Graph Analytics to Improve Business Insights
What you'll learn
- You will be able to understand and articulate the use case for knowledge graph solution
- You will be able to define relative complexity of knowledge graph solution
- You will learn how to Design a Graph solution
- Finally, using numerous class examples, you will be able to design and document your understanding on Knowledge Graph Insights
- In case, you are looking for development / coding experience, please try our other course titled "Knowledge Graph solution development using TigerGraph".
Requirements
- None
Description
Most of the knowledge we possess is encoded in many ways. There are books, publications, dictionaries, standards, government regulations, company three-letter-acronyms (TLA) and so on. Also as we develop insights, we know how to connect the dots by relating seemingly disparate insights by connecting the dots.
As computers get smarter, they also need to know how to connect the dots and relate to published knowledge. Today, you can use Google to connect the dots across a large number of web pages. Graph analytics drives the most successful recommendation engines. It powers media content analysis and discovery for news, movies and speeches. How do you bring this ability to your private and public information and how do you use it for a business application?
A Graph can be designed at various levels of sophistication. We start from simple graphs connecting nodes using edges, and then gradually introduce elastic graphs, classifications, ontology and temporal reasoning to bring advanced graph concepts. We show how graphs can be used for a variety of reasoning techniques including search, discovery, and recommendation.
The course will cover many topics associated with design of a Graph Analytics Solution:
Design and analytics using structured graph
Evolution of Knowledge Graphs
Knowledge graph concepts and maturity levels
Elastic Graph design and applications
Classification Graph design and applications
Higher levels of Knowledge graph insight solutions
Components of a Graph solution and how they interact with users
Using a dataset on movies, we have created a course material to show you how to design a Graph solution which uses many Knowledge Graph techniques to mimic how we process information and develop insights by connecting the dots. The examples have been created using TigerGraph, a leading Graph database provider. We also show the system components for a graph based solution and how it integrates with data / knowledge sources, users and knowledge curators.
Who this course is for:
- Management, strategy and business analyst professionals
- Architects, technical leads and system analysts from IT organization
- Senior year undergraduate and graduate students in Business, Analytics, and IT
- Vendors, consultants and service providers for Graph Analytics
Instructor
![Neena Sathi](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
- 4.2 Instructor Rating
- 329 Reviews
- 1,018 Students
- 13 Courses
Neena Sathi is a principal at Applied AI Institute. She has 30+ years of experience envisioning, designing, developing and implementing AI solutions associated with enhancing customer experience, back office automation and risk and compliance for many Fortune 100 organizations. She has worked in senior technical positions at Carnegie Group, Inc, an AI startup, Accenture, KPMG, and IBM.
Neena has three masters degrees including MBA from leading US universities. She is Master certified integration architect from IBM and Open Group as well as certified Project management professional (PMP) from Project management institute. She is also certified in many Cloud and Cognitive technologies. She has widely presented and published many papers in AAAI, IEEE, WCF, ECF, IBM Information on Demand, IBM Insight, World of Watson, IBM Developer Works and various academic journals.
Suvesh Balasubramanian has teamed up with Neena Sathi on this course. He has over 25 years of broad base industry, management consulting and advisory experience aligning enterprise strategy and desired outcomes with technology enabled business solutions. He has led and delivered several large digital transformation initiatives, including AI, Data Science and Analytics across a wide array of industries including Healthcare, Logistics, Hi-Tech, Financial Services and Retail