Contents:
Child pages:
Data structures
Questions
- What is the threshold at which point it's a good idea to create a class for a data structure?
Non-algorithm-specific interview advice
Algorithms-Specific Informational Websites (not programming challenge websites)
Nick Parlante - Pointers, Binary Trees, etc.
Very good explanations
- GeeksForGeeks
- OK explanations
- Jason Park - Algorithm Visualizer
- Khan Academy - Algorithms
- Treehouse
Books
- Look good:
- The Algorithm Design Manual by Skiena
- Rec'd by Adam D'Angelo in a Quora post.
- Algorithms in a Nutshell, 2nd ed.
- Essential Algorithms - 5 stars on O'Reilly
- Working with Algorithms in Python - O'Reilly video series - 4.3 stars - Same guy who wrote 'Algorithms in a Nutshell'
- The Algorithm Design Manual by Skiena
- Maybe good:
- Meh:
Articles
- http://haseebq.com/how-to-break-into-tech-job-hunting-and-interviews/
First, a high-level roadmap.
Watch the Princeton Coursera course on algorithms (by Robert Sedgewick), mostly up until Dijkstra’s algorithm and not much more than that. Try to follow along and code as you go. You don’t need to implement things in Java unless you already know Java.
Read The Algorithm Design Manual for general algorithms and data structures background. Go through most of it. (You can skip the giant appendix about specific algorithmic topics).
Buy Cracking the Coding Interview. We’ll be using this as a repository of coding problems and solutions.
(...)
If you’re working on an algorithms problem (such as out of CTCI) and can’t figure it out, spend no more than 20-30 minutes without making progress. Just go look up the answer. Contrary to popular belief, most struggling past 30 minutes is pointless.Some people will disagree with this. [NW: Like Tom and Chris Uga.]
Screw those people.
That said, if you find up having to look up the answer to more than 2/3rds of problems, your problem set is too hard and you need to downgrade.
But banging your head against a wall or staring blankly at code is a waste of time. For code/algorithms problems, you very much want to maximize the density of learning per hour.
Any time you look up the answer to a coding problem, try to understand the solution. Walk through it with a debugger if it’s particularly mysterious. Read multiple explanations of why it works.
Then—and this is extremely important—if you didn’t solve the problem completely on your own, treat the problem as though you never solved it at all. Put it back into your rotation of problems. Mark it as something you need to try again from scratch. Wait at least a day, and try to solve it fresh. If you fail, rinse and repeat ad infinitum.
And finally, structure the hell out of your learning. Make a schedule. Know exactly when you’re going to be working on this stuff, when you’re going to take breaks, when you’re going to go to lunch, etc. Build flexibility into your schedule, but have clear goals for how many hours a day you’ll spend studying.
Interview Challenge sites
- Starfighter
- Stockfighter
- this is by Kalzumeus
Front-end interview challenge / prep sites
Interview Prep sites
- CareerCup
- rec'd by Choketsu
- rec'd by Choketsu
- Codility
- Lessons
- Challenges
- Rec'd by Toptal
- Cracking the Coding Interview - Video Interviews
- HackerRank
- rec'd by Choketsu
- InterviewBit
- via Tom A.
- Interview Cake
- Tech Interview Handbook (GitHub project)
Non-Interview-Prep Challenge sites
- beat.my.code()
- Rec'd by Toptal
- CoderByte
- Codility
- CodingBat
- Kaggle
- Someone who works with machine learning explaining how Kaggle is a simplification of real ML work:
- LeetCode
- Polish Olympiad in Informatics (POI)
- Project Euler
- Random Observations - Solving Project Euler Problems
- Jason B Hill's Project Euler solutions
- Rec'd by Toptal, Chris Uga
- Sphere Online Judge (SPOJ)
- Top Coder
- USACO Training
- rec'd by Choketsu
- How to get started training at the USACO Gateway:
- What is it like to attend the USACO training camp?
- http://www.quora.com/USA-Computing-Olym ... ining-camp
- It's only a week
- New people just get 3 hours of lab/lecture in the morning
- The top guys do a contest every day. Sometimes the contest goes until after lunch, otherwise they just review after lunch. (Seems like 5 hrs/day)
- Then everyone has fun for the rest of the day
- If one is stuck on a particular section, is it worthwhile to keep on trying to solve the given problem, or should one move on to other sources in preparation for programming contests?
- Richard Peng, one of the most successful Canadian IOI competitors of all time, on the USACO training pages:
- "USACO training was put together before the major IOI escalation (Finland/Korea/Wisconsin). A lot of the techniques described on it are no longer useful on *OI and a lot of the 'hot' topics over the past few years are not covered. Also, a lot of the bottle necks on training are quite meaningless, and they typically cause a lot of frustration and time waste on the scale of months."
Topics
Linked Lists
Databases
Dynamic Programming (DP)
- http://en.wikipedia.org/wiki/Dynamic_programming
- Jesse Farmer - Intro to Dynamic Programming (blog post)
- https://activities.tjhsst.edu/sct/lectures/0405/dp1.pdf
- "Dynamic programming, often abbreviated "DP", is a technique to efficiently solve recursion problems -- problems whose solutions depend on solutions of smaller problems -- by storing partial results. Many times, it is obvious that a given problem is a recursion problem. However, it may not be so obvious that using DP will tremendously speed up the solution. We start by looking at an example."