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Improved a few lines.

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John Washam 2016-06-06 21:15:38 -07:00
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plan.txt
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@ -15,12 +15,14 @@ to use this the same way. If you search/replace, there are a couple of places to
* - Videos:
* - https://www.youtube.com/watch?v=oWbUtlUhwa8&feature=youtu.be
* - https://www.youtube.com/watch?v=qc1owf2-220&feature=youtu.be
- https://www.youtube.com/watch?v=8npJLXkcmu8
Articles:
- http://dondodge.typepad.com/the_next_big_thing/2010/09/how-to-get-a-job-at-google-interview-questions-hiring-process.html
- http://steve-yegge.blogspot.com/2008/03/get-that-job-at-google.html
- http://sites.google.com/site/steveyegge2/five-essential-phone-screen-questions
- http://www.google.com/about/careers/lifeatgoogle/hiringprocess/
* - http://steve-yegge.blogspot.com/2008/03/get-that-job-at-google.html
- all the things he mentions that you need to know are listed below
* - (very dated) http://dondodge.typepad.com/the_next_big_thing/2010/09/how-to-get-a-job-at-google-interview-questions-hiring-process.html
- http://sites.google.com/site/steveyegge2/five-essential-phone-screen-questions
Additional (not suggested by Google but I added):
- https://courses.csail.mit.edu/iap/interview/materials.php
@ -241,14 +243,15 @@ Graphs:
- matrix
- adjacency list
- familiarize yourself with each representation and its pros & cons
- now their computational complexity, their tradeoffs, and how to implement them in real code
- BFS and DFS - know their computational complexity, their tradeoffs, and how to implement them in real code
- If you get a chance, try to study up on fancier algorithms:
- Dijkstra
- A*
- when asked a question, look for a graph-based solution first, then move on if none.
Other data structures:
- You should study up on as many other data structures and algorithms as possible
- You should especially 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.
- Find out what NP-complete means.
- Know what NP-complete means.
Recursion
- when it is appropriate to use it
Algorithmic complexity