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Algorithmic Design and Techniques
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Algorithmic Design and Techniques

This course is part of Algorithms and Data Structures.

Course Cost

₹ 13,133

Intermediate

Skill Level

6 Weeks

Self-paced lessons

This comprehensive course teaches fundamental algorithmic techniques for solving computational problems efficiently. Students learn key concepts including sorting, searching, divide and conquer, greedy algorithms, and dynamic programming. The course emphasizes practical implementation through programming challenges, teaching students to design and implement efficient solutions that execute in under a second. Real-world applications are explored, from genomic studies to automated spelling corrections. The curriculum combines theoretical understanding with hands-on coding practice, ensuring students can both design algorithms and implement them effectively.

Daniel Kane

Daniel Kane

Alexander S. Kulikov

Alexander S. Kulikov

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English

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Daniel Kane

Daniel Kane

Alexander S. Kulikov

Alexander S. Kulikov

What you'll learn

  • Master essential algorithmic techniques for efficient problem solving

  • Implement sorting and searching algorithms with optimal performance

  • Apply divide and conquer strategies to break down complex problems

  • Develop efficient solutions using greedy algorithms

  • Understand and implement dynamic programming solutions

  • Optimize code for faster execution

  • Master debugging and testing techniques

  • Design algorithms for real-world computational challenges

Skills you'll gain

Algorithm Design
Data Structures
Dynamic Programming
Greedy Algorithms
Divide and Conquer
Sorting
Computational Problem Solving
Performance Optimization

This course includes:

PreRecorded video

Graded assignments, Exams

Access on Mobile, Tablet, Desktop

Limited Access access

Shareable certificate

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Top companies offer this course to their employees

Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.

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There are 5 modules in this course

This course provides a comprehensive introduction to algorithmic techniques essential for solving computational problems efficiently. Students learn fundamental approaches including greedy algorithms, divide and conquer strategies, and dynamic programming. The curriculum covers practical applications from database searching to genomic studies, with emphasis on implementing solutions that execute efficiently. Through hands-on programming challenges, students learn to design, test, and debug their implementations. The course is structured to build both theoretical understanding and practical coding skills, preparing students for real-world algorithm design and implementation challenges.

Welcome

Module 1

Introduction

Module 2

Greedy Algorithms

Module 3

Divide-and-Conquer

Module 4

Dynamic Programming

Module 5

Fee Structure

Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Algorithms and Data Structures

Payment options

Financial Aid

Reviews

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Faculties

These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.

Daniel Kane
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8

Courses

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Daniel Mertz Kane serves as a full professor with a joint appointment in Mathematics and Computer Science at the University of California, San Diego, where he has established himself as a leading researcher in theoretical computer science, combinatorics, and number theory. Born in 1986 to academic parents in Madison, Wisconsin, his extraordinary mathematical talent emerged early, mastering K-9 mathematics by third grade and conducting university-level research under Ken Ono while still in high school. His academic achievements include two gold medals in the International Mathematical Olympiad (2002, 2003), being one of only eight people in history to become a four-time Putnam Fellow, and winning the 2007 Morgan Prize. After earning dual bachelor's degrees from MIT in mathematics with computer science and physics (2007), he completed his Ph.D. at Harvard under Barry Mazur in 2011. His research contributions span multiple areas, including groundbreaking work in computational statistics, Boolean functions, and machine learning, earning him numerous awards including the IBM Pat Goldberg Memorial and PODS best paper awards. He recently co-authored a book on robust statistics with Ilias Diakonikolas, to be published by Cambridge University Press
Alexander S. Kulikov
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13

Courses

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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.
Michael Levin
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7

Courses

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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.
Pavel Pevzner
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16

Courses

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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"
Algorithmic Design and Techniques

Intermediate

Skill Level

6 Weeks

Self-paced lessons

Course Cost

₹ 13,133

Completion

CERTIFICATE

Frequently asked Questions

Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.