/coding-interview-university

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

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Coding Interview University

Few more like this

Table of Contents

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

Don't feel you aren't smart enough

About Video Resources

Some videos are available only by enrolling in a Coursera, EdX, or Lynda.com 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. Lynda.com courses are not free.

I'd appreciate your help to add free and always-available public sources, such as YouTube videos to accompany the online course videos.
I like using university lectures.

Interview Process & General Interview Prep

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Pick One Language for the Interview

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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

Read more about choices:

See language resources here

You'll see some C, C++, and Python learning included below, because I'm learning. There are a few books involved, see the bottom.

Book List

This is a shorter list than what I used. This is abbreviated to save you time.

Interview Prep

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If you have tons of extra time:

Computer Architecture

If short on time: Click to expand!
  • 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    • The book was published in 2004, and is somewhat outdated, but it's a terrific resource for understanding a computer in brief.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
If you have more time: Click to expand!

Language Specific

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You need to choose a language for the interview (see above). Here are my recommendations by language. I don't have resources for all languages. I welcome additions.

If you read though one of these, you should have all the data structures and algorithms knowledge you'll need to start doing coding problems. You can skip all the video lectures in this project, unless you'd like a review.

Additional language-specific resources here.

C++

I haven't read these two, but they are highly rated and written by Sedgewick. He's awesome.

If you have a better recommendation for C++, please let me know. Looking for a comprehensive resource.

Java

OR:

  • Data Structures and Algorithms in Java
    • by Goodrich, Tamassia, Goldwasser
    • used as optional text for CS intro course at UC Berkeley
    • see my book report on the Python version below. This book covers the same topics.

Python

Optional Books

Some people recommend these, but I think it's going overboard, unless you have many years of software engineering experience and expect a much harder interview:

  • 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, 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.
        • about to get to this part. Will update here once I've made my way through it.
    • Can rent it on kindle
    • Answers:
    • Errata

  • 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

  • Programming Pearls

    • The first couple of chapters present clever solutions to programming problems (some very old using data tape) but that is just an intro. This a guidebook on program design and architecture, much like Code Complete, but much shorter.

  • "Algorithms and Programming: Problems and Solutions" by Shen

    • A fine book, but after working through problems on several pages I got frustrated with the Pascal, do while loops, 1-indexed arrays, and unclear post-condition satisfaction results.
    • Would rather spend time on coding problems from another book or online coding problems.

Before you Get Started

Click to expand!

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

Here are some mistakes I made so you'll have a better experience.

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.

Read please so you won't make my mistakes:

Retaining Computer Science Knowledge

2. Use Flashcards

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 and tablet, wherever I am.

Make your own for free:

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)

3. Review, review, review

I keep a set of cheat sheets on ASCII, OSI stack, Big-O notations, and more. I study them when I have some spare time.

Take a break from programming problems for a half hour and go through your flashcards.

4. Focus

There are a lot of distractions that can take up valuable time. Focus and concentration are hard.

The Daily Plan

Some subjects take one day, and some will take multiple days. Some are just learning with nothing to implement.

Each day I take one subject from the list below, watch videos about that subject, and write an implementation in:

  • C - using structs and functions that take a struct * and something else as args.
  • C++ - without using built-in types
  • C++ - using built-in types, like STL's std::list for a linked list
  • Python - using built-in types (to keep practicing Python)
  • and write tests to ensure I'm doing it right, sometimes just using simple assert() statements
  • You may do Java or something else, this is just my thing.

You don't need all these. You need only one language for the interview.

Why code in all of these?

  • Practice, practice, practice, until I'm sick of it, and can do it with no problem (some have many edge cases and bookkeeping details to remember)
  • Work within the raw constraints (allocating/freeing memory without help of garbage collection (except Python))
  • Make use of built-in types so I have experience using the built-in tools for real-world use (not going to write my own linked list implementation in production)

I may not have time to do all of these for every subject, but I'll try.

You can see my code here:

You don't need to memorize the guts of every algorithm.

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

Prerequisite Knowledge

Algorithmic complexity / Big-O / Asymptotic analysis

Data Structures

Click to expand!

  • Arrays

Arrays: Click to expand! 
- Implement an automatically resizing vector.
- [x] Description:
    - [Arrays (video)](https://www.coursera.org/learn/data-structures/lecture/OsBSF/arrays)
    - [UC Berkeley CS61B - Linear and Multi-Dim Arrays (video)](https://archive.org/details/ucberkeley_webcast_Wp8oiO_CZZE) (Start watching from 15m 32s)
    - ~[Basic Arrays (video)](https://www.lynda.com/Developer-Programming-Foundations-tutorials/Basic-arrays/149042/177104-4.html)~
    - ~[Multi-dim (video)](https://www.lynda.com/Developer-Programming-Foundations-tutorials/Multidimensional-arrays/149042/177105-4.html)~
    - [Dynamic Arrays (video)](https://www.coursera.org/learn/data-structures/lecture/EwbnV/dynamic-arrays)
    - [Jagged Arrays (video)](https://www.youtube.com/watch?v=1jtrQqYpt7g)
    - ~[Jagged Arrays (video)](https://www.lynda.com/Developer-Programming-Foundations-tutorials/Jagged-arrays/149042/177106-4.html)~
    - ~[Resizing arrays (video)](https://www.lynda.com/Developer-Programming-Foundations-tutorials/Resizable-arrays/149042/177108-4.html)~
- [ ] 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.
    - [x] 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
    - [x] size() - number of items
    - [x] 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)

More Knowledge

Trees

Sorting

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

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

You'll get more graph practice in Skiena's book (see Books section below) and the interview books

Even More Knowledge

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System Design, Scalability, Data Handling

  • You can expect system design questions if you have 4+ years of experience.
Click to expand!

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.
Click to expand!

Coding Question Practice

Now that you know all the computer science topics above, it's time to practice answering coding problems.

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

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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
  • 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

No whiteboard at home? That makes sense. I'm a weirdo and have a big whiteboard. Instead of a whiteboard, 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 for scale. If you use a pen, you'll wish you could erase. Gets messy quick.

my sofa whiteboard

Supplemental:

Read and Do Programming Problems (in this order):

See Book List above

Coding exercises/challenges

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Once you've learned your brains out, put those brains to work. Take coding challenges every day, as many as you can.

Coding Interview Question Videos:

Challenge sites:

Challenge repos:

Mock Interviews:

Interview Handling stuff. Click to expand!

Once you're closer to the interview

Your Resume

  • See Resume prep items in Cracking The Coding Interview and back of Programming Interviews Exposed

Be thinking of for when the interview comes

Think of about 20 interview questions you'll get, along with the lines of the items below. Have 2-3 answers for each. 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]?

Have questions for the interviewer

Some of mine (I already may know answer to 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?

Once You've Got The Job

Congratulations!

Keep learning.

You're never really done.

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Everything below this point is optional.
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

Additional Learning

These topics will likely not come up in an interview, but 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.

Click to expand! 
- [ ] Why ML?
    - [ ] [How Google Is Remaking Itself As A Machine Learning First Company](https://backchannel.com/how-google-is-remaking-itself-as-a-machine-learning-first-company-ada63defcb70)
    - [ ] [Large-Scale Deep Learning for Intelligent Computer Systems (video)](https://www.youtube.com/watch?v=QSaZGT4-6EY)
    - [ ] [Deep Learning and Understandability versus Software Engineering and Verification by Peter Norvig](https://www.youtube.com/watch?v=X769cyzBNVw)
- [ ] [Google's Cloud Machine learning tools (video)](https://www.youtube.com/watch?v=Ja2hxBAwG_0)
- [ ] [Google Developers' Machine Learning Recipes (Scikit Learn & Tensorflow) (video)](https://www.youtube.com/playlist?list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal)
- [ ] [Tensorflow (video)](https://www.youtube.com/watch?v=oZikw5k_2FM)
- [ ] [Tensorflow Tutorials](https://www.tensorflow.org/versions/r0.11/tutorials/index.html)
- [ ] [Practical Guide to implementing Neural Networks in Python (using Theano)](http://www.analyticsvidhya.com/blog/2016/04/neural-networks-python-theano/)
- Courses:
    - [Great starter course: Machine Learning](https://www.coursera.org/learn/machine-learning)
          - [videos only](https://www.youtube.com/playlist?list=PLZ9qNFMHZ-A4rycgrgOYma6zxF4BZGGPW)
          - see videos 12-18 for a review of linear algebra (14 and 15 are duplicates)
    - [Neural Networks for Machine Learning](https://www.coursera.org/learn/neural-networks)
    - [Google's Deep Learning Nanodegree](https://www.udacity.com/course/deep-learning--ud730)
    - [Google/Kaggle Machine Learning Engineer Nanodegree](https://www.udacity.com/course/machine-learning-engineer-nanodegree-by-google--nd009)
    - [Self-Driving Car Engineer Nanodegree](https://www.udacity.com/drive)
    - [Metis Online Course ($99 for 2 months)](http://www.thisismetis.com/explore-data-science)
- Resources:
    - Books:
        - [Python Machine Learning](https://www.amazon.com/Python-Machine-Learning-Sebastian-Raschka/dp/1783555130/)
        - [Data Science from Scratch: First Principles with Python](https://www.amazon.com/Data-Science-Scratch-Principles-Python/dp/149190142X)
        - [Introduction to Machine Learning with Python](https://www.amazon.com/Introduction-Machine-Learning-Python-Scientists/dp/1449369413/)
    - [Machine Learning for Software Engineers](https://github.com/ZuzooVn/machine-learning-for-software-engineers)
    - Data School: http://www.dataschool.io/

--

Additional Detail on Some Subjects

Click to expand! 
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?

Video Series

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Sit back and enjoy. "Netflix and skill" :P

Computer Science Courses