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Clair Sullivan: Arrays, Linked Lists and Graphs
- Transcript: https://github.com/data-umbrella/event-transcripts/blob/main/2022/48-clair-linkedlist.md
- Meetup Event: https://www.meetup.com/data-umbrella/events/283846178/
- Video: https://www.youtube.com/watch?v=gWGyvfU80kc
- GitHub repo: https://github.com/cj2001/data_umbrella_linked_lists
- Transcriber: ? [needs a transcriber]
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It is easy and convenient to treat all data as an array. They are the basis of much of Python and a simple data structure to deal with. But there are times that arrays fail us, such as on element insertion and the pre-allocation of memory. There is power and efficiency in linking data through data structures such as linked lists. We will see how using linked lists can reduce Big O complexity and solve a variety of problems. Then we will explore how graph data structures take this a step further and open up a world of new options and opportunities for efficient computation.
Dr. Clair Sullivan is currently a data science advocate at Neo4j, working to expand the community of data scientists and machine learning engineers using graphs to solve challenging problems. She received her doctorate degree in nuclear engineering from the University of Michigan in 2002. After that, she worked in a variety of research positions within the US national laboratory and federal government and as a professor at the University of Illinois at Urbana-Champaign. In 2017 she accepted a job at GitHub as a Machine Learning Engineer and in 2021 she joined Neo4j as a Data Science Advocate. She has authored 4 book chapters, over 20 peer-reviewed papers, and more than 30 conference papers.
- LinkedIn: https://www.linkedin.com/in/dr-clair-sullivan-09914342/
- Twitter: https://twitter.com/CJLovesData1
- GitHub: https://github.com/cj2001
