Meet the data scientist: Patrick Hall

By Stephanie Robertson, SAS Insights Editor

Only a few years after completing the Masters of Analytics program at NC State University, Patrick Hall was part of a team that submitted a patent for an algorithm that will solve a fundamental data mining problem: determining the number of clusters in a data set. As part of our Data Scientist Series, we interviewed Hall who works at SAS.

What’s your background and education?
I originally did a BA in math at the University of North Carolina. I tried a PhD program in physical chemistry at the University of Illinois, but found out it wasn’t really for me. I took the programming skills I’d picked up and began working at small start-ups back in the Triangle area of North Carolina. After a few years, I went back to school at NC State in the Masters of Analytics program. Now I’m at SAS.

Patrick Hall, Data Scientist
Patrick Hall, Data Scientist

What skills help you most as a data scientist?
To me – and I do think data scientist is a hard term to define – being able to manipulate large amounts of data is what separates data scientists from more traditional roles like analysts or statisticians. Being able to pull diverse sources of data together usually requires creating your own software tools, but once you get data into the appropriate format, it’s much easier to analyze it, visualize it and tell a persuasive story with it.

When did you figure out you wanted to be a data scientist? What motivated you to become one?
Data science wasn’t a thing when I was in high school and college, but I realized I liked analyzing and visualizing data in chemistry graduate school. I gravitated toward advanced data analysis methods and I just really liked making the visualizations that we used to communicate our experimental results.

What department do you work in and who do you report to?
I work in SAS Enterprise Miner R&D. I report to Susan Haller, director in advanced analytics.

How long have you had your job and were you hired specifically to be a data scientist?
I’ve worked in SAS Enterprise Miner R&D since mid-2012. I’m officially a senior staff scientist. Another good definition of a data scientist is someone who is a better statistician than most programmers and a better programmer than most statisticians. I think that’s basically what’s expected of me in my current role.

Do you work on a team? If so, what’s the makeup of the team?
Yes! I work with many brilliant, and nice, people. Many of them are highly specialized statisticians, mathematicians and programmers. We have complementary strengths and have been able to do some pretty cool things together.

What’s your job like? Is there a typical day, or is each day different? Can you give us a basic idea of what you do and the kind of projects you work on?
Every day is different. Sometimes I work on code. Sometimes I work on prototypes of analytical methods that customers might never even see. Sometimes I work on books and papers. Sometimes I work on customer problems.

What’s your biggest challenge?
My biggest challenges usually come from specific problems that customers are dealing with. Solving these problems can be difficult and time consuming, but it’s incredibly important to stay in touch with our customers so that we can continue improving the tools we provide them.

What’s your biggest accomplishment thus far?
Submitting a patent application, along with several colleagues, for an algorithm that can estimate the number of clusters in a data set. Determining the number of clusters in a data set is a fundamental problem in data mining and customer relationship management (CRM).

What do you enjoy doing in your spare time?
I’m a pretty big nerd. So I spend a lot of time on Kaggle – which is a data mining contest platform – and learning about newer technologies – like Bokeh, a data visualization package in Python. I also do some cycling and try to make it to the North Carolina mountains whenever I can.

What’s your favorite new technology or app?
It’s a three-way tie between Quora, Strava and Github, none of which are really that new.

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