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Algorithmic Thinking (Part 1)

Algorithmic Thinking (Part 1)

This course is part of Fundamentals of Computing Specialization

Instructors: Luay Nakhleh

58,310 already enrolled

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4 modules

Gain insight into a topic and learn the fundamentals.

384 reviews

Intermediate level

Some related experience required

Flexible schedule

1 week at 10 hours a week

Learn at your own pace

93%

Most learners liked this course

4 modules

Gain insight into a topic and learn the fundamentals.

384 reviews

Intermediate level

Some related experience required

Flexible schedule

1 week at 10 hours a week

Learn at your own pace

93%

Most learners liked this course

Skills you'll gain

Tools you'll learn

Python Programming

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

2 assignments¹

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Taught in English

91% of learners achieved a positive career outcome

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This course is part of the Fundamentals of Computing Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
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There are 4 modules in this course

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".