Learners will practice and master the fundamentals of algorithms through several types of assessments. Every week, there is a multiple choice quiz to test your understanding of the most important concepts. There are also weekly programming assignments, where you implement one of the algorithms covered in lecture in a programming language of your choosing. Each course concludes with a multiple-choice final exam.
Learn To Think Like A Computer Scientist
Master the fundamentals of the design and analysis of algorithms.
About This Specialization
Algorithms are the heart of computer science, and the subject has countless practical applications as well as intellectual depth. This specialization is an introduction to algorithms for learners with at least a little programming experience. The specialization is rigorous but emphasizes the big picture and conceptual understanding over low-level implementation and mathematical details. After completing this specialization, you will be well-positioned to ace your technical interviews and speak fluently about algorithms with other programmers and computer scientists.
About the instructor: Tim Roughgarden has been a professor in the Computer Science Department at Stanford University since 2004. He has taught and published extensively on the subject of algorithms and their applications.
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4 courses
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Projects
Designed to help you practice and apply the skills you learn.
Certificates
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Projects Overview
Courses
- Intermediate Specialization.
- Some related experience required.
COURSE 1
Divide and Conquer, Sorting and Searching, and Randomized Algorithms
Current session: Sep 25 — Oct 30.- Commitment
- 4 weeks of study, 4-8 hours/week
- Subtitles
- English
About the Course
The primary topics in this part of the specialization are: asymptotic ("Big-oh") notation, sorting and searching, divide and conquer (master method, integer and matrix multiplication, closest pair), and randomized algorithms (QuickSort, contraction algorithm for min cuts).Show or hide details about course Divide and Conquer, Sorting and Searching, and Randomized AlgorithmsCOURSE 2
Graph Search, Shortest Paths, and Data Structures
Current session: Sep 25 — Oct 30.- Commitment
- 4 weeks of study, 4-8 hours/week
- Subtitles
- English
About the Course
The primary topics in this part of the specialization are: data structures (heaps, balanced search trees, hash tables, bloom filters), graph primitives (applications of breadth-first and depth-first search, connectivity, shortest paths), and their applications (ranging from deduplication to social network analysis).Show or hide details about course Graph Search, Shortest Paths, and Data StructuresCOURSE 3
Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming
Current session: Sep 25 — Oct 30.- Commitment
- 4 weeks of study, 4-8 hours/week
- Subtitles
- English
About the Course
The primary topics in this part of the specialization are: greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes) and dynamic programming (knapsack, sequence alignment, optimal search trees).Show or hide details about course Greedy Algorithms, Minimum Spanning Trees, and Dynamic ProgrammingCOURSE 4
Shortest Paths Revisited, NP-Complete Problems and What To Do About Them
Upcoming session: Oct 2 — Nov 6.- Commitment
- 4 weeks of study, 4-8 hours/week
- Subtitles
- English
About the Course
The primary topics in this part of the specialization are: shortest paths (Bellman-Ford, Floyd-Warshall, Johnson), NP-completeness and what it means for the algorithm designer, and strategies for coping with computationally intractable problems (analysis of heuristics, local search).Show or hide details about course Shortest Paths Revisited, NP-Complete Problems and What To Do About Them
Creators
Tim Roughgarden
Professor
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