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Coursera Algorithmic Thinking (Part 1)

Rice University via Coursera

  • Overview
  1. Coursera
    Platform:
    Coursera
    Provider:
    Rice University
    Length:
    4 weeks
    Effort:
    7-10 hours/week
    Language:
    English
    Credentials:
    Paid Certificate Available
    Part of:
    Fundamentals of Computing | Coursera
    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".

    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

    Taught by
    Luay Nakhleh, Scott Rixner and Joe Warren

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