Applying topological data analysis and geometry-based ML

Highlights:

• 00:02:25 - Colleen’s motivation for writing a book, interdisciplinary collaborations, and explaining advanced mathematical tools in accessible ways.
• 00:08:44 - Journey from biology and social sciences to data science, and the integration of different mathematical tools in solving data problems.
• 00:14:13 - Overcoming imposter syndrome and the value of exploring beyond one's field.
• 00:15:02 - The importance of mentorship.
• 00:23:40 - Coping strategies for setbacks in academia and industry.

About the Guest:

Colleen Farrelly is an author and senior data scientist. Her research has focused on network science, topological data analysis, and geometry-based machine learning. She has a master's from the University of Miami and has experience in many fields, including healthcare, biotechnology, nuclear engineering, marketing, and education. Colleen wrote the book, The Shape of Data: Geometry-Based Machine Learning and Data Analysis in R. 

Mentions:

Connect with Colleen Farrelly on LinkedIn

Related Links:

The Shape of Data: Geometry-Based Machine Learning and Data Analysis in R

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