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README-ru.md

Google Interview University

Оригинальная версия: Английский

Для кого это?

Это мой учебный план рассчитанный на несколько месяцев для веб-разработчиков, не имеющих образования в Computer Science (CS) и планирующих работать инженерами-программистами (software engineer) в компании Google.

Кодирование на доске - из телесериала канала HBO Кремниевая Долина

За основу учебного плана я взял список вопросов Google's coaching notes и значительно расширил его. Тут вы найдёте много полезных вещей, которые необходимо знать. Дополнительные вопросы я добавил в конец списка, их могут задавать на интервью, так же они могут быть полезны в решении повседневных задач. Некоторые пункты я взял из поста Стива Йеги (Steve Yegge) "Получить работу в Google", а некоторые слово в слово соответствуют вопросам, разбираемых Google в их постах о подготовке.

Я сократил тот объем знаний, который необходим, по сравнению с рекомендациями Йеги. Я изменил требования Йеги исходя из той информации, которую мне предоставил мой знакомый из Google. Это важно для тех, кто сейчас еще новички в разработке программного обеспечения или являются веб-разработчиками и планируют стать инженерами-программистами (это та профессия где требуются знания в области CS). Если вы опытный разработчик, ожидайте что собеседование будет сложным. Подробнее.

Если вы обладаете многолетним опытом разработки ПО, помните, что Google разделяет понятия инженер-программист и разработчик ПО/веб-разработчик. Первое требует знаний в области CS.

Если вы хотите быть инженерами обеспечивающими надежность ПО или системными инженерами, то уделить внимание вопросом из опционального списка (разделы сеть, безопасность).

Содержание

---------------- Все что ниже - опционально ----------------

Чем это полезно?

Я следую этому плану, готовясь к собеседованию в Google. Я разрабатываю веб-приложения, сервисы и запускаю стартапы с 1997 года. У меня есть степень по экономике, но нет по CS. До сих пор у меня очень успешная карьера, но я хочу работать в Google. Я хочу работать с большими системами и понять принципы их работы, изучить эффективность алгоритмов и различные структуры данных, узнать, как работают низкоуровневые языки программирования. Если ты не знаешь что-то из перечисленного, Google не возьмёт тебя на работу.

Когда я начал этот проект, я ничего не знал о стеке, куче, Big-O, деревья и способах обхода графа. Если бы мне нужно было писать код для сортировки, это было бы не очень хорошо. Структуры данных, которые я использовал, были частью языка, и я не знал, как они на самом деле работали. Мне никогда не приходилось управлять памятью, если процесс, который я запускал, сообщал об ошибке "out of memory", я искал способ как ее обойти. Я использовал в своей работе несколько многомерных массивов и тысячи ассоциативных, но никогда не создавал структуру данных "с нуля".

Но после выполнения этого учебного плана я поверил, что Google меня наймет. Это длинный путь. Я потрачу на это месяцы. Если вы уже знакомы с большинством тем, то потратите намного меньше времени.

Как пользоваться

Ниже описан способ использования, вы должны выпонить пункты в описаном порядке.

Я использую разметку Github, включая список задач для оценки прогресса.

  • [x] Создай новую ветку и тогда вы сможете оставлять отметки у элементов списка, просто добавляя x внутрь скобок: [x]

    Скопируй репозиторий и выполни команды перечисленные ниже

git checkout -b progress

git remote add jwasham https://github.com/jwasham/google-interview-university

git fetch --all

Mark all boxes with X after you completed your changes

git add .

git commit -m "Marked x"

git rebase jwasham/master

git push --force

Подробнее о разметке на Github

Получи гугловское настроение

Напечатай знак "future Googler" и смотри на приз.

future Googler sign

Получил ли я работу?

Пока я ожидаю своей очереди. Надеюсь интервью будет скоро.

Thanks for the referral, JP.

Двигайся вместе со мной

Моя история: Почему я готовился в течении 8 месяцев для собеседования в Google

Я еще на пути к цели. Двигаемся вперед:

John Washam - Google Interview University

Не переживайте о том, что вы не достаточно умны

О Google

О видео ресурсах

Некоторые видео доступно в том случае, если вы являетесь слушателями курсов Coursera, EdX или Lynda.com Их называют MOOCs. Некоторые курсы не имеют круглогодичного доступа и вам нужно подождать несколько месяцев, прежде чем получите к ним доступ. Курсы на Lynda.com платные.

Я был бы вам благодарен за помощь в добавлении бесплатных, всегда доступных публичных ресурсов, таких как видео с YouTube
сопровождающих онлайн курсы. Мне нравяться использовать университетские лекции.

Процесс собеседования & Основное в подготовке к интервью

Pick One Language for the Interview

I wrote this short article about it: Important: Pick One Language for the Google Interview

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

  • C++
  • Java
  • Python

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

  • JavaScript
  • Ruby

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

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

If you have tons of extra time:

Computer Architecture

If short on time:

  • 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 (I want this book):

Language Specific

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.
    • To quote Yegge: "More than any other book it helped me understand just how astonishingly commonplace (and important) graph problems are – they should be part of every working programmer's toolkit. The book also covers basic data structures and sorting algorithms, which is a nice bonus. But the gold mine is the second half of the book, which is a sort of encyclopedia of 1-pagers on zillions of useful problems and various ways to solve them, without too much detail. Almost every 1-pager has a simple picture, making it easy to remember. This is a great way to learn how to identify hundreds of problem types."
    • Can rent it on kindle
    • Half.com is a great resource for textbooks at good prices.
    • 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.
    • To quote Yegge: "But if you want to come into your interviews prepped, then consider deferring your application until you've made your way through that book."
    • Half.com is a great resource for textbooks at good prices.
    • 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

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:

  • Flashcards site repo
  • My flash cards database: 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 by Google.

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.

What you won't see covered

This big list all started as a personal to-do list made from Google interview coaching notes. These are prevalent technologies but were not mentioned in those notes:

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

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

More Knowledge

Trees

Sorting

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

System Design, Scalability, Data Handling

Final Review

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

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.

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

My Process for Coding Interview (Book) Exercises

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

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

Challenge sites:

Maybe:

Once you're closer to the interview

Your Resume

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 Google 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.

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

Everything below this point is optional. These are my recommendations, not Google's.
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.

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

Additional Books

Additional Learning

--

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?

Video Series

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

Computer Science Courses