Master string algorithms for efficient pattern matching and learn their applications in search engines and genomics.
Master string algorithms for efficient pattern matching and learn their applications in search engines and genomics.
This comprehensive course explores advanced string processing and pattern matching algorithms essential for modern computing applications. You'll learn about suffix trees, suffix arrays, and the Burrows-Wheeler Transform, understanding their implementation and practical applications. The course covers both theoretical foundations and real-world applications, from powering internet searches to analyzing genomic data in personalized medicine. Through hands-on programming assignments, you'll master key concepts in pattern matching and develop skills crucial for working with textual data and genomic sequences.
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Instructors:
English
English
What you'll learn
Master key concepts in pattern matching and suffix tree algorithms
Implement suffix arrays for efficient text processing
Understand the Burrows-Wheeler Transform and its applications
Apply string algorithms to bioinformatics problems
Develop efficient search algorithms for large text datasets
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, Exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 4 modules in this course
The course covers advanced string processing algorithms with applications in search engines and bioinformatics. Topics include suffix trees for efficient pattern matching, suffix arrays for text processing, and the Burrows-Wheeler Transform for compression. Students learn both theoretical foundations and practical implementations, exploring applications in internet search and genomic analysis. The curriculum emphasizes hands-on programming experience and real-world applications in computer science and personalized medicine.
Pattern Matching and Suffix Trees Fundamentals
Module 1
Advanced Suffix Trees
Module 2
Burrows-Wheeler Transform
Module 3
Suffix Arrays and Applications
Module 4
Fee Structure
Instructors

7 Courses
Distinguished Data Scientist and Algorithms Expert
Michael Levin serves as Chief Data Scientist at Yandex.Market, Russia's leading price comparison and online shopping service, while also maintaining a position as Lecturer in Computer Science at the National Research University Higher School of Economics since 2015. His academic excellence began at Moscow State University, where he earned his degree in mathematics and achieved both silver and bronze medals at the ACM ICPC World Finals as part of the university's team. His expertise in algorithms, machine learning, and data science has made him a prominent figure in both industry and education, leading to his role as an instructor for the University of California San Diego's online courses in algorithms and data structures, which have reached over 345,000 students worldwide. His teaching portfolio includes comprehensive courses in algorithmic design, graph algorithms, string processing, and advanced complexity theory, making him a influential figure in computer science education while maintaining his leadership role in one of Russia's largest tech companies.
Pioneering Bioinformatics Scholar and Computational Biology Innovator
Pavel Arkadevich Pevzner serves as the Ronald R. Taylor Professor of Computer Science at the University of California, San Diego, and director of the NIH Center for Computational Mass Spectrometry, where he has revolutionized the field of computational biology since 2000. After receiving his Ph.D. in mathematics and physics from the Moscow Institute of Physics and Technology, he completed postdoctoral work with Michael Waterman at USC, before establishing himself through positions at Penn State and USC. His groundbreaking research spans bioinformatics algorithms, genome rearrangements, DNA sequencing, and computational proteomics, leading to significant advances in genome assembly and antibiotics discovery. His academic excellence has been recognized through numerous prestigious honors, including the Howard Hughes Medical Institute Professorship (2006), ACM Fellowship (2010), ISCB Fellowship (2012), and the ACM Paris Kanellakis Theory and Practice Award (2018). As an educator, he has transformed bioinformatics education through innovative approaches, including the development of massive open online courses that have reached over half a million students, and authored influential textbooks including "Computational Molecular Biology: An Algorithmic Approach" and "Bioinformatics Algorithms: An Active Learning Approach"
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