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यह मेरा वेब डेवलपर से गूगल सॉफ्टवेर इंजिनियर बनाने की अद्ययन योजना हैं.
यह लम्बी सूचि गूगल कोचिंग नोट्स से छाती एव विस्तारित की गयी हैं, ताकि इन बातो को आपको पता चल सके. मैंने आपके इंटरव्यू में मदत कर सकने वाले कुछ अतिरिक्त विषय सूचि के आखिर में डाले हे.
मैं यह योजना का अनुपालन गूगल इनेर्विएव के तयारी के लिए कर रहा हूँ. मैं १९९७ से वेब, सर्विसेज और स्टार्टअप का निर्माण कर रहा हूँ. मेरे पास संगणक शात्र की पदवी ना होक अर्थशात्र की पदवी हैं. मैं अपने कैरियर में बहुत सफल रहा हूँ, पर मुजे गूगल में काम करने की इच्छा हें. मैं एक बड़े सिस्टम में प्रगति और कंप्यूटर प्रणालियों की एक असली समझ प्राप्त करना चाहते हु, अल्गोरिथम की निपुणता, डाटा स्ट्रक्चर का निष्पादन, लो-लेवल भाषाए, और वो कैसे काम करती हें. और अगर आपको एनमेंसे किसी की जानकारी नहीं हे तो गूगल आपको नियुन्क्त नहीं करेगा. मैंने जब ये परियोजना शुरू की, तब मैं स्टैक और हीप में फरक नहीं जनता था, मुजे नहीं पता था की Big-O क्या हे, ट्रीज क्या हे, या ग्राफ को पार कैसे करते हैं. अगर मुजे छाटने का अल्गोरिथम लिखना पड़ता तो मैं आपको ये बता सकता हु के वो इतना ख़ास नहीं होगा. जो भी डाटा स्ट्रक्चर का मैंने उपयोग किया वो भाषा में समाविष्ट था, और वो कैसे काम करता हे उसकी कोई जानकारी मुजे नहीं थी. मुजे कभी मेमोरी का संचालन नहीं करता पड़ा, जबतक मेरी चलाई कोई प्रोसेस "out of memory" का एरर न दे, और तब मुजे कोई वैकल्पिक हल धुन्दाना पड़ता था. मैंने मेरी जिन्दगी में बहोत कम मुल्ती-डायमेंशनल ऐरे और बहोत सारे अस्सोसिअतिव् ऐरे का उपयोग किया हे, पर मैंने कोई भी डाटा स्ट्रक्चर शुरू से नहीं लिखा था. पर इस अध्ययन योजना का उपयोग करने बाद मेरा नौकरी लगाने का आत्मविश्वास बहोत बढ़ा हें. यह एक लम्बी योजना हें. यह मेरे लिए बहोत महीनोतक चलेगी. अगर आपको ईंमैसे कुछ पता हैं तो आपको कम वक्त लगेगा.
नीचे सब कुछ एक रूपरेखा है, और आप ऊपर से नीचे के क्रम में पढ़े.
मैं Github के विशेष Markdown का उपयोग कर रहा हूँ, प्रगति की जाँच करने के लिए कार्य सूचियों का प्रयोग करे.
Github-flavored markdown की अधिक जानकारी
Print out a "future Googler" sign (or two) and keep your eyes on the prize.
I haven't applied yet.
I still have a few days in the learning phase (finishing up this crazy list), and starting next week all I'll be doing is programming questions all day long. That will continue for a few weeks, and then I'll apply through a referral I've been holding onto since February (yes, February).
Thanks for the referral, JP.
I'm on the journey, too. Follow along on my blog at GoogleyAsHeck.com
Some videos are available only by enrolling in a Coursera, EdX, or Lynda.com class. These are called MOOCs. It is free to do so, but sometimes the classes are not in session so you have to wait a couple of months, so you have no access.
I'd appreciate your help converting the MOOC video links to public sources to replace the online course videos over time. I like using university lectures.
[ ] Videos:
[ ] Articles:
[ ] Additional (not suggested by Google but I added):
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:
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 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 (see below).
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, whereever I am.
Make your own for free:
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.
I keep a set of cheatsheets 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 is hard.
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:
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.
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, not a computer. Test with some sample inputs. Then test it out on a computer.
[ ] How computers process a program:
[ ] How floating point numbers are stored:
[ ] Computer Arch Intro: (first video only - interesting but not required) Introduction and Basics - Carnegie Mellon - Computer Architecture
[ ] Compilers
[ ] Cheat sheet
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
[ ] AVL trees
[ ] Splay trees
[ ] 2-3 search trees
[ ] 2-3-4 Trees (aka 2-4 trees)
[ ] B-Trees
[ ] Red/black trees
[ ] Notes:
For heapsort, see Heap data structure above. Heap sort is great, but not stable.
[ ] Stanford lectures on sorting:
[ ] Shai Simonson, Aduni.org:
[ ] Steven Skiena lectures on sorting:
[ ] UC Berkeley:
[ ] - Merge sort code:
[ ] - Quick sort code:
[ ] Implement:
[ ] For curiosity - not required:
Graphs can be used to represent many problems in computer science, so this section is long, like trees and sorting were.
Notes from Yegge:
[ ] Skiena Lectures - great intro:
[ ] Graphs (review and more):
Full Coursera Course:
Yegge: If you get a chance, try to study up on fancier algorithms:
I'll implement:
You'll get more graph practice in Skiena's book (see Books section below) and the interview books
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.
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.
(More items will be added here)
Read and do exercises:
[ ] The Algorithm Design Manual (Skiena)
Once you've understood everything in the daily plan, and read and done exercises from the the books above, read and do exercises from the books below. Then move to coding challenges (further down below)
Read first:
Read second (recommended by many, but not in Google coaching docs):
These were not suggested by Google but I added because I needed the background knowledge
[ ] C Programming Language, Vol 2
[ ] C++ Primer Plus, 6th Edition
[ ] Introduction to Algorithms
[ ] Elements of Programming Interviews
Once you've learned your brains out, put those brains to work. Take coding challenges every day, as many as you can.
Think of about 20 interview questions you'll get, along 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):
Everything below is my recommendation, not Google's, and you may not have enough time to
learn, watch or read them all. That's ok. I may not either.
--
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?
[ ] More Dynamic Programming (videos)
[ ] Advanced Graph Processing (videos)
[ ] MIT Probability (mathy, and go slowly, which is good for mathy things) (videos):
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 61B (Spring 2014): Data Structures (25 videos)
[ ] UC Berkeley 61B (Fall 2006): Data Structures (39 videos)
[ ] UC Berkeley CS 152: Computer Architecture and Engineering (20 videos)
[ ] MIT 6.034 Artificial Intelligence, Fall 2010 (30 videos)
[ ] MIT 6.042J: Mathematics for Computer Science, Fall 2010 (25 videos)
[ ] MIT 6.046: Design and Analysis of Algorithms (34 videos)
[ ] MIT 6.050J: Information and Entropy, Spring 2008 (19 videos)
[ ] Stanford: Programming Paradigms (17 videos)
[ ] Mining Massive Datasets - Stanford University (94 videos)
http://www.gainlo.co/ - Mock interviewers from big companies
Congratulations!
Keep learning.
You're never really done.