Algorithmic Thinking (Part 1)
Learn algorithmic efficiency and graph theory: Analyze real-world data using Python implementations of key algorithms.
Learn algorithmic efficiency and graph theory: Analyze real-world data using Python implementations of key algorithms.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Fundamentals of Computing Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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What you'll learn
Apply algorithmic thinking to solve computational problems
Implement and analyze graph algorithms in Python
Evaluate algorithm efficiency using asymptotic analysis
Analyze real-world networks using graph theory
Develop efficient solutions for complex computational tasks
Skills you'll gain
This course includes:
4.78 Hours PreRecorded video
2 assignments
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There are 4 modules in this course
This comprehensive course focuses on developing algorithmic thinking skills through graph theory and algorithm analysis. Students learn to implement and analyze graph algorithms in Python, working with real-world datasets including citation graphs and computer networks. The curriculum covers fundamental concepts like algorithmic efficiency, asymptotic analysis, breadth-first search, and connected components, emphasizing the interaction between algorithms and data structures.
Module 1 - Core Materials
Module 1 · 3 Hours to complete
Modules 1 - Project and Application
Module 2 · 3 Hours to complete
Module 2 - Core Materials
Module 3 · 2 Hours to complete
Module 2 - Project and Application
Module 4 · 2 Hours to complete
Fee Structure
Instructors
Professor and Researcher
Scott Rixner is a Professor of Computer Science at Rice University, where his research primarily focuses on virtualization, operating systems, and computer architecture. He is particularly interested in memory systems and networking, exploring the intersection of hardware and software to enhance system performance. Rixner has developed Python interpreters for various platforms, including embedded systems and web browsers, and holds 11 patents related to his work, which has been integrated into several open-source projects.In addition to his research, Rixner plays a significant role in curriculum development at Rice University, having chaired committees for both the Department of Computer Science and the School of Engineering. He is also involved in leading the MCS@Rice and MDS@Rice online degree programs. Rixner earned his Ph.D. in Electrical Engineering from MIT in 2001, following a Master’s and Bachelor’s degree in Computer Science and Electrical Engineering from the same institution in 1995.
Innovator in Computer Graphics and Game Design
Joe Warren is a Professor in the Department of Computer Science at Rice University, where he specializes in computer graphics and geometric modeling. He has made significant contributions to the field, authoring the book Subdivision Methods for Geometric Design and publishing extensively on various topics within computer graphics. Warren has a strong passion for computer gaming, both as a player and an educator; he has taught courses on game creation and design for over a decade, collaborating with industry professionals in Houston. An alumnus of Rice University, he completed his undergraduate studies there before earning his Ph.D. from Cornell University in 1986. Since then, he has been a dedicated faculty member at Rice, serving as the Chair of the Department from 2008 to 2013.
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4.6 course rating
374 ratings
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