Back to Algorithmic Thinking (Part 1)

4.7

232 ratings

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44 reviews

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"....

By OT

•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.

By MN

•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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43 Reviews

By Olga Timofeeva

•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.

By Artur Parkhisenko

•Sep 21, 2018

Some parts was hard and some not because of my own experience, in general very good course and only hard problems forces us to think.

By Jayadev Haddadi

•Aug 22, 2018

lectures are a bit on the slow side... not straight to the point and a bit repetative..

bfs we have already done in this spezialization.

but homework/project/applications are excellent!

makes up for the rest!

Thank you!

By 熊华东

•Aug 21, 2018

Very great course.At first i under estimate this course, but through this course i became stuggling in project and assignment. The depth and breadth of the course is wonderful.Maybe sometimes got stucked, but finally always found It's worthwhile spending hours on this course. This course drove me thinking and thinking. It should take some time to review. Sometimes i don't know how i finished

By Vern Kerchberger

•Jul 26, 2018

Course and assignments were very well thought out and informative.

By Tairan Yang

•May 13, 2018

very thoughtful course!

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

By Aaron Moran

•Mar 22, 2018

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

By Julian Ong

•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.

By Márton András Nemeskéri

•Mar 08, 2018

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

By Andrew Foord

•Mar 05, 2018

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

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