Who should attend
This course is intended for data analysts, data scientists, marketers and decision makers
About the course
Graph-based data is becoming increasingly ubiquitous. For example, social media networks and professional networks like LinkedIn all feature graph-based data. This course will teach how to gain business insights by analysing graph-based data and show how to discover and leverage hidden relationships between the entities in the graph. This has widespread applications across a broad range of domains including business, engineering, manufacturing, healthcare. In addition, graph-based databases and algorithms offer a very scalable platform for undertaking big data analytics. Course topics include graph mining, link analysis, network complexity measurement, key player identification, network clustering, graph databases. Applications include mining social networks, professional networks, communications networks, document networks and web usage patterns. For example:
- Marketing Analytics – Graphs can be used to figure out the most influential people in a Social Network, advertisers and marketers can then route their message through these people.
- Banking Transactions – Graphs can be used to find unusual patterns that may indicate fraud, e.g. by analysing the flow of money across interconnected Banking networks.
- Supply Chain & Transportation – Graphs help in identifying optimum routes for delivery trucks and in identifying locations for warehouses and delivery centres
- Human Resource Management – Graphs can be used to match people to jobs, to identify key employees, to identify staff attrition causes and relationships etc.
This course is part of the Data Science series and Graduate Certificate in Big Data Engineering & Web Analytics series offered by NUS-ISS.
At the end of the course, the participants will be able to:
- Understand graph theory and the properties of graph and network data
- Understand the benefits of applying graph-based approaches to big data
- Identify the types of problem suitable for graph-based solution methods
- Identify and convert suitable data and problems into graph-based formalisms
- Identify and apply appropriate graph mining techniques and methods to gain business insights and/or achieve a defined business goal
- Basic programming skills are required for the workshops. Familiarity with R, Python or similar is recommended.
- Participants must successfully completed Big Data Engineering for Analytics course offered by NUS-ISS.
Suria has twenty years of teaching and consulting experience in areas such as software engineering, application architecture, crafting cloud services, agile development and big data engineering. Her research interest spans around cloud computing, software engineering, test automation and big dat...
Barry teaches Business Analytics, Data Mining and Knowledge Engineering at ISS and has over 30 years experience in these areas. Before joining ISS he was based in the US and specialized in web analytics and user response modeling for online ad targeting at Microsoft Display Advertising and in pro...
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Because of COVID-19, many providers are cancelling or postponing in-person programs or providing online participation options.
We are happy to help you find a suitable online alternative.