24fe5be8bd
Added new directory for translated README files.
8 年之前
README-ar.md
الإعداد لمقابلة البرمجة
أنشأتها في المقام الأول كلائحة قصيرة من المواضيع الدراسية لكيف يصبح المرء مهندس برمجيات, و ولكن سرعان ما كبرت هذه القائمة الى ما تراه امامك اليوم. بعد خوض هذه الخطة الدراسية,
تم توظيفي كمهندس تطوير برمجيات لدى أمازون! على الاغلب لن تحتاج إلى ان تدرس بالقدر الذي درسته أنا. لكن على كل حال كل ما تحتاج إليه موجود
هنا
هي خطتي متعددة الأشهر للوصول من مطور ويب (تعليم ذاتي، بدون درجة علمية في علوم الحاسب) لمنصب مهندس برمجيات لشركة عملاقة
تم إعدادها لمهنسي البرمجيات الجدد او أولئك المنتقلين من تطوير الويب إلى هندسة البرمجيات (حيث المعرفة بعلوم الحاسب ضرورية) إذا كان لديك العديد من سنوات الخبرة في بناء تطبيقات الويب أو البرمجيات، خذ في عين الإعتبار ان المقابلة ستكون اصعب
إذا كانت لديك العديد من سنوات الخبرة في تطوير الويب, خذ في عين الإعتبار أن الشركات االبرمجية الضخمة مثل قوقل, فيسبوك, و ميكروسوفت ينظرون إلى هندسة البرمجيات بشكل مختلف عن تطوير البرمجيات/ويب, و تحتاج إلى معرفة علوم الحاسب
إذا اردت ان تصبح مهندس موثوقية أو مهندس عمليات, ادرس اكثر من القائمة الإختيارية (شبكات, امن)
أقوم بمتابعة هذه الخطة لتحضير إلى المقابلة الشخصية بجوجل. لقد قمت بناء مواقع ويب، وتقديم خدمات ذات صلة، وبناء شركات ناشئة منذ 1997. لدي درجة علمية في الاقتصاد، وليس في علوم الحاسب. أنا شخص ناجح في مجال عملي، ولكنني أريد أن أعمل بجوجل. أريد أن أعمل على أنظمة كبيرة والحصول على فهم كبير في أنظمة الحاسوب، كفاءة الخوارزميات، كقاءة الهياكل البيانية، اللغات الأقرب إلى الآلة وكيفية عملها. وإذ لم تعرف أين منها لن تعينك جوجل.
عندما بدأت هذا المشروع، لم أكن أعرف الإستاك "stack" من الهيب "heap"، ولم أكن أعرف المعامل الأعلى في قياس كفاءة الخوارزميات "Big-O"، ولا عن التري "tree"، أو عن زيارة الجراف "graph".
إذا كان عليا أن أصنع برنامج عن الترتيب، سأخبرك أنه ليس على درجة عالية من الكقاءة.
كل هياكل البيانات التي استخدمتها كانت من الأشياء السابق إعدادها في اللغة البرمجية, ولم أعرف كيفية عملها من الداخل. لم أعرف إطلاقا كيفية تنظيم الذاكرة مالم أحصل على "خارج نطاق الذاكرة" من عملية برمجية، وعندها كان عليا أن أجد طريقة ما لتحايل على الأمر. لقد استخدمت مصفوفة من أكثر من بعد في مرات قليلة, وألاف من المصفوفات المترابطة، لكن لم أنشأ هياكل بيانات من البداية.
لكن عند المضي قدما في هذه الدراسة وجدت أني على ثقة عالية من أنه سيتم توظيفي. إنها خطة طويلة، أخدت مني شهور. إذا كانت على دراية من كثير من هذه الأشياء ستأخد وقتا أقل.
كيفية استخدامها
كل عنصر من هذه القائمة مرتب لذلك عليم أن تتبع العناصر من أعلى إلى أسفل.
سأستخدم خاصية تعليم ماأنجز من الجيت هب "Github" لمتابعة التقدم.
بعض الفديوهات متاحة فقط عن طريق الاشتراك في كورسيرا "Coursera"، إيدكس "Edx" أو ليندا "Lynda.com". يطلق عليهم موكس"MOOCS".
بعض الأحيان الدروس ليست متاحة في كل الأوقات لذلك عليك الانتظار عدة أشهر حتى تكون متاحة للالتحاق بها. دروس ليندا "Lynda" ليست مجانية.
أقدر مساعدتك لإضافة مصارد متاحة دائما مثل اليوتيوب "Youtube" أو مصادر أخرى متاحة.
For a richer, more up-to-date (2011), but longer treatment
تحديد اللغة
عليك اختيار لغة واحدة للمقابلة الشخصية (انظر للأعلى). هنا بعض التوصيات للغات. لا أملك كل المصادر للغات. ارجب بالإضافة
إذا قرأت واحدا فقط من هذه المصارد، سيكون لديك كل الهياكل البيانية والخوارزميات للبدء في المشاكل البرمجية.
يمكن تخطي كل محاضرات الفديو في هذا المشروع، في حالة إذا لا تريد أن تلقي النظر عليهم
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:
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.
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
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.
قبل البدء
هذه القائمة تمتد لأشهر نعم وهذا ما باليد حيلة.
هنا بعض الأخطاء التي فعلتها لذا لديك فرصة أفضل.
1. لن تتذكر هذا كله
لقد شاهدت ساعات من الفديوهات وأخذت مدونات لها، وبعد شهور لم أتذكر شيئا. استغرقت 3 أيام لمراجعة مادرسته وعمل فلاش كارد للتذكري.
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.
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.
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.
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.
Gotcha: you need pointer to pointer knowledge:
(for when you pass a pointer to a function that may change the address where that pointer points)
This page is just to get a grasp on ptr to ptr. I don't recommend this list traversal style. Readability and maintainability suffer due to cleverness.
dequeue() - returns value and removes least recently added element (front)
empty()
Implement using fixed-sized array:
enqueue(value) - adds item at end of available storage
dequeue() - returns value and removes least recently added element
empty()
full()
Cost:
a bad implementation using linked list where you enqueue at head and dequeue at tail would be O(n)
because you'd need the next to last element, causing a full traversal each dequeue
enqueue: O(1) (amortized, linked list and array [probing])
NOTE: DP is a valuable technique, but it is not mentioned on any of the prep material Google provides. But you could get a problem where DP provides an optimal solution. So I'm including it.
This subject can be pretty difficult, as each DP soluble problem must be defined as a recursion relation, and coming up with it can be tricky.
I suggest looking at many examples of DP problems until you have a solid understanding of the pattern involved.
Videos:
the Skiena videos can be hard to follow since he sometimes uses the whiteboard, which is too small to see
Know about the most famous classes of NP-complete problems, such as traveling salesman and the knapsack problem,
and be able to recognize them when an interviewer asks you them in disguise.
Reading all from end to end with full comprehension will likely take more time than you have. I recommend being selective on papers and their sections.
You can expect system design questions if you have 4+ years of experience.
Scalability and System Design are very large topics with many topics and resources, since
there is a lot to consider when designing a software/hardware system that can scale.
Expect to spend quite a bit of time on this.
For even more, see "Mining Massive Datasets" video series in the Video Series section.
Practicing the system design process: Here are some ideas to try working through on paper, each with some documentation on how it was handled in the real world:
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.
Series of 2-3 minutes short subject videos (23 videos)
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.
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 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?
*****************************************************************************************************
*****************************************************************************************************
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.
*****************************************************************************************************
*****************************************************************************************************
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.
splay tree: insert, search, delete functions
If you end up implementing red/black tree try just these:
search and insertion functions, skipping delete
I want to learn more about B-Tree since it's used so widely with very large data sets.
In practice:
From what I can tell, these aren't used much in practice, but I could see where they would be:
The AVL tree is another structure supporting O(log n) search, insertion, and removal. It is more rigidly
balanced than red–black trees, leading to slower insertion and removal but faster retrieval. This makes it
attractive for data structures that may be built once and loaded without reconstruction, such as language
dictionaries (or program dictionaries, such as the opcodes of an assembler or interpreter).
In practice:
Splay trees are typically used in the implementation of caches, memory allocators, routers, garbage collectors,
data compression, ropes (replacement of string used for long text strings), in Windows NT (in the virtual memory,
networking and file system code) etc.
In practice:
Red–black trees offer worst-case guarantees for insertion time, deletion time, and search time.
Not only does this make them valuable in time-sensitive applications such as real-time applications,
but it makes them valuable building blocks in other data structures which provide worst-case guarantees;
for example, many data structures used in computational geometry can be based on red–black trees, and
the Completely Fair Scheduler used in current Linux kernels uses red–black trees. In the version 8 of Java,
the Collection HashMap has been modified such that instead of using a LinkedList to store identical elements with poor
hashcodes, a Red-Black tree is used.
In practice:
For every 2-4 tree, there are corresponding red–black trees with data elements in the same order. The insertion and deletion
operations on 2-4 trees are also equivalent to color-flipping and rotations in red–black trees. This makes 2-4 trees an
important tool for understanding the logic behind red–black trees, and this is why many introductory algorithm texts introduce
2-4 trees just before red–black trees, even though 2-4 trees are not often used in practice.
fun fact: it's a mystery, but the B could stand for Boeing, Balanced, or Bayer (co-inventor)
In Practice:
B-Trees are widely used in databases. Most modern filesystems use B-trees (or Variants). In addition to
its use in databases, the B-tree is also used in filesystems to allow quick random access to an arbitrary
block in a particular file. The basic problem is turning the file block i address into a disk block
(or perhaps to a cylinder-head-sector) address.
MIT 6.851 - Memory Hierarchy Models (video)
- covers cache-oblivious B-Trees, very interesting data structures
- the first 37 minutes are very technical, may be skipped (B is block size, cache line size)
k-D Trees
great for finding number of points in a rectangle or higher dimension object
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?