Algorithmic Thinking (Part 1)(으)로 돌아가기

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54개의 리뷰

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.

Sep 17, 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.

필터링 기준:

교육 기관: Deepak V

•Jun 19, 2019

It was a good learning experience

교육 기관: Wynand

•Jan 11, 2018

Not quite the same level of energy presents in IIPP and Computing Principles. Also did not like the peer review projects, too messy.

교육 기관: Qi D

•Feb 18, 2017

coursea does not allow me to quit the class. Also, I cannot do the homework or watch video at my own pace.