Back to Algorithmic Thinking (Part 1)
Learner Reviews & Feedback for Algorithmic Thinking (Part 1) by Rice University
376 ratings
About the Course
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".
Top reviews
OT
Sep 28, 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.
MR
Sep 16, 2019
The class is very useful, I already see the improvement in the codes that I write. And the assignments are very well-designed and truly helpful.
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