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

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Quick Overview

Coding Interview University is a comprehensive study plan created by John Washam to prepare for software engineering interviews, particularly at large tech companies. It covers a wide range of computer science topics, data structures, algorithms, and system design concepts, providing a structured approach to interview preparation.

Pros

  • Extensive coverage of essential computer science topics and interview-related subjects
  • Well-organized with a clear study plan and progress tracking system
  • Free and open-source, accessible to anyone looking to improve their skills
  • Regularly updated with community contributions and feedback

Cons

  • Can be overwhelming due to the vast amount of information and resources
  • May require a significant time commitment to complete the entire curriculum
  • Some topics may be more in-depth than necessary for certain interview processes
  • Primarily focused on theoretical knowledge rather than practical coding experience

Note: As this is not a code library, the code example and quick start sections have been omitted.

Competitor Comparisons

yangshun logo

tech-interview-handbook

117,996

💯 Curated coding interview preparation materials for busy software engineers

Pros of Tech Interview Handbook

  • More concise and focused on practical interview preparation
  • Includes non-technical aspects like behavioral questions and resume tips
  • Regularly updated with recent interview experiences and trends

Cons of Tech Interview Handbook

  • Less comprehensive coverage of computer science fundamentals
  • Fewer in-depth explanations of algorithms and data structures
  • Limited guidance for long-term learning and career development

Code Comparison

Tech Interview Handbook provides more practical code snippets:

def binary_search(arr, target):
    left, right = 0, len(arr) - 1
    while left <= right:
        mid = (left + right) // 2
        if arr[mid] == target:
            return mid
        elif arr[mid] < target:
            left = mid + 1
        else:
            right = mid - 1
    return -1

Coding Interview University focuses on theoretical concepts:

Big O Notation:
O(1) - Constant time
O(log n) - Logarithmic time
O(n) - Linear time
O(n log n) - Linearithmic time
O(n^2) - Quadratic time

Both repositories offer valuable resources for interview preparation, but they cater to different learning styles and goals. Tech Interview Handbook is more suitable for quick, practical preparation, while Coding Interview University provides a comprehensive, long-term learning path for aspiring software engineers.

donnemartin logo

system-design-primer

272,640

Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.

Pros of system-design-primer

  • Focuses specifically on system design, providing in-depth coverage of this crucial topic
  • Includes visual aids and diagrams to illustrate complex concepts
  • Offers practical examples and case studies of real-world system designs

Cons of system-design-primer

  • Narrower scope compared to coding-interview-university, which covers a broader range of topics
  • May be more advanced for beginners, as it assumes some prior knowledge of software engineering concepts
  • Less emphasis on coding practice and algorithm implementation

Code Comparison

While both repositories primarily focus on concepts rather than code, system-design-primer does include some code snippets for illustration. For example:

# system-design-primer
def get_user(request):
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    user = User.objects.get(id=user_id)
    return JsonResponse({'user': user})

coding-interview-university, on the other hand, provides links to external resources for coding practice rather than including code snippets directly in the repository.

Both repositories serve as excellent resources for software engineers preparing for technical interviews, with system-design-primer offering a deep dive into system design concepts and coding-interview-university providing a comprehensive curriculum covering various aspects of computer science and software engineering.

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Pros of interviews

  • More concise and focused on specific interview topics
  • Includes code examples and solutions for common interview problems
  • Organized by data structures and algorithms, making it easy to find relevant information

Cons of interviews

  • Less comprehensive coverage of computer science fundamentals
  • Lacks a structured learning path or study plan
  • May not be as suitable for beginners or those seeking a broader understanding

Code Comparison

interviews:

public ListNode reverseList(ListNode head) {
    ListNode prev = null;
    while (head != null) {
        ListNode next = head.next;
        head.next = prev;
        prev = head;
        head = next;
    }
    return prev;
}

coding-interview-university:

def reverse_list(head):
    prev = None
    current = head
    while current:
        next_node = current.next
        current.next = prev
        prev = current
        current = next_node
    return prev

Both repositories provide similar implementations for reversing a linked list, but interviews uses Java while coding-interview-university uses Python. The coding-interview-university example is slightly more readable due to Python's syntax, but both achieve the same result.

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javascript-algorithms

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📝 Algorithms and data structures implemented in JavaScript with explanations and links to further readings

Pros of javascript-algorithms

  • Focused specifically on JavaScript implementations
  • Includes visual explanations and animations for algorithms
  • Provides both source code and explanations in the same repository

Cons of javascript-algorithms

  • Limited to JavaScript, not as comprehensive for general CS knowledge
  • Less structured learning path compared to coding-interview-university
  • Fewer resources for non-algorithmic topics (e.g., system design, networking)

Code Comparison

coding-interview-university (Python):

def binary_search(list, item):
    low = 0
    high = len(list) - 1
    while low <= high:
        mid = (low + high) // 2
        guess = list[mid]
        if guess == item:
            return mid
        if guess > item:
            high = mid - 1
        else:
            low = mid + 1
    return None

javascript-algorithms (JavaScript):

function binarySearch(sortedArray, seekElement) {
  let startIndex = 0;
  let endIndex = sortedArray.length - 1;
  while (startIndex <= endIndex) {
    const middleIndex = startIndex + Math.floor((endIndex - startIndex) / 2);
    if (sortedArray[middleIndex] === seekElement) {
      return middleIndex;
    }
    if (sortedArray[middleIndex] < seekElement) {
      startIndex = middleIndex + 1;
    } else {
      endIndex = middleIndex - 1;
    }
  }
  return -1;
}

Both repositories offer valuable resources for learning algorithms and data structures. coding-interview-university provides a broader, language-agnostic approach to computer science topics, while javascript-algorithms offers in-depth JavaScript-specific implementations with visual aids.

TheAlgorithms logo

Python

191,495

All Algorithms implemented in Python

Pros of The Algorithms

  • Focuses specifically on Python implementations of algorithms
  • Provides a wide range of algorithm categories (sorting, searching, machine learning, etc.)
  • Includes practical examples and explanations for each algorithm

Cons of The Algorithms

  • Limited coverage of general computer science topics and system design
  • Lacks a structured learning path or curriculum
  • May not cover all topics typically asked in coding interviews

Code Comparison

Coding Interview University:

No specific code examples provided in the main repository

The Algorithms:

def binary_search(arr, x):
    low = 0
    high = len(arr) - 1
    while low <= high:
        mid = (low + high) // 2
        if arr[mid] < x:
            low = mid + 1
        elif arr[mid] > x:
            high = mid - 1
        else:
            return mid
    return -1

Summary

Coding Interview University provides a comprehensive study plan for computer science topics and interview preparation, while The Algorithms focuses on Python implementations of various algorithms. The former offers a structured learning path, covering a broader range of topics, while the latter provides practical code examples and explanations for specific algorithms. Choose based on your learning style and goals: comprehensive CS knowledge or hands-on algorithm practice in Python.

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developer-roadmap

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Pros of Developer Roadmap

  • Provides visual roadmaps for different tech paths (frontend, backend, DevOps)
  • Covers a broader range of technologies and career paths
  • Regularly updated with modern tech trends and practices

Cons of Developer Roadmap

  • Less focused on computer science fundamentals and algorithms
  • Doesn't provide detailed study plans or resources for each topic
  • May be overwhelming for beginners due to the breadth of information

Code Comparison

While both repositories focus on learning resources rather than code, Developer Roadmap includes some markdown for generating visual roadmaps:

graph TD
    A[Computer Science] --> B(Pick a Language)
    B --> C{Web Development}
    C -->|Frontend| D[HTML/CSS/JavaScript]
    C -->|Backend| E[Python/Ruby/PHP/Java]

Coding Interview University, on the other hand, uses markdown for organizing study topics:

- [x] Data Structures
    - [x] Arrays
    - [x] Linked Lists
    - [x] Stack
    - [x] Queue
    - [x] Hash table

Both repositories use markdown extensively, but for different purposes reflecting their distinct focuses on visual career paths versus detailed study plans.

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README

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! 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!

Translations:
Translations in progress:

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

Topics of Study

Getting the Job

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

Optional Extra Topics & Resources

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.

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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

  2. Clone to your local repo:

    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

  3. 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

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Don't feel you aren't smart enough

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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.

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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

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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.

C

Python

Java

Your choice:

C++

Your choice:

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Interview Prep Books

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.

If you have tons of extra time:

Choose one:

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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.

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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

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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.

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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.

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Coding Problems

Don't forget your key coding interview books here.

Solving Problems:

Coding Interview Question Videos:

Challenge/Practice sites:

  • LeetCode
    • 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.
  • HackerRank
  • TopCoder
  • Codeforces
  • Codility
  • Geeks for Geeks
  • AlgoExpert
    • Created by Google engineers, this is also an excellent resource to hone your skills.
  • Project Euler
    • very math-focused, and not really suited for coding interviews

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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.

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Data Structures

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More Knowledge

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Trees

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Sorting

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

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Graphs

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

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Even More Knowledge

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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.

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Update Your Resume

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Interview Process & General Interview Prep

Mock Interviews:

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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]?

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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?

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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.

*****************************************************************************************************
*****************************************************************************************************

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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:
    • 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.

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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.

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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?

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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