- Platform
- Coursera
- Provider
- Rice University
- Effort
- 7-10 hours/week
- Length
- 4 weeks
- Language
- English
- Credentials
- Paid Certificate Available
- Part of
- Course Link
Overview
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".
Taught by
Luay Nakhleh, Scott Rixner and Joe Warren
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".
Syllabus
Module 1 - Core Materials
What is Algorithmic Thinking?, class structure, graphs, brute-force algorithms
Modules 1 - Project and Application
Graph representations, plotting, analysis of citation graphs
Module 2 - Core Materials
Asymptotic analysis, "big O" notation, pseudocode, breadth-first search
Module 2 - Project and Application
Connected components, graph resilience, and analysis of computer networks
Module 1 - Core Materials
What is Algorithmic Thinking?, class structure, graphs, brute-force algorithms
Modules 1 - Project and Application
Graph representations, plotting, analysis of citation graphs
Module 2 - Core Materials
Asymptotic analysis, "big O" notation, pseudocode, breadth-first search
Module 2 - Project and Application
Connected components, graph resilience, and analysis of computer networks
Taught by
Luay Nakhleh, Scott Rixner and Joe Warren