Ask HN: What's the best algorithms and data structures online course?
38 comments
Video lectures from MIT course with same name are available here[1]. Few lectures are given by Leiserson, who is one of the authors of this book.
[1] http://videolectures.net/mit6046jf05_introduction_algorithms...
[1] http://videolectures.net/mit6046jf05_introduction_algorithms...
Isn't this book too academical for any practical learning of algorithms and data structures?
I would recommend The algorithm design manual for more practical purposes. https://www.amazon.com/Algorithm-Design-Manual-Steven-Skiena...
I would recommend The algorithm design manual for more practical purposes. https://www.amazon.com/Algorithm-Design-Manual-Steven-Skiena...
> Isn't this book too academical for any practical learning of algorithms and data structures?
I don't think so, I've worked through it and I didn't find it that difficult/academic. But I actually don't read a lot of computer science books / textbooks so I don't really have much to compare it to other than mathematical texts which I do read a lot of.
If you don't like proofs or math then its probably not the best text to work through, on the other hand, if you like rigorously understanding the material I would highly recommend it.
Either way, from what I remember it gives psuedocode for just about everything and has lots of graphs and pictures for elucidating the material, so you could probably just skip the math if you have an allergy to corrolaries, theorems, and proofs. Admittedly, that extra insight is probably a lot of the reason I liked it so much.
I don't think so, I've worked through it and I didn't find it that difficult/academic. But I actually don't read a lot of computer science books / textbooks so I don't really have much to compare it to other than mathematical texts which I do read a lot of.
If you don't like proofs or math then its probably not the best text to work through, on the other hand, if you like rigorously understanding the material I would highly recommend it.
Either way, from what I remember it gives psuedocode for just about everything and has lots of graphs and pictures for elucidating the material, so you could probably just skip the math if you have an allergy to corrolaries, theorems, and proofs. Admittedly, that extra insight is probably a lot of the reason I liked it so much.
His lectures from 2016 are also on YouTube:
https://www.youtube.com/watch?v=A2bFN3MyNDA&list=PLOtl7M3yp-...
In addition to the Stanford courses mentioned in another comment, there are also two Princeton courses taught by Sedgewick https://www.coursera.org/instructor/~250165 and the MIT course taught by Demaine https://ocw.mit.edu/courses/electrical-engineering-and-compu...
The followup to the Demaine one is great too. Not at all intro, but great if your foundations are strong.
https://ocw.mit.edu/courses/electrical-engineering-and-compu...
https://ocw.mit.edu/courses/electrical-engineering-and-compu...
Would you say this is good for someone who has recently graduated and has been in industry for a year or so to refresh on?
They touch on a bunch of decently exotic data structures like van Emde Boas trees and things like cache oblivious data structures. If you're comfortable with data structures and algorithm design (which it sounds like you are, from your description) it should be accessible. At the end of the day it's just a graduate level CS course.
Sedgewick also has some great books.
Don't know about the best, but I learned a lot from these two courses.
https://lagunita.stanford.edu/courses/course-v1:Engineering+...
https://lagunita.stanford.edu/courses/course-v1:Engineering+...
https://lagunita.stanford.edu/courses/course-v1:Engineering+...
https://lagunita.stanford.edu/courses/course-v1:Engineering+...
+1 for these. I took them a few years ago, and really enjoyed them! I reviewed both of them in detail:
https://henrikwarne.com/2012/05/08/coursera-algorithms-cours...
https://henrikwarne.com/2013/02/18/coursera-algorithms-cours...
https://henrikwarne.com/2012/05/08/coursera-algorithms-cours...
https://henrikwarne.com/2013/02/18/coursera-algorithms-cours...
Tim Roughgarden is a fantastic teacher. I personally love his style and speed. He throws in some humor here and there and makes learning a lot of fun. His lectures on graphs are absolutely brilliant.
This same course is available on Coursera as well.
This same course is available on Coursera as well.
These were my favorite courses on Coursera. A lot of the course material is available here. http://theory.stanford.edu/~tim/algorithmsilluminated.html
There’s also the book “Algorithms Illuminated: Part 1: The Basics” by Tim Roughgarden
https://www.amazon.com/gp/aw/d/0999282905/
There’s also the book “Algorithms Illuminated: Part 1: The Basics” by Tim Roughgarden
https://www.amazon.com/gp/aw/d/0999282905/
Yes. Same as Coursera. I took this on Coursera only, before specialization thing happened. Don't know if the course videos and exercises are fully available for audit purpose.
Thanks, I've looked at the syllabus and it seems to be exactly what I'm looking for (Asymptotic analysis, and coverage of the most widely known algorithms).
Second this recommendation really enjoyed these courses as well. Tim explains everything in an easy to follow way.
I can also really recommend these courses. Really like how Tim teaches!
It's not really a course but I found InterviewBit [0] a great resource. It structures everything around moving from simpler to more complex data structures and algorithms. It's kind of learning by doing lots and lots of questions. You have to complete enough problems in one section to move onto the next.
I spent a hundred hours going through the various tests and it showed the power of functional programming / recursion. The questions that took me much longer than average were the ones where I had a bug and had to track it down. The recursive problems didn't suffer from this, ie I would get the solution pretty much correct first time. This could just have been the questions / my programming style but I found it eye opening.
[0]: https://www.interviewbit.com/
I spent a hundred hours going through the various tests and it showed the power of functional programming / recursion. The questions that took me much longer than average were the ones where I had a bug and had to track it down. The recursive problems didn't suffer from this, ie I would get the solution pretty much correct first time. This could just have been the questions / my programming style but I found it eye opening.
[0]: https://www.interviewbit.com/
If your eventual goal after learning the basics is programming questions for interviews, there are a tons of resources like leetcode, interviewbit, geeksforgeeks. I started writing some of the FAQ with explanations here[1], check it out to see if it is of any help for you.
[1] : http://letstalkalgorithms.com/
[1] : http://letstalkalgorithms.com/
The best one is HSE & Yandex & Computer Science Club & UC San Diego 6 course specialization on coursera
https://www.coursera.org/specializations/data-structures-alg...
https://www.coursera.org/specializations/data-structures-alg...
I agree. This sequence of courses is the best I've found.
The princeton one on coursera is really great and easy to read. Buy the book and take the course. Sedgewick is my hero.
It doesn't require a lot of math in contrast to the Stanford coursera one. It's much easier for the general person to follow IMO. https://www.coursera.org/learn/algorithms-part1
All good resources. I'd also add Algorithms, Etc.
by Jeff Erickson.
http://jeffe.cs.illinois.edu/teaching/algorithms/
And for practice, Google Code Jam past problems are quite challenging ;)
https://code.google.com/codejam/
http://jeffe.cs.illinois.edu/teaching/algorithms/
And for practice, Google Code Jam past problems are quite challenging ;)
https://code.google.com/codejam/
The past problems are here: https://code.google.com/codejam/past-contests
Is it wise to do any of these courses without taking a course in linear algebra first? I started watching Skiena's algorithm course on youtube recently and he seemed to really emphasize finishing a course in linear algebra before taking any algorithm course. Thoughts?
No, I don't believe so (from my experience as a CS undergrad).
Linear algebra isn’t a prerequisite for learning data structures and algorithms. Though you typically do find linear algebra to be part of the lower division computer science curriculum at most colleges. I would recommend you take a course in it, I argue it expands one’s problem-solving mental models.
I certainly agree. However, if OP is simply referring to learning the typical algorithms presented in an undergraduate CS course, then linear algebra isn't a strict prerequisite.
I took both of Tim Roughgarden's algorithm courses on Coursera, and in my opinion you don't need to know linear algebra to complete them.
Just FYI, Kahn Academy's Linear Algebra course is great. Very thorough and well-thought-out.
Would recommend this for Linear Algebra. If possible, make a donation. He creates wonderful videos.
https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2x...
https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2x...
This is useful background: "Mathematics for Computer Science"
https://courses.csail.mit.edu/6.042/spring17/mcs.pdf
https://courses.csail.mit.edu/6.042/spring17/mcs.pdf
Yeah I'm currently going through this course. Some of it is going over my head but I get the basic concepts
The Intuitive Guide to Data Structures And Algorithms, on Interview Cake:
https://www.interviewcake.com/data-structures-and-algorithms...
Disclosure: my company made this :)
Disclosure: my company made this :)
These aren't courses, but from answers to
"Ask HN: Recommended course/website/book to learn data structure and algorithms" :
Data Structure: https://en.wikipedia.org/wiki/Data_structure
Algorithm:https://en.wikipedia.org/wiki/Algorithm
Big O notation:https://en.wikipedia.org/wiki/Big_O_notation
Big-O Cheatsheet: http://bigocheatsheet.com
Coding Interview University > Data Structures: https://github.com/jwasham/coding-interview-university/blob/...
OSSU: Open Source Society University > Core CS > Core Theory > "Algorithms: Design and Analysis, Part I" [&2] https://github.com/ossu/computer-science/blob/master/README....
"Algorithms, 4th Edition" (2011; Sedgewick, Wayne): https://algs4.cs.princeton.edu/
Complexity Zoo > Petting Zoo (P, NP,): https://complexityzoo.uwaterloo.ca/Petting_Zoo
While perusing awesome-awesomeness [1], I found awesome-algorithms [2] , algovis [3], and awesome-big-o [4].
[1] https://github.com/bayandin/awesome-awesomeness
[2] https://github.com/tayllan/awesome-algorithms
[3] https://github.com/enjalot/algovis
[4] https://github.com/okulbilisim/awesome-big-o
Data Structure: https://en.wikipedia.org/wiki/Data_structure
Algorithm:https://en.wikipedia.org/wiki/Algorithm
Big O notation:https://en.wikipedia.org/wiki/Big_O_notation
Big-O Cheatsheet: http://bigocheatsheet.com
Coding Interview University > Data Structures: https://github.com/jwasham/coding-interview-university/blob/...
OSSU: Open Source Society University > Core CS > Core Theory > "Algorithms: Design and Analysis, Part I" [&2] https://github.com/ossu/computer-science/blob/master/README....
"Algorithms, 4th Edition" (2011; Sedgewick, Wayne): https://algs4.cs.princeton.edu/
Complexity Zoo > Petting Zoo (P, NP,): https://complexityzoo.uwaterloo.ca/Petting_Zoo
While perusing awesome-awesomeness [1], I found awesome-algorithms [2] , algovis [3], and awesome-big-o [4].
[1] https://github.com/bayandin/awesome-awesomeness
[2] https://github.com/tayllan/awesome-algorithms
[3] https://github.com/enjalot/algovis
[4] https://github.com/okulbilisim/awesome-big-o
This book has great explanations and exercises for everything you could want to learn from the basics of sorting and algorithmic design and analysis, to graph algorithms, linear programming, and dynamic programming.
It lacks some degree of depth on more advanced topics, but if you work your way through it and actually implement what you read and do the exercises, you will be more than well enough equipped to take on just about any problem.
The key is going to be to actually implement what you read/learn, I think it might take you a little more time than watching an online course, but in the long run it will give you a much deeper knowledge of the material.
1. https://www.amazon.com/Introduction-Algorithms-Thomas-H-Corm...