2024-11-16 –, LT9
Language: English
Graphs and networks are fundamental data structures that are rapidly growing in popularity with practicing engineers due to their use of simple elements like nodes and edges. Many real-world problems can be translated into graph problems, and we can use Python libraries, such as NetworkX, for creative problem-solving.
Take a day-to-day example as simple as arranging desks in an office. Is there an optimal way to arrange people by organizational structure and/or proximity to their informal social network in order to facilitate a conducive workplace environment that also takes into account noise levels for at-desk meetings, etc.? If we were to model this in a graph data structure with nodes being people with different properties, and edges describing relationships, this will enable us to see things in different perspectives and to come up with some innovative solutions to this problem.
Xiao Ying is a data analyst with the Company Financials Metadata Management & Analytics team in Bloomberg's Data department. In this role, she works closely with product managers, data teams, and the company's engineers on data product developments. Prior to joining Bloomberg to work on Data teams in the firm's Singapore and Hong Kong offices, she worked at a venture capital firm focused on transformational deep tech like artificial intelligence, blockchain, and MedTech. She is passionate about exploring innovative tech solutions that add tangible value. She holds a bachelor’s degree of science in statistics and management from the National University of Singapore.