Retour à Algorithmic Thinking (Part 1)

4.7

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52 avis

Experienced Computer Scientists analyze and solve computational problems at a level of abstraction that is beyond that of any particular programming language. This two-part course builds on the principles that you learned in our Principles of Computing course and is designed to train students in the mathematical concepts and process of "Algorithmic Thinking", allowing them to build simpler, more efficient solutions to real-world computational problems.
In part 1 of this course, we will study the notion of algorithmic efficiency and consider its application to several problems from graph theory. As the central part of the course, students will implement several important graph algorithms in Python and then use these algorithms to analyze two large real-world data sets. The main focus of these tasks is to understand interaction between the algorithms and the structure of the data sets being analyzed by these algorithms.
Recommended Background - Students should be comfortable writing intermediate size (300+ line) programs in Python and have a basic understanding of searching, sorting, and recursion. Students should also have a solid math background that includes algebra, pre-calculus and a familiarity with the math concepts covered in "Principles of Computing"....

Sep 29, 2018

very educational. I've learnt not only about graph theory but also how to use matplotlib and timeit libraries. The assignments were quite challengeable but rewarding.

Mar 08, 2018

This is where computer science truly starts, without the excessive preliminary math that usually scares most people away. Great course!

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par Yair B

•Apr 30, 2016

The lectures will not get you near the understanding you'll need to complete the assignments. though the resources will.

The application is great, and hard so you'll actually have to understand the material.

You have been warned, this course is very different to the other courses in the specialization!

par Rita I G

•Feb 07, 2019

Good course!!

par Max B

•Mar 21, 2019

Oh man, I hade so much fun in this course! The lectures and material is very good, and everything is wrapped up in much fun projects and applications where you will learn a lot. I especially enjoyed the more mathematical approach in AT compared to PoC and IIPP, and also the general class structure! Highly recommended!

par Rudy H

•Mar 06, 2019

Prof. Luay is an excellent instructor, his approach is very well thought of and his explanation on the subject is very constructive and clear which is vital to the understanding of such subject. I am learning a ton and very thankful to all that involved.

par Gerardo G

•Mar 09, 2019

Great course, please offer an oline program to obtain an Rice university grade in science computer.

par Y A

•Oct 11, 2017

This is Wonderful and simpler explained course that is detailed with 'learner's requirement'.

par Tairan Y

•May 13, 2018

very thoughtful course!

not easy by any means, but for sure learned a lot from the hard experience.

par Márton A N

•Mar 08, 2018

This is where computer science truly starts, without the excessive preliminary math that usually scares most people away. Great course!

par Yu(Helena) H

•Dec 02, 2016

Great class, very well constructed. Professors are very knowledgeable and the course materials are well designed. I started from the very beginning of the class knowing very little about python and algorithm, by this class I'm pretty comfortable coding in python. And I got to apply what I've learned in my interview for jobs. Definitely a great class! Learned a lot, and still applying it in my job.

par Jaehwi C

•Dec 11, 2017

The best course to study computer science and algorithm for beginner!

par Andrew F

•Mar 05, 2018

Another fantastic course from the team at Rice - thank you!

par Martin W

•Feb 19, 2017

great course

par Edwin R

•Nov 12, 2017

The course content is well structured and the instructors' explanation is clear and concise!

par Gundala S R

•Jun 24, 2016

One of the best course offered by coursera, helps you to develop very strong basics if new,.

par emmanouil k

•Jul 10, 2016

optimization and fragmentation..algos arithmos olokliroma..fractal resilience..

par Isuru

•Oct 12, 2016

A course I enjoy very much!

par Siwei L

•Dec 23, 2017

Very helpful course!!

par Arthur-Lance

•Aug 15, 2017

thanks a lot

par Ian B

•Jul 26, 2017

Excellent course.

Algorithmic Thinking is significantly more difficult than Principles of Computing. I felt there was a big step up, and I had to do a lot of research and reading outside of the course just to keep up. The listed 7-10 hrs/wk is too low IMO. I spent 14.5 hrs/wk on average (for parts 1 and 2). Maybe if you're already really good at math and experienced with matplotlib you'll take less time.

Anyway, the material is great, and gives you a good glimpse of how difficult problems can be solved efficiently. It's one thing to be shown an algorithm and how it was created, but it's entirely different thing to do it yourself from scratch. But at least I'm now aware of the strategy and can practice it. I learned a lot from these courses (the whole specialty), and am miles ahead of where I was when I started.

par Zou S

•Oct 16, 2017

Very impressive and interesting. Graph theory is really elegant representation of the computer network.

par Albert C G

•Dec 02, 2017

Great Class - Truly makes you think

par Nathaniel B

•Oct 09, 2017

Excellent course!

par Aaron M

•Mar 22, 2018

A step up in difficulty from the previous modules in this specialisation.

par Todd R

•Aug 29, 2016

It takes some serious dedication to understand and apply the material if math and computation are foreign to you, like they were for me. But if you apply that dedication, the material is accessible and the feeling of accomplishment is tremendous. Great class/teaching.

par Julian O

•Mar 21, 2018

Another excellent course in the specialization from Rice. Really interesting algorithms that were fun, and non-trivial, to implement. The plotting and comparison exercises are helpful for gaining insight.

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