About 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".
Taught by: Luay Nakhleh, Associate Professor
Computer Science; Biochemistry and Cell Biology
Taught by: Scott Rixner, Professor
Computer Science
Taught by: Joe Warren, Professor
Computer Science
| Basic Info | Course 5 of 7 in the Fundamentals of Computing Specialization |
| Level | Intermediate |
| Commitment | 4 weeks of study, 7-10 hours/week |
| Language | English, Subtitles: Spanish |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
- Video: What is Algorithmic Thinking?
- Video: Class structure
- Video: Pseudo-code
- Video: The small-world problem
- Video: Graphs and representation
- Video: Paths and distances
- Video: Brute force
- Video: What Is algorithm efficiency?
- Video: Measuring efficiency
- Video: Efficiency of brute force distance
- Video: Number of steps of brute force distance
- Reading: Class notes
- Reading: Coding notes
- Video: Coding styles and standards - PoC
- Video: Machine grading - PoC
- Video: Plotting data - PoC
- Video: Peer assessment - "We want a shrubbery!" - IIPP
- Reading: Project #1 Description
- LTI Item: Project Submission History
- Reading: Application #1 Description
- Reading: Application #1 Solution
- Video: Orders of growth
- Video: Asymptotics
- Video: Illustrating "Big O"
- Video: Illustrating BFS
- Video: Queues and boundary cases
- Video: Pseudocode
- Video: BFS running time - loose analysis
- Video: BFS running time - tighter analysis
- Video: BFS-based distance distribution
- Reading: Project #2 Description
- Reading: Application #2 Description
- Reading: Application #2 Solution
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| Audit | Purchase Course | |
|---|---|---|
| Access to course materials | Available | Available |
| Access to graded materials | Not available | Available |
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| Earn a shareable Course Certificate | Not available | Available |
thanks a lot
Excellent course.
Algorithmic Thinking is significantly more difficult than Principles of Computing. I felt there was a big step up, and I had to do a lot of research and reading outside of the course just to keep up. The listed 7-10 hrs/wk is too low IMO. I spent 14.5 hrs/wk on average (for parts 1 and 2). Maybe if you're already really good at math and experienced with matplotlib you'll take less time.
Anyway, the material is great, and gives you a good glimpse of how difficult problems can be solved efficiently. It's one thing to be shown an algorithm and how it was created, but it's entirely different thing to do it yourself from scratch. But at least I'm now aware of the strategy and can practice it. I learned a lot from these courses (the whole specialty), and am miles ahead of where I was when I started.
Thanks )
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RK
The project-based course structure works really well for the material. This was a great course!