If you have ever used a navigation service to find optimal route and estimate time to destination, you've used algorithms on graphs. Graphs arise in various real-world situations as there are road networks, computer networks and, most recently, social networks! If you're looking for the fastest time to get to work, cheapest way to connect set of computers into a network or efficient algorithm to automatically find communities and opinion leaders in Facebook, you're going to work with graphs and algorithms on graphs. In this course, you will first learn what a graph is and what are some of the most important properties. Then you'll learn several ways to traverse graphs and how you can do useful things while traversing the graph in some order. We will then talk about shortest paths algorithms — from the basic ones to those which open door for 1000000 times faster algorithms used in Google Maps and other navigational services. You will use these algorithms if you choose to work on our Fast Shortest Routes industrial capstone project. We will finish with minimum spanning trees which are used to plan road, telephone and computer networks and also find applications in clustering and approximate algorithms....

Very well taught. I would like to recommend to every new comer to do at least once.

The explanation of lecturer is not good as expected. Overall it is good.

great course, too bad it seems not being actively mananged

Some topic are hard to follow because too few examples

Good course for understanding algorithms for graphs

Not clear what is wrong on programming assignment

Was very informative course. Learned a lot stuff.

Interesting course to start learning about graphs

This course is better than the previous 2 by far

Good Course material and Assignment.

Good sessions and assignments.

I really enjoyed the course.

Very good explanations

Amazing course

Nice course.

Great

Nice

I must say I feel disappointed especially when the whole Data Structures and Algorithms series take this course as the selling point ( "you'll learn how to compute the fastest route between New York and Mountain View thousands of times faster than classic algorithms and close to those used in Google Maps", blablabla), but it turns out to be the least challenging course with only elementary introduction to the big topic. Perhaps the preparing time for this course is too short? I still miss the teaching style and assignment design of the first course of the series though. It would seem to be unwise to pay for the following courses if this continues on.

(FYI I was the among the first ones who take this series, and I have got 100% grade for each course ever since then.)

For those who passed previous two courses in this Specialization, this course will probably be easy to pass, even if you aim at 100%. I liked the programming assignments, did not like videos that much. Used to find myself watching different explanations from the Web. On the other hand, if you want to absorb all stuff presented here, this course is not too easy IMHO.

Всё хорошо, но есть задачи, где (например) формат ввода накладывает ограничения на максимальную эффективность. Например, нет никакого смысла писать DSU для Крускала, если только ввод данных займёт n^2

Last 2 weeks is a nightmare. Don't try to place 8 weeks in 2 anymore, please.

The lecture is pretty much scripted from the DPV book...

programming assignments were too difficult

Читается по Кормену. Книгу понять быстрее, чем прослушать весь курс. Материала в книге больше. Задачки полезные, можно брать отсюда

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