Algorithms: Design and Analysis
Learn essential algorithms and data structures with Stanford's comprehensive course covering sorting, searching, and graph theory.
Learn essential algorithms and data structures with Stanford's comprehensive course covering sorting, searching, and graph theory.
Dive into the fundamentals of computer science with Stanford's rigorous algorithms course. This specialization provides a thorough understanding of algorithmic concepts and their practical applications. You'll master Big-O notation, sorting algorithms, divide-and-conquer strategies, randomized algorithms, data structures, and graph primitives. The course emphasizes conceptual understanding over implementation details, preparing you for technical interviews and advanced computer science discussions. Through hands-on programming assignments and quizzes, you'll gain practical experience implementing these concepts in your preferred programming language.
4.7
(13 ratings)
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What you'll learn
Understand and apply Big-O notation for algorithm analysis
Master sorting and searching algorithms with implementation techniques
Implement divide and conquer algorithms including master method and matrix multiplication
Design and analyze randomized algorithms such as QuickSort
Work with advanced data structures including heaps, balanced search trees and hash tables
Apply graph primitives for connectivity and shortest path problems
Skills you'll gain
This course includes:
PreRecorded video
6 multiple choice quizzes, 6 programming assignments, Final exam
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Limited Access access
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Module Description
This course offers a comprehensive introduction to fundamental algorithms and data structures, essential for computer science and software development. Topics include algorithmic complexity analysis using Big-O notation, sorting and searching algorithms, divide-and-conquer strategies, randomized algorithms like QuickSort, essential data structures including heaps and hash tables, and graph algorithms. The course emphasizes conceptual understanding while providing practical implementation experience through programming assignments. Students learn to analyze algorithm efficiency, implement various data structures, and apply algorithmic thinking to solve complex problems.
Fee Structure
Instructor
2 Courses
A Pioneering Computer Scientist and Game Theory Expert
Tim Roughgarden has established himself as a leading figure in theoretical computer science, particularly at the intersection of algorithms and economics. Born July 20, 1975, he earned his Ph.D. from Cornell University in 2002 under Éva Tardos's supervision, followed by a postdoc at UC Berkeley. He served as a professor in Stanford University's Computer Science department from 2004 to 2018 before joining Columbia University. His research focuses on algorithm design, game theory, and their applications to networks, auctions, and blockchains. His contributions have earned him numerous prestigious honors, including the Grace Murray Hopper Award, the Presidential Early Career Award for Scientists and Engineers, the Gödel Prize, and a Guggenheim Fellowship. As an educator, he has developed widely-used online courses in algorithms through Coursera and authored several influential textbooks including "Algorithms Illuminated" and "Twenty Lectures on Algorithmic Game Theory." Currently serving as a Professor at Columbia University and Head of Research at a16z crypto, he continues to advance the field through his work on the boundary of computer science and economics.
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4.7 course rating
13 ratings
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