ ] remove(item) - looks for value and removes index holding it (even if in multiple places)
- [ ] find(item) - looks for value and returns first index with that value, -1 if not found
- [ ] resize(new_capacity) // private function
- when you reach capacity, resize to double the size
- when popping an item, if size is 1/4 of capacity, resize to half
- [ ] Time
- O(1) to add/remove at end (amortized for allocations for more space), index, or update
- O(n) to insert/remove elsewhere
- [ ] Space
- contiguous in memory, so proximity helps performance
- space needed = (array capacity, which is >= n) * size of item, but even if 2n, still O(n)
- [ ] Description:
- [ ] C Code (video)
- not the whole video, just portions about Node struct and memory allocation
- [ ] Linked List vs Arrays:
- [ ] Why you should avoid linked lists (video)
- [ ] Gotcha: you need pointer to pointer knowledge:
(for when you pass a pointer to a function that may change the address where that pointer points)
This page is just to get a grasp on ptr to ptr. I don't recommend this list traversal style. Readability and maintainability suffer due to cleverness.
- [ ] Implement (I did with tail pointer & without):
- [ ] size() - returns number of data elements in list
- [ ] empty() - bool returns true if empty
- [ ] value_at(index) - returns the value of the nth item (starting at 0 for first)
- [ ] push_front(value) - adds an item to the front of the list
- [ ] pop_front() - remove front item and return its value
- [ ] push_back(value) - adds an item at the end
- [ ] pop_back() - removes end item and returns its value
- [ ] front() - get value of front item
- [ ] back() - get value of end item
- [ ] insert(index, value) - insert value at index, so current item at that index is pointed to by new item at index
- [ ] erase(index) - removes node at given index
- [ ] value_n_from_end(n) - returns the value of the node at nth position from the end of the list
- [ ] reverse() - reverses the list
- [ ] remove_value(value) - removes the first item in the list with this value
- [ ] Doubly-linked List
- [ ] Queue (video)
- [ ] Circular buffer/FIFO
- [ ] [Review] Queues in 3 minutes (video)
- [ ] Implement using linked-list, with tail pointer:
- enqueue(value) - adds value at position at tail
- dequeue() - returns value and removes least recently added element (front)
- [ ] Implement using fixed-sized array:
- enqueue(value) - adds item at end of available storage
- dequeue() - returns value and removes least recently added element
- [ ] Cost:
- a bad implementation using linked list where you enqueue at head and dequeue at tail would be O(n)
because you'd need the next to last element, causing a full traversal each dequeue
- enqueue: O(1) (amortized, linked list and array [probing])
- dequeue: O(1) (linked list and array)
- empty: O(1) (linked list and array)
- [ ] Bits cheat sheet - you should know many of the powers of 2 from (2^1 to 2^16 and 2^32)
- [ ] Get a really good understanding of manipulating bits with: &, |, ^, ~, >>, <<
- [ ] 2s and 1s complement
- [ ] Count set bits
- [ ] Swap values:
- [ ] Absolute value:
As a summary, here is a visual representation of 15 sorting algorithms.
If you need more detail on this subject, see "Sorting" section in Additional Detail on Some Subjects
Graphs can be used to represent many problems in computer science, so this section is long, like trees and sorting were.
Backtracking Blueprint: Java
- You probably won't see any dynamic programming problems in your interview, but it's worth being able to recognize a
problem as being a candidate for dynamic programming.
- This subject can be pretty difficult, as each DP soluble problem must be defined as a recursion relation, and coming up with it can be tricky.
- I suggest looking at many examples of DP problems until you have a solid understanding of the pattern involved.
- [ ] Videos:
- [ ] Yale Lecture notes:
- [ ] Coursera:
- [ ] LRU cache:
- [ ] CPU cache:
- [ ] Computer Science 162 - Operating Systems (25 videos):
- What Is The Difference Between A Process And A Thread?
- Processes, Threads, Concurrency issues
- Difference between processes and threads
- How they work?
- CPU activity, interrupts, context switching
- Modern concurrency constructs with multicore processors
- Paging, segmentation and virtual memory (video)
- Interrupts (video)
- Process resource needs (memory: code, static storage, stack, heap, and also file descriptors, i/o)
- Thread resource needs (shares above (minus stack) with other threads in the same process but each has its own pc, stack counter, registers, and stack)
- Forking is really copy on write (read-only) until the new process writes to memory, then it does a full copy.
- Context switching
- [ ] threads in C++ (series - 10 videos)
- [ ] CS 377 Spring '14: Operating Systems from University of Massachusetts
- [ ] concurrency in Python (videos):
If you need more detail on this subject, see "String Matching" section in Additional Detail on Some Subjects.
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.
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]?
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?
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.
These are here so you can dive into a topic you find interesting.
The Unix Programming Environment
The Linux Command Line: A Complete Introduction
- 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
- 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
- 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
- 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
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
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
- Distill large data sets to single values
- Transform one data set to another
- Handling obscenely large amounts of data
- System design
- features sets
- class hierarchies
- designing a system under certain constraints
- simplicity and robustness
- performance analysis and optimization
- [ ] START HERE: The System Design Primer
- [ ] System Design from HiredInTech
- [ ] How Do I Prepare To Answer Design Questions In A Technical Interview?
- [ ] 8 steps guide to ace your system design interview
- [ ] Database Normalization - 1NF, 2NF, 3NF and 4NF (video)
- [ ] System Design Interview - There are a lot of resources in this one. Look through the articles and examples. I put some of them below
- [ ] How to ace a systems design interview
- [ ] Numbers Everyone Should Know
- [ ] How long does it take to make a context switch?
- [ ] Transactions Across Datacenters (video)
- [ ] A plain English introduction to CAP Theorem
- [ ] MIT 6.824: Distributed Systems, Spring 2020 (20 videos)
- [ ] Consensus Algorithms:
- [ ] Consistent Hashing
- [ ] NoSQL Patterns
- [ ] Scalability:
- [ ] Practicing the system design process: Here are some ideas to try working through on paper, each with some documentation on how it was handled in the real world:
- review: The System Design Primer
- System Design from HiredInTech
- cheat sheet
- Understand the problem and scope:
- Define the use cases, with interviewer's help
- Suggest additional features
- Remove items that interviewer deems out of scope
- Assume high availability is required, add as a use case
- 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
- Total storage required over 5 years
- How much data read per second
- Abstract design:
- Layers (service, data, caching)
- Infrastructure: load balancing, messaging
- Rough overview of any key algorithm that drives the service
- Consider bottlenecks and determine solutions
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.
- I filled in the list below from good tools.
- curl or wget
- Khan Academy
- More about Markov processes:
- See more in MIT 6.050J Information and Entropy series below
- 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
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 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
- 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
- CS 61B: Splay Trees (video)
- MIT Lecture: Splay Trees:
- Gets very mathy, but watch the last 10 minutes for sure.
2-3 search trees
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.
- CS 61B Lecture 26: Balanced Search Trees (video)
- Bottom Up 234-Trees (video)
- Top Down 234-Trees (video)
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
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?
More Dynamic Programming (videos)
Advanced Graph Processing (videos)
MIT Probability (mathy, and go slowly, which is good for mathy things) (videos):
Simonson: Approximation Algorithms (video)
- Rabin-Karp (videos):
- Knuth-Morris-Pratt (KMP):
- Boyer–Moore string search algorithm
Coursera: Algorithms on Strings
- starts off great, but by the time it gets past KMP it gets more complicated than it needs to be
- nice explanation of tries
- can be skipped
- Stanford lectures on sorting:
- Shai Simonson:
- Steven Skiena lectures on sorting:
NAND To Tetris: Build a Modern Computer from First Principles
Sit back and enjoy.