Algorithmic Toolbox
Master essential algorithmic techniques with hands-on programming challenges. Learn sorting, searching, and optimization methods.
Master essential algorithmic techniques with hands-on programming challenges. Learn sorting, searching, and optimization methods.
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 Data Structures and Algorithms 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
Design efficient algorithms for complex computational problems
Implement sorting and searching algorithms effectively
Master divide-and-conquer problem-solving techniques
Apply greedy algorithms to optimization problems
Understand dynamic programming principles
Skills you'll gain
This course includes:
6.4 Hours PreRecorded video
25 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 6 modules in this course
This comprehensive course covers fundamental algorithmic techniques for solving computational problems. Students learn essential methods including sorting, searching, divide and conquer, greedy algorithms, and dynamic programming. The curriculum combines theoretical understanding with practical implementation through programming challenges, teaching efficient problem-solving approaches and algorithm optimization. Each module includes hands-on assignments to reinforce concepts and develop coding skills.
Programming Challenges
Module 1 · 5 Hours to complete
Algorithmic Warm-up
Module 2 · 5 Hours to complete
Greedy Algorithms
Module 3 · 8 Hours to complete
Divide-and-Conquer
Module 4 · 8 Hours to complete
Dynamic Programming 1
Module 5 · 7 Hours to complete
Dynamic Programming 2
Module 6 · 4 Hours to complete
Fee Structure
Instructors
13 Courses
Distinguished Computer Scientist and Algorithms Expert
Alexander S. Kulikov serves as a visiting professor at the University of California, San Diego, and a leading research fellow at the Steklov Institute of Mathematics in St. Petersburg. His academic journey includes earning his Ph.D. in 2009 and Dr.Sci. in 2017 from the St. Petersburg Department of Steklov Institute of Mathematics. His research focuses on algorithms for NP-hard problems and circuit complexity, with significant contributions to computational complexity theory and algorithm design. He has authored several influential educational resources, including "Learning Algorithms Through Programming and Puzzle Solving" and co-created major online courses on platforms like Coursera and edX. His teaching experience spans more than eight years, during which he has developed innovative approaches to algorithms education. Currently at JetBrains as a researcher, he continues to advance the field through his work on algorithmic problem-solving and computational complexity, while maintaining his academic connections through his visiting professorship at UCSD and research position at Steklov Institute.
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.
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