Algorithmic Thinking (Part 1)

This course is part of Fundamentals of Computing Specialization

Instructors: Luay Nakhleh +2 more

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Skills you'll gain

  •   Network Analysis
  •   Computational Thinking
  •   Computer Programming
  •   Theoretical Computer Science
  •   Graph Theory
  •   Python Programming
  •   Analysis
  •   Algorithms
  •   Data Structures
  •   Data Analysis
  • There are 4 modules in this course

    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".

    Modules 1 - Project and Application

    Module 2 - Core Materials

    Module 2 - Project and Application

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