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Janani Ravi
This course focuses on how to represent a graph using three common classes of graph algorithms - the topological sort to sort vertices by precedence relationships, the shortest path algorithm, and finally the spanning tree algorithms.
Read more
This course focuses on how to represent a graph using three common classes of graph algorithms - the topological sort to sort vertices by precedence relationships, the shortest path algorithm, and finally the spanning tree algorithms.
Read more
This course focuses on how to represent a graph using three common classes of graph algorithms - the topological sort to sort vertices by precedence relationships, the shortest path algorithm, and finally the spanning tree algorithms.
A graph is the underlying data structure behind social networks, maps, routing networks and logistics, and a whole range of applications that you commonly use today. In this course, Working with Graph Algorithms in Python, you'll learn different kinds of graphs, their use cases, and how they're represented in code. First, you'll dive into understanding the pros and cons of adjacency matrices, adjacency lists, adjacency sets, and know when you would choose one data structure over another. Next, you'll explore common graph algorithms, such as the topological sort, used to model dependencies in tasks, build components, and manage projects. Additionally, you'll cover how to find the shortest path in a graph, the core algorithm for mapping technologies. Lastly, you'll be introduced to spanning tree algorithms, which are used to find a path and covers all nodes with minimum cost, the fundamental algorithm behind figuring flight paths, and bus routes. By the end of this course, you'll have a better understanding of these principles and the necessary skills to implement them into simple, easy to follow Python code.
Register for this course and see more details by visiting:
OpenCourser.com/course/4lrims/working
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What's inside
Syllabus
Course Overview
Introducing the Graph Data Structure
Understanding Topological Sort
Working with Shortest Path Algorithms
Read more
Syllabus
Course Overview
Introducing the Graph Data Structure
Understanding Topological Sort
Working with Shortest Path Algorithms
Read more
Working with Spanning Tree Algorithms
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores multiple algorithms for graphs, giving learners a deeper understanding and more utility for them
Taught by Janani Ravi, who has years of experience working with graphs
Uses Python, which is commonly used for working with data structures in the industry
Builds a strong foundation for learners in Python, data structures, and graph theory
Provides real-world examples of graph applications such as maps and social networks
Gives learners the skills and knowledge to implement graph algorithms in code
Save this course
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Working with Graph Algorithms in Python with these
activities:
Review basic graph algorithms
Show steps
Understand the core concepts of graph algorithms to prepare for the advanced topics covered in this course.
Browse courses on
Graph Algorithms
Show steps
-
Review topological sort algorithm
-
Review shortest path algorithms
-
Review spanning tree algorithms
Discuss graph algorithms with peers
Show steps
Engage with fellow students to share knowledge, clarify concepts, and provide support in learning graph algorithms.
Show steps
-
Join an online or in-person study group or discussion forum.
-
Actively participate in discussions and ask questions.
-
Collaborate on projects or problem-solving exercises.
Python implementation of graph algorithms
Show steps
Develop proficiency in implementing graph algorithms using Python, which will be the primary language used in this course.
Show steps
-
Find online tutorials on Python implementation of graph algorithms.
-
Follow the tutorials and practice implementing the algorithms yourself.
-
Test your implementations with sample datasets.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Implement graph algorithms in Python
Show steps
Solidify your understanding of the algorithms by implementing them in Python and solving practice problems.
Show steps
-
Solve practice problems related to topological sort.
-
Solve practice problems related to shortest path algorithms.
-
Solve practice problems related to spanning tree algorithms.
Attend a workshop on graph algorithms
Show steps
Immerse yourself in a workshop tailored to graph algorithms, where you can learn from experts and network with other professionals.
Show steps
-
Find relevant workshops offered by universities, industry organizations, or online platforms.
-
Register and attend the workshop.
-
Participate actively and engage with the instructors and attendees.
Write a blog post or article on graph algorithms
Show steps
Deepen your understanding of the concepts by explaining them to others in a well-written and informative blog post or article.
Show steps
-
Choose a specific topic related to graph algorithms.
-
Research and gather information on the topic.
-
Write a clear and concise blog post or article that explains the topic.
-
Publish your writing and share it with others.
Contribute to an open-source graph library
Show steps
Gain practical experience by contributing to real-world graph-related projects and collaborating with a community of developers.
Show steps
-
Find an open-source graph library or project that interests you.
-
Review the project's documentation and codebase.
-
Identify an area where you can contribute, such as bug fixes, feature enhancements, or documentation improvements.
-
Submit your contributions and engage with the project maintainers.
Review basic graph algorithms
Show steps
Understand the core concepts of graph algorithms to prepare for the advanced topics covered in this course.
Browse courses on
Graph Algorithms
Show steps
Show steps
Show steps
- Review topological sort algorithm
- Review shortest path algorithms
- Review spanning tree algorithms
Discuss graph algorithms with peers
Show steps
Engage with fellow students to share knowledge, clarify concepts, and provide support in learning graph algorithms.
Show steps
Show steps
Show steps
- Join an online or in-person study group or discussion forum.
- Actively participate in discussions and ask questions.
- Collaborate on projects or problem-solving exercises.
Python implementation of graph algorithms
Show steps
Develop proficiency in implementing graph algorithms using Python, which will be the primary language used in this course.
Show steps
Show steps
Show steps
- Find online tutorials on Python implementation of graph algorithms.
- Follow the tutorials and practice implementing the algorithms yourself.
- Test your implementations with sample datasets.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Implement graph algorithms in Python
Show steps
Solidify your understanding of the algorithms by implementing them in Python and solving practice problems.
Show steps
Show steps
Show steps
- Solve practice problems related to topological sort.
- Solve practice problems related to shortest path algorithms.
- Solve practice problems related to spanning tree algorithms.
Attend a workshop on graph algorithms
Show steps
Immerse yourself in a workshop tailored to graph algorithms, where you can learn from experts and network with other professionals.
Show steps
Show steps
Show steps
- Find relevant workshops offered by universities, industry organizations, or online platforms.
- Register and attend the workshop.
- Participate actively and engage with the instructors and attendees.
Write a blog post or article on graph algorithms
Show steps
Deepen your understanding of the concepts by explaining them to others in a well-written and informative blog post or article.
Show steps
Show steps
Show steps
- Choose a specific topic related to graph algorithms.
- Research and gather information on the topic.
- Write a clear and concise blog post or article that explains the topic.
- Publish your writing and share it with others.
Contribute to an open-source graph library
Show steps
Gain practical experience by contributing to real-world graph-related projects and collaborating with a community of developers.
Show steps
Show steps
Show steps
- Find an open-source graph library or project that interests you.
- Review the project's documentation and codebase.
- Identify an area where you can contribute, such as bug fixes, feature enhancements, or documentation improvements.
- Submit your contributions and engage with the project maintainers.
Career center
Learners who complete Working with Graph Algorithms in Python will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts interpret complex data to derive meaningful insights, utilizing various tools and techniques to analyze and visualize data. This course can be a valuable resource for those aspiring to become Data Analysts, providing a solid foundation in graph algorithms, which are extensively used in data analysis for tasks such as identifying patterns and relationships within data, building predictive models, and optimizing resource allocation. By understanding and applying these algorithms, Data Analysts can enhance the accuracy and efficiency of their data analysis, leading to more informed decision-making.
Software Engineer
Software Engineer
Software Engineers design, develop, test, and maintain software systems. A strong understanding of graph algorithms is highly sought after in this field, as graphs are widely used to model complex relationships and data structures in software applications. This course can help Software Engineers build a solid foundation in graph algorithms, enabling them to develop more efficient and effective software solutions. By mastering these algorithms, Software Engineers can enhance their problem-solving and coding skills, becoming more valuable assets to their teams.
Data Scientist
Data Scientist
Data Scientists use scientific methods and algorithms to extract knowledge and insights from data. A deep understanding of graph algorithms is essential for Data Scientists, as they are commonly used for tasks such as modeling complex networks, identifying influential nodes, and clustering data into meaningful groups. This course provides a comprehensive introduction to graph algorithms, empowering Data Scientists to leverage these techniques in their work. By mastering these algorithms, Data Scientists can develop more accurate and sophisticated data-driven models, leading to transformative insights and informed decision-making.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical modeling to analyze financial data and make investment decisions. Graph algorithms play a crucial role in this field, as they are used to model complex financial networks, identify patterns and trends, and optimize investment portfolios. This course provides a solid foundation in graph algorithms, equipping Quantitative Analysts with the necessary skills to excel in their roles. By mastering these algorithms, Quantitative Analysts can develop more sophisticated and data-driven investment strategies, leading to improved financial outcomes.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex business problems. Graph algorithms are widely used in this field for tasks such as optimizing supply chains, scheduling resources, and designing transportation networks. This course provides a comprehensive introduction to graph algorithms, empowering Operations Research Analysts to leverage these techniques in their work. By mastering these algorithms, Operations Research Analysts can develop more efficient and effective solutions to real-world problems, leading to improved operational performance and cost savings.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning models. Graph algorithms are increasingly used in machine learning for tasks such as modeling relationships between data points, identifying influential nodes, and clustering data into meaningful groups. This course provides a solid foundation in graph algorithms, enabling Machine Learning Engineers to leverage these techniques in their work. By mastering these algorithms, Machine Learning Engineers can develop more accurate and sophisticated machine learning models, leading to improved performance and decision-making.
Computer Scientist
Computer Scientist
Computer Scientists research and develop new computing technologies and applications. Graph algorithms are fundamental to computer science, as they are used in various areas such as network analysis, data mining, and artificial intelligence. This course provides a deep dive into graph algorithms, equipping Computer Scientists with the necessary skills to advance their research and contribute to the field. By mastering these algorithms, Computer Scientists can develop innovative solutions to complex computational problems, leading to advancements in technology and scientific discovery.
Network Engineer
Network Engineer
Network Engineers design, build, and maintain computer networks. Graph algorithms are essential in this field, as they are used for tasks such as network optimization, routing, and traffic management. This course provides a comprehensive introduction to graph algorithms, empowering Network Engineers to leverage these techniques in their work. By mastering these algorithms, Network Engineers can design more efficient and reliable networks, ensuring seamless communication and data transfer.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines and infrastructure. Graph algorithms are increasingly used in this field for tasks such as data integration, data cleaning, and data transformation. This course provides a solid foundation in graph algorithms, enabling Data Engineers to leverage these techniques in their work. By mastering these algorithms, Data Engineers can develop more efficient and effective data pipelines, ensuring high-quality data for downstream analysis and decision-making.
Business Analyst
Business Analyst
Business Analysts use data and analytical techniques to solve business problems and improve organizational performance. Graph algorithms are becoming increasingly valuable in this field, as they can be used to model complex business processes, identify inefficiencies, and optimize resource allocation. This course provides a practical introduction to graph algorithms, empowering Business Analysts to leverage these techniques in their work. By mastering these algorithms, Business Analysts can develop more data-driven and effective solutions to business challenges, leading to improved operational performance and profitability.
Software Architect
Software Architect
Software Architects design and oversee the development of complex software systems. Graph algorithms are often used in this field to model software architectures, analyze system dependencies, and optimize performance. This course provides a comprehensive introduction to graph algorithms, equipping Software Architects with the necessary skills to make informed decisions in their work. By mastering these algorithms, Software Architects can design more scalable, reliable, and efficient software systems, ensuring successful project outcomes and user satisfaction.
Data Visualization Engineer
Data Visualization Engineer
Data Visualization Engineers design and develop data visualizations to help users understand complex data. Graph algorithms are essential in this field, as they are used to create interactive visualizations of networks, hierarchies, and other complex relationships. This course provides a practical introduction to graph algorithms, empowering Data Visualization Engineers to leverage these techniques in their work. By mastering these algorithms, Data Visualization Engineers can create more engaging and informative visualizations, enabling users to make better decisions based on data.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases, ensuring data integrity and availability. Graph algorithms are increasingly used in this field for tasks such as database optimization, data integration, and data analysis. This course provides a solid foundation in graph algorithms, enabling Database Administrators to leverage these techniques in their work. By mastering these algorithms, Database Administrators can improve database performance, reduce data redundancy, and enhance data security.
Product Manager
Product Manager
Product Managers oversee the development and launch of new products and features. Graph algorithms can be useful in this field for tasks such as market research, customer segmentation, and product roadmap planning. This course provides a practical introduction to graph algorithms, empowering Product Managers to leverage these techniques in their work. By mastering these algorithms, Product Managers can make more data-driven decisions, better understand customer needs, and develop more successful products.
Project Manager
Project Manager
Project Managers plan and execute projects, ensuring their successful completion. Graph algorithms can be useful in this field for tasks such as project planning, resource allocation, and risk management. This course provides a practical introduction to graph algorithms, empowering Project Managers to leverage these techniques in their work. By mastering these algorithms, Project Managers can develop more efficient project plans, optimize resource utilization, and mitigate potential risks.
For more career information including salaries, visit:
OpenCourser.com/course/4lrims/working
Reading list
We've selected 15 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Working with Graph Algorithms in Python.
This textbook provides a comprehensive introduction to graph theory and includes topics such as graph algorithms, graph coloring, and graph embeddings. It valuable reference for students and researchers interested in graph theory.
Save
This textbook provides a broad overview of graph algorithms and covers topics such as shortest paths, minimum spanning trees, and network flows. It valuable resource for students and researchers interested in graph algorithms.
Save
This textbook provides a comprehensive introduction to algorithms and covers topics such as sorting, searching, and graph algorithms. It valuable resource for students and researchers interested in algorithms.
This textbook provides a comprehensive introduction to data structures and algorithms in Python. It covers topics such as lists, stacks, queues, trees, and graphs. It valuable resource for students and researchers interested in data structures and algorithms in Python.
Provides a practical introduction to algorithms and covers topics such as sorting, searching, and graph algorithms. It valuable resource for students and researchers interested in learning about algorithms in a fun and engaging way.
This textbook provides a comprehensive introduction to graph theory and includes topics such as graph algorithms, graph coloring, and graph embeddings. It valuable resource for students and researchers interested in graph theory.
This textbook provides a comprehensive introduction to discrete mathematics and covers topics such as graph theory, logic, and set theory. It valuable resource for students and researchers interested in discrete mathematics.
Save
Provides a practical introduction to algorithms and covers topics such as sorting, searching, and graph algorithms. It valuable resource for students and researchers interested in learning about algorithms in a clear and concise way.
Provides a concise introduction to algorithms and covers topics such as sorting, searching, and graph algorithms. It valuable resource for students and researchers interested in learning about algorithms in a quick and easy-to-understand way.
Save
This textbook provides a comprehensive introduction to algorithm design and covers topics such as greedy algorithms, dynamic programming, and network flows. It valuable resource for students and researchers interested in algorithm design.
Provides a practical introduction to algorithm design and covers topics such as greedy algorithms, dynamic programming, and network flows. It valuable resource for students and researchers interested in learning about algorithm design in a hands-on way.
Provides a practical introduction to data structures and algorithms and covers topics such as lists, stacks, queues, trees, and graphs. It valuable resource for students and researchers interested in learning about data structures and algorithms in an easy-to-understand way.
Provides a practical introduction to algorithms and covers topics such as sorting, searching, and graph algorithms. It valuable resource for students and researchers interested in learning about algorithms in a clear and engaging way.
Provides a comprehensive introduction to data structures and algorithm analysis in C++. It covers topics such as lists, stacks, queues, trees, and graphs. It valuable resource for students and researchers interested in learning about data structures and algorithms in C++.
Provides a comprehensive introduction to data structures and algorithms in JavaScript. It covers topics such as lists, stacks, queues, trees, and graphs. It valuable resource for students and researchers interested in learning about data structures and algorithms in JavaScript.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/4lrims/working
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Time
134 hours
Level
Introductory
Via
Pluralsight
Instructor
Janani Ravi
Language
English
Good to know
Explores multiple algorithms for graphs, giving learners a deeper understanding and more utility for them
Taught by Janani Ravi, who has years of experience working with graphs
Uses Python, which is commonly used for working with data structures in the industry
Builds a strong foundation for learners in Python, data structures, and graph theory
Provides real-world examples of graph applications such as maps and social networks
Gives learners the skills and knowledge to implement graph algorithms in code
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