Unlock the power of graph theory to analyze complex data at scale with Python. This course delves into network science and its real-world applications, offering practical insights into transforming data into network structures. Learners will explore advanced graph algorithms and apply them to solve real-world problems, building scalable solutions that address big data challenges. With hands-on Python examples, you'll deepen your understanding of data analysis, machine learning, and network-based analytics. By the end, you’ll be equipped to tackle network-related problems efficiently in both research and industry settings.
Modern Graph Theory Algorithms with Python
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Transform spatial and time series data into network structures
Apply graph theory and Python tools to analyze complex datasets
Implement machine learning algorithms on network data
Details to know
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Recently updated!
February 2026
Assessments
14 assignments
Taught in English
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There are 14 modules in this course
In this section, we introduce graph theory fundamentals, real-world social networks, and Python-based network visualization techniques for data analysis applications.
What's included
2 videos3 readings1 assignment
In this section, we cover transforming spatial, temporal, and social data into networks.
What's included
1 video5 readings1 assignment
In this section, we analyze how social factors shape network structures and influence the spread of ideas and diseases. Key concepts include cultural similarity, geographic ties, and network features in real-world examples.
What's included
1 video3 readings1 assignment
In this section, we explore transportation logistics, focusing on shortest path algorithms, route optimality, and the max-flow min-cut method to optimize delivery efficiency and scalability in real-world networks.
What's included
1 video2 readings1 assignment
In this section, we explore spectral clustering methods for analyzing ecological data, focusing on animal population networks and text-based surveys to support conservation and urban planning.
What's included
1 video3 readings1 assignment
In this section, we explore temporal data analysis and apply centrality metrics to stock market trends, enabling the identification of structural changes and price behavior patterns over time.
What's included
1 video4 readings1 assignment
In this section, we analyze spatiotemporal data using igraph, examining local Moran statistics and changes in curvature and PageRank centrality over time slices.
What's included
1 video2 readings1 assignment
In this section, we examine dynamic social networks and their evolving structures, focusing on spreading processes and real-world applications using wildlife and social datasets.
What's included
1 video5 readings1 assignment
In this section, we explore machine learning on relational network data, integrating network metrics with metadata to predict outcomes and enhance relationship analysis.
What's included
1 video4 readings1 assignment
In this section, we explore pathway mining using Bayesian networks and reasoning algorithms to analyze sequential data in education and medicine, identifying causal links and optimal pathways for intervention.
What's included
1 video3 readings1 assignment
In this section, we examine ontologies and language families using network science to analyze relationships and quantify differences in linguistic structures.
What's included
1 video3 readings1 assignment
In this section, we explore graph databases for network data storage, focusing on Neo4j. We learn to query and modify data using Cypher for efficient analysis in real-world applications.
What's included
1 video4 readings1 assignment
In this section, we apply network science and GEEs to analyze spatiotemporal Ebola data for public health risk assessment.
What's included
1 video3 readings1 assignment
In this section, we explore emerging network science tools like quantum graph algorithms, neural network architectures, and hypergraphs to enhance data analysis and organization in diverse fields.
What's included
1 video4 readings1 assignment
Instructor
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Frequently asked questions
Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.
If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.
Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.
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