Об этом курсе: 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".
Преподаватели: Luay Nakhleh, Associate Professor
Computer Science; Biochemistry and Cell Biology
Преподаватели: Scott Rixner, Professor
Computer Science
Преподаватели: Joe Warren, Professor
Computer Science
| Основные сведения | Course 5 of 7 in the Fundamentals of Computing Specialization |
| Уровень | Intermediate |
| Выполнение | 4 weeks of study, 7-10 hours/week |
| Язык | English, Субтитры: Greek, Spanish |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
| Оценки пользователей |
- Видео: Class structure
- Видео: Pseudo-code
- Видео: The small-world problem
- Видео: Graphs and representation
- Видео: Paths and distances
- Видео: Brute force
- Видео: What Is algorithm efficiency?
- Видео: Measuring efficiency
- Видео: Efficiency of brute force distance
- Видео: Number of steps of brute force distance
- Reading: Class notes
- Reading: Coding notes
- Видео: Coding styles and standards - PoC
- Видео: Machine grading - PoC
- Видео: Plotting data - PoC
- Видео: Peer assessment - "We want a shrubbery!" - IIPP
- Reading: Project #1 Description
- LTI Item: Project Submission History
- Reading: Application #1 Description
- Reading: Application #1 Solution
- Видео: Asymptotics
- Видео: Illustrating "Big O"
- Видео: Illustrating BFS
- Видео: Queues and boundary cases
- Видео: Pseudocode
- Видео: BFS running time - loose analysis
- Видео: BFS running time - tighter analysis
- Видео: BFS-based distance distribution
- Reading: Project #2 Description
- Reading: Application #2 Description
- Reading: Application #2 Solution
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| Получить доступ к материалам курса | Доступен |
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| Получить итоговую оценку | Доступен |
| Earn a shareable Course Certificate | Доступен |
A step up in difficulty from the previous modules in this specialisation.
Another excellent course in the specialization from Rice. Really interesting algorithms that were fun, and non-trivial, to implement. The plotting and comparison exercises are helpful for gaining insight.
This is where computer science truly starts, without the excessive preliminary math that usually scares most people away. Great course!
very thoughtful course!
not easy by any means, but for sure learned a lot from the hard experience.