Graph Analytics for Big Data
Master graph analytics to model, analyze, and extract insights from complex network data using Neo4j, GraphX, and Giraph.
Master graph analytics to model, analyze, and extract insights from complex network data using Neo4j, GraphX, and Giraph.
This course provides a comprehensive overview of graph analytics, teaching you how to model, store, retrieve, and analyze graph-structured data in a scalable manner. You'll discover how to represent real-world problems as graphs and apply analytical techniques to extract valuable insights. The course covers fundamental graph concepts and their applications in various domains including social networks, biological systems, and smart cities. You'll learn essential analytics techniques such as path finding using Dijkstra's algorithm, connectivity analysis, community detection, and centrality measures. Through hands-on exercises with powerful tools like Neo4j and its Cypher query language, you'll perform practical analyses on graph networks. The course also introduces large-scale graph processing frameworks like Pregel, Giraph, and GraphX, enabling you to implement graph algorithms at scale. By the end of this course, you'll be able to model problems into graph databases, perform analytical tasks over graphs in a scalable manner, and apply these techniques to understand the significance of your own datasets.
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English
پښتو, বাংলা, اردو, 3 more
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What you'll learn
Model real-world problems into graph database structures
Perform path analytics using algorithms like Dijkstra's
Implement connectivity and community detection analyses
Use Neo4j and Cypher for practical graph querying and analysis
Apply centrality measures to identify important nodes in networks
Work with large-scale graph processing frameworks like GraphX
Skills you'll gain
This course includes:
3.9 Hours PreRecorded video
6 assignments
Access on Mobile, Tablet, Desktop
Batch access
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There are 5 modules in this course
This course provides a comprehensive introduction to graph analytics for big data applications. Students learn to model and analyze complex networked data using graph structures and algorithms. The curriculum begins with fundamental graph concepts and their applications in domains like social networking, biological networks, and smart cities. Core graph analytics techniques are covered in depth, including path finding, connectivity analysis, community detection, and centrality measures. The course has a strong practical component, with hands-on demonstrations using Neo4j and its Cypher query language to perform various graph analyses. Students also explore large-scale graph processing frameworks like Pregel, Giraph, and GraphX for handling big data graph problems. Throughout the course, theoretical concepts are reinforced with practical examples and exercises, enabling students to apply graph analytics techniques to their own data challenges.
Welcome to Graph Analytics
Module 1 · 13 Minutes to complete
Introduction to Graphs
Module 2 · 2 Hours to complete
Graph Analytics
Module 3 · 3 Hours to complete
Graph Analytics Techniques
Module 4 · 2 Hours to complete
Computing Platforms for Graph Analytics
Module 5 · 2 Hours to complete
Fee Structure
Instructor
Expert in Cosmology and Scientific Computing
Andrea Zonca leads the Scientific Computing Applications group at the San Diego Supercomputer Center, combining his cosmology expertise with advanced computing skills. His academic foundation includes extensive work analyzing Cosmic Microwave Background data from the Planck Satellite during his Ph.D. and postdoctoral research. At SDSC, he has developed significant expertise in supercomputing, particularly in parallel computing with Python and C++, and maintains widely used community software packages like healpy and PySM. His current role involves leading efforts to help research groups optimize their data analysis pipelines for national supercomputers. He has also built specialized knowledge in cloud computing, particularly in deploying services on platforms like Jetstream using Kubernetes and JupyterHub. As a certified Software Carpentry instructor, he teaches essential computational skills to scientists, including automation with bash, version control with git, and Python programming. His research contributions have been significant, with his work on the healpy package becoming a crucial tool for data analysis on spherical surfaces in Python, garnering widespread use in the scientific community.
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