代码拉取完成，页面将自动刷新
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.
The items listed here will prepare you well for in an interview at just about any software company, including the giants: Amazon, Facebook, Google or Microsoft.
Best of luck to you!
Translations:
This is my multi-month study plan for going from web developer (self-taught, no CS degree) to software engineer for a large company.
This is meant for new software engineers or those switching from software/web development to software engineering (where computer science knowledge is required). If you have many years of experience and are claiming many years of software engineering experience, expect a harder interview.
If you have many years of software/web development experience, note that large software companies like Google, Amazon, Facebook and Microsoft view software engineering as different from software/web development, and they require computer science knowledge.
If you want to be a reliability engineer or systems engineer, study more from the optional list (networking, security).
---------------- Everything below this point is optional ----------------
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.
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
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.
Cracking The Coding Interview Set 1:
How to Get a Job at the Big 4:
Prep Course:
You can use a language you are comfortable in to do the coding part of the interview, but for large companies, these are solid choices:
You could also use these, but read around first. There may be caveats:
You need to be very comfortable in the language and be knowledgeable.
Read more about choices:
You'll see some C, C++, and Python learning included below, because I'm learning. There are a few books involved, see the bottom.
This is a shorter list than what I used. This is abbreviated to save you time.
If you have tons of extra time:
If short on time:
If you have more time (I want this book):
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.
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.
OR:
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)
"Algorithms and Programming: Problems and Solutions" by Shen
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.
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
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)
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.
There are a lot of distractions that can take up valuable time. Focus and concentration are hard.
These are prevalent technologies but not part of this study 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:
You don't need all these. You need only one language for the interview.
Why code in all of these?
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.
Learn C
How computers process a program:
nothing to implement
Big O Notation (and Omega and Theta) - best mathematical explanation (video)
Skiena:
TopCoder (includes recurrence relations and master theorem):
If some of the lectures are too mathy, you can jump down to the bottom and watch the discrete mathematics videos to get the background knowledge.
Videos:
Online Courses:
implement with array using linear probing
Notes:
For heapsort, see Heap data structure above. Heap sort is great, but not stable.
UC Berkeley:
Merge sort code:
Quick sort code:
Implement:
Not required, but I recommended them:
As a summary, here is a visual representation of 15 sorting algorithms. If you need more detail on this subject, see "Sorting" section in Additional Detail on Some Subjects
Graphs can be used to represent many problems in computer science, so this section is long, like trees and sorting were.
Notes:
Skiena Lectures - great intro:
Graphs (review and more):
Full Coursera Course:
I'll implement:
You'll get more graph practice in Skiena's book (see Books section below) and the interview books
If you need more detail on this subject, see "String Matching" section in Additional Detail on Some Subjects
This section will have shorter videos that you can watch pretty quickly to review most of the important concepts.
It's nice if you want a refresher often.
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:
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.
Supplemental:
Read and Do Programming Problems (in this order):
See Book List above
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:
Mock Interviews:
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.
Some of mine (I already may know answer to but want their opinion or team perspective):
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.
*****************************************************************************************************
*****************************************************************************************************
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.
Know least one type of balanced binary tree (and know how it's implemented):
"Among balanced search trees, AVL and 2/3 trees are now passé, and red-black trees seem to be more popular. A particularly interesting self-organizing data structure is the splay tree, which uses rotations to move any accessed key to the root." - Skiena
Of these, I chose to implement a splay tree. From what I've read, you won't implement a balanced search tree in your interview. But I wanted exposure to coding one up and let's face it, splay trees are the bee's knees. I did read a lot of red-black tree code.
I want to learn more about B-Tree since it's used so widely with very large data sets.
AVL trees
Splay trees
Red/black trees
2-3 search trees
2-3-4 Trees (aka 2-4 trees)
N-ary (K-ary, M-ary) trees
B-Trees
--
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?
Union-Find
More Dynamic Programming (videos)
Advanced Graph Processing (videos)
MIT Probability (mathy, and go slowly, which is good for mathy things) (videos):
String Matching
Sorting
Sit back and enjoy. "Netflix and skill" :P
List of individual Dynamic Programming problems (each is short)
Excellent - MIT Calculus Revisited: Single Variable Calculus
Computer Science 70, 001 - Spring 2015 - Discrete Mathematics and Probability Theory
CSE373 - Analysis of Algorithms (25 videos)
UC Berkeley CS 152: Computer Architecture and Engineering (20 videos)
Carnegie Mellon - Computer Architecture Lectures (39 videos)
MIT 6.042J: Mathematics for Computer Science, Fall 2010 (25 videos)
MIT 6.050J: Information and Entropy, Spring 2008 (19 videos)
此处可能存在不合适展示的内容，页面不予展示。您可通过相关编辑功能自查并修改。
如您确认内容无涉及 不当用语 / 纯广告导流 / 暴力 / 低俗色情 / 侵权 / 盗版 / 虚假 / 无价值内容或违法国家有关法律法规的内容，可点击提交进行申诉，我们将尽快为您处理。