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Algorithmic Thinking (Part 1)

1913
students interested
Earn A Credential Part of the Fundamentals of Computing Specialization
• Provider Coursera
• Subject Algorithms and Data Structures
• \$ Cost Free Online Course (Audit)
• Session Upcoming
• Language English
• Certificate Paid Certificate Available
• Effort 7-10 hours a week
• Start Date
• Duration 4 weeks long

Taken this course? Share your experience with other students. Write review

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

Reviews for Coursera's Algorithmic Thinking (Part 1) 4.1 Based on 14 reviews

• 5 stars 57%
• 4 stars 21%
• 3 star 7%
• 2 star 7%
• 1 star 7%

Did you take this course? Share your experience with other students.

• 1
Alex I
4.0 2 years ago
completed this course.
Zach Z
5.0 3 years ago
by completed this course, spending 5 hours a week on it and found the course difficulty to be hard.
This course is the first part of a somewhat challenging conclusion to the specialization. The course is split into several two-week long components, during which you will have to complete a quiz and a machine-graded piece of code. Finally you will have to write additional code and answer somewhat challenging questions about the week's application. This includes preparing graphs of the runtimes of algorithms or their effectiveness.

As someone with a background in maths, I found this course an excellent way to apply my current knowledge to a programming environment. The applications…
4 people found
Csaba K
3.0 3 years ago
completed this course and found the course difficulty to be hard.
I took this course back when it was one course, more than a year ago so some things may have changed since.

This course is mainly built on understanding maths and not coding, unlike the previous courses in the specialization. The point of the course is understanding algorithmic efficiency of an algorithm.

The professor Luay Nakhleh is not my favourite teacher. I understood what he taught in his videos, however often that wasn't enough to finish the exam. I should have learned or relearned math which wasn't part of the course. They provided some external links but fo…
1 person found
Prose S
1.0 4 years ago
by completed this course, spending 7 hours a week on it and found the course difficulty to be medium.
Good course - comparable to the same professors' IIPP, but a little rougher round the edges - with a fairly challenging final project. One weakness was treatment of the relevant maths, which was a little sketchy.

Rated 1 (& not reviewed at greater length) because it was cynically switched to pay only - and the material split over two courses - with no warning. :(

10 people found
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