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Built off of the popular JWasham coding interview university — This is a custom study plan for becoming a Full-Stack Software Engineer in the ML/AI space. [Overhaul Coming Q4 2018]

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Project 9894

Welcome to the Project 9894.

Whether you’re a student or an educator, newer to computer science or a more experienced coder, or otherwise interested in software engineering, I hope there’s something for you here at Project 9894.

Update September 26th, 2018:

For anyone wondering, this repository is built off of the original coding interview university by John Washam. Anything marked as a checkmark has been double checked and approved and thus will stay in the final copy of this repository.

However, if something is not checked, don't count on it staying. Refer back to the original for the single source of truth.

Update August 9th, 2018:

I am currently revising the Data Science aspect of 9894, as well as the Data Structures & Algorithms section. I want to touch base with John Washam to get further feedback from him, as well as with some of my colleagues at Google. This comprehensive guide as it stands now is a bit too exhaustive. If you complete all of it, or are close to, you'll definitely be more than capable of working at a very high level. But there should be some distinction. You don't actually need the section on Data Structures & Algorithms if you want to work purely with product.

Some aspects are beneficial and I will do my best to show what is needed and what is not.

As for the Full-Stack Web guide, some people have messaged me on Twitter with regards to what order to study each listing and I must say there is no order.

I made the Full-Stack Web document to exist as more of a reference to some of the best guides I've found online to allow me to be productive at my job. However, I think there should...be more clarity there.

So with the overhaul, there will be a path setup for those who want to focus more heavy on Data Science as a whole and those who want to transition into roles as Research Software Engineers, or Software Engineers working with researchers and scientists.

Expect all of this to be complete before the end of 2018. In the mean time, continue going through the list, nothing that I can think of will be dropped yet, besides the guides on Webpack 2; you shouldn't be focused on Webpack 2 anymore, due to Webpack 4 being a thing now. At the very least, if you have to look at Webpack 3 that's fine. I just won't be providing that material here.

Last but not least, the data science material that you need will also be added to this list, but that will get it's own Markdown file more than likely.

A general corpus of everything will be live on Heroku sometime in October or November.

Disclaimer

Before you get overly enthusiastic about this program, you should know that Project 9894 is not meant for the average individual. It is instead intended for university-level Computer Science students who are considering seeking an internship or full-time role at a top technology company.

Furthermore, it is not complete, in the sense that I do not dive into several methodologies. Nor do I try to replace courses available at your university. You should look at Project 9894 instead as a guide more than anything, to supplement your learning.

My hope is that you are also coming into this with a basic understanding of Computer Science or at least have an idea as to how a computer works. Nothing too crazy, just a basic understanding of operating systems, hardware architecture, and the essential aspects of the language itself.

If you are fresh, though, I did ensure to include resources that I utilized before beginning this guide and I will make an effort to keep the curriculum as up-to-date as possible.

So What Is Project 9894

Project 9894 is a product based curriculum that will help prepare you for an interview at just about any software company that exists, including the top 5: Amazon, Facebook, Google, Microsoft, and Apple.

I created it to supplement my time spent at Make School, where I am currently majoring in Computer Science with an emphasis in Mobile Development and minor in Data Science.

Hence, most of the material you will come across, while, heavily focused on Data Structures & Algorithms, will also cover Mobile Development and Data Science.

Additionally, I will go quite in-depth on Data Science, even though it is my minor. The reason being is that my goal is to end up working as a Software Engineer for a Research Lab developing tools and infrastructure for scientists.

You need to know computer science front to back to do this and because the path to getting there is quite long, you will need capital along the way to support yourself. What better way to make money than to create mobile applications for companies both large and small.

When we go about using technology every single day, we don't realize that the programs we interact with are built by people. People who know how to code. And if these individuals can learn, so can anyone. Even you.

Programming knowledge is indispensable in today’s world, and learning to code is one of the most valuable and useful things you can do.

Whether you’re launching a career, advancing a career, or just excited to learn a new skill, there is no time like the present to start learning, and this program offers everything you need to get up to speed—with no prior programming skills required.

By completing this prework, you will accelerate your time becoming a software engineer, and can even make it so your can graduate from the Product College early. If you do not complete this prework, you will not be "in trouble" but you might be discouraged by being less prepared than other students which will make your time more difficult.

You may already possess the skills and knowledge to "test out" of these classes. Or you may not. Either way, it is important for you to review and complete the following prework to level up your skills.

The primary languages that you will learn at the Product College are Python (for CS) and either JavaScript (for web) or Swift (for mobile). This prework focuses on these languages and the major paradigms for web and mobile development.

This is my multi-month study plan for preparing for a career as a software engineer specializing in Machine Learning at a top company.

Computer Science Foundations

Table of Contents

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

Why use it?

When I started this project, I didn't know a stack from a heap, didn't know Big-O anything, anything about trees, or how to traverse a graph. If I had to code a sorting algorithm, I can tell ya it wouldn't have been very good. Every data structure I've ever used was built into the language, and I didn't know how they worked under the hood at all. I've 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've used a few multidimensional arrays in my life and thousands of associative arrays, but I've 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.

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 check progress.

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

Fork a branch and follow the commands below

git checkout -b progress

git remote add jwasham https://github.com/jwasham/coding-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

More about Github-flavored markdown

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

Pick One Language for the 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

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.
    • 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.
    • 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:

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.

What you won't see covered

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

  • 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

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

System Design, Scalability, Data Handling

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.

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

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.

Coding Interview Question Videos:

Challenge sites:

Challenge repos:

Mock Interviews:

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.

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

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.

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

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.

--

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

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Built off of the popular JWasham coding interview university — This is a custom study plan for becoming a Full-Stack Software Engineer in the ML/AI space. [Overhaul Coming Q4 2018]

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