À propos de ce cours : 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".
Enseigné par : Luay Nakhleh, Associate Professor
Computer Science; Biochemistry and Cell Biology
Enseigné par : Scott Rixner, Professor
Computer Science
Enseigné par : Joe Warren, Professor
Computer Science
| Informations de base | Cours 5 de 7 dans Fundamentals of Computing Specialization |
| Niveau | Intermediate |
| Engagement | 4 weeks of study, 7-10 hours/week |
| Langue | English, Sous-titres : Greek, Spanish |
| Comment réussir | Réussissez tous les devoirs notés pour terminer le cours. |
| Notes des utilisateurs |
- Vidéo: Class structure
- Vidéo: Pseudo-code
- Vidéo: The small-world problem
- Vidéo: Graphs and representation
- Vidéo: Paths and distances
- Vidéo: Brute force
- Vidéo: What Is algorithm efficiency?
- Vidéo: Measuring efficiency
- Vidéo: Efficiency of brute force distance
- Vidéo: Number of steps of brute force distance
- Reading: Class notes
- Reading: Coding notes
- Vidéo: Coding styles and standards - PoC
- Vidéo: Machine grading - PoC
- Vidéo: Plotting data - PoC
- Vidéo: Peer assessment - "We want a shrubbery!" - IIPP
- Reading: Project #1 Description
- LTI Item: Project Submission History
- Reading: Application #1 Description
- Reading: Application #1 Solution
- Vidéo: Asymptotics
- Vidéo: Illustrating "Big O"
- Vidéo: Illustrating BFS
- Vidéo: Queues and boundary cases
- Vidéo: Pseudocode
- Vidéo: BFS running time - loose analysis
- Vidéo: BFS running time - tighter analysis
- Vidéo: BFS-based distance distribution
- Reading: Project #2 Description
- Reading: Application #2 Description
- Reading: Application #2 Solution
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| Acheter le cours | |
|---|---|
| Accéder au contenu du cours | Disponible |
| Accéder au contenu noté | Disponible |
| Recevoir une Note Finale | Disponible |
| Obtenir un Certificat de Cours partageable | Disponible |
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.