15 November 2018
Learn why an averages approach fails to describe data in the real world and how graph algorithms can help you predict real-world behavior - or potential events in the popular series, Game of Thrones! We will start with an overview of which algorithms in Neo4j to apply for various types of optimal paths, influence in a network, and community detection. We will also discuss real-life use cases that span across industries including recommendations, resiliency planning, fraud prevention, and traffic engineering/routing (such as IP and call). These topics will come alive through a live demo using data from the Game of Thrones, where we look at what kinds of information you can retrieve and decisions you can make based on results from different algorithms. From this session, you will gain the knowledge to recognize whether you have a graph analytics problem and how you can get started!
Jennifer Reif (https://twitter.com/jmhreif) is a Developer Relations Engineer at Neo4j, conference speaker, blogger, and an avid developer and problem-solver. She holds a Master’s degree in Computer Management and Information Systems and has worked with large enterprises to organize and make sense of widespread data assets and leverage them for maximum business value. She has worked with a variety of commercial and open source tools and enjoys learning new technologies, sometimes on a daily basis! Her passion is finding ways to organize chaos and deliver software more effectively.
Metis (thisismetis.com) accelerates careers in data science by providing full-time immersive bootcamps, evening part-time professional development courses, online resources, and corporate programs based in Seattle, New York, Chicago, and San Francisco.
Brought to you by Kaplan, Metis focuses primarily on Python, machine learning, data visualization, deep learning, big data processing, statistical foundations, and more. Students and alumni of the bootcamp program receive continuous support from our career advisors, empowering them to pursue a successful career in the fast-growing field of data science.
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