by Ian-Mathew Hornburg | at Minnebar 17
Engagement with data science, machine learning, and AI has grown substantially as these fields have matured and found new applications. While large and flashy commercial projects draw a great deal of attention, publically-available datasets produced by governments offer opportunities for citizen data scientists to contribute meaningfully to their communities through data-driven insights. I propose that such datasets are underutilized, especially given the potential to improve transparency in public life and promote accountability for civil servants and government functions.
This session will demonstrate an accessible case-study using public data, walking through the steps from dataset to descriptive statistics, and feature-engineering to model training using open-source, Python-based libraries. Attendees will gain skills (and hopefully inspiration) for getting started with their own projects in citizen data science.
Ian-Mathew Hornburg is a Minneapolis-based software engineer, where he currently develops managed Apache Kafka infrastructure as a Senior Engineer at Target. His work focuses on the intersection of data and risk management; modern big-data infrastructure; and engineering culture and practices. Previously, he worked in financial services with an emphasis on large-scale, performant data engineering using Apache Spark and NoSQL datastores for anti-money laundering analytics. He also contributes to efforts to mentor and recruit women, veterans, and LGBT people interested in technology.
He is a graduate of the University of Minnesota, where he earned a Bachelor of Applied Science in data science, data management, and computer science. He is also a U.S. Army veteran, serving as a soldier-musician for over a decade.
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