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Graph Algorithms for Network Analysis
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Graph Algorithms for Network Analysis

This course is part of Algorithms and Data Structures.

Course Cost

₹ 12,343

Intermediate

Skill Level

6 Weeks

Self-paced lessons

This comprehensive course explores graph algorithms and their real-world applications in navigation systems, social networks, and computer networks. Students learn to implement various graph algorithms, including shortest path algorithms (Breadth-First Search, Dijkstra's, and Bellman-Ford), minimum spanning trees (Kruskal's and Prim's algorithms), and network flow analysis. The course combines theoretical foundations with practical programming assignments, allowing students to solve real-world problems like optimizing road networks, analyzing social networks, and planning computer network infrastructure. Through hands-on projects, participants develop efficient algorithmic solutions for complex network-based challenges.

Daniel Kane

Daniel Kane

Alexander S. Kulikov

Alexander S. Kulikov

English

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English

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Daniel Kane

Daniel Kane

Alexander S. Kulikov

Alexander S. Kulikov

What you'll learn

  • Master graph exploration and decomposition techniques

  • Implement efficient shortest path algorithms like Dijkstra's and Bellman-Ford

  • Design minimum spanning tree algorithms for network optimization

  • Apply network flow algorithms to solve transportation problems

  • Analyze real-world networks using graph algorithms

Skills you'll gain

Graph Theory
Algorithms
Data Structures
Network Analysis
Shortest Path Algorithms
Minimum Spanning Trees
Connected Components
Breadth-First Search
Network Flow
Computer Networks

This course includes:

PreRecorded video

Graded assignments, Exams

Access on Mobile, Tablet, Desktop

Limited Access access

Shareable certificate

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Certificate
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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

The course covers graph algorithms essential for analyzing and optimizing networks. Students learn graph representation and decomposition, shortest path algorithms, minimum spanning trees, and network flows. The curriculum includes practical applications in road networks, social networks, and computer networks, with programming assignments that reinforce theoretical concepts through real-world problem-solving.

Graph Decomposition - Part 1

Module 1

Graph Decomposition - Part 2

Module 2

Shortest Paths in Graphs

Module 3

Minimum Spanning Trees

Module 4

Flows in Networks

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

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.
Graph Algorithms for Network Analysis

Intermediate

Skill Level

6 Weeks

Self-paced lessons

Course Cost

₹ 12,343

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.