Algorithmic Thinking (Part 1)

开始时间: 05/23/2020 持续时间: Unknown

所在平台: Coursera

课程类别: 计算机科学

大学或机构: CourseraNew



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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".

算法思维(第1部分):经验丰富的计算机科学家可以在超出任何特定编程语言的抽象水平上分析和解决计算问题。本课程分为两部分,以您在我们的“计算原理”课程中学到的原理为基础,旨在对学生进行数学概念和“算法思维”过程的培训,从而使他们能够为现实世界的计算构建更简单,更有效的解决方案问题。 在本课程的第1部分中,我们将研究算法效率的概念,并考虑将其应用于图论中的几个问题。作为课程的中心部分,学生将在Python中实现几种重要的图形算法,然后使用这些算法来分析两个大型的实际数据集。这些任务的主要重点是了解算法之间的交互以及这些算法正在分析的数据集的结构。 推荐的背景知识-学生应该习惯用Python编写中等大小(超过300行)的程序,并且对搜索,排序和递归有基本的了解。学生还应该具有扎实的数学背景,其中包括代数,微积分和熟悉“计算原理”中涵盖的数学概念。





Experienced Computer Scientists analyze and solve computational problems at a level of abstraction t


算法思维 算法 Python 编程基础 Python编程基础