Data Science: Interview with Kirk Borne, Principal Data Scientist, Booz Allen Hamilton
We thank Kirk Borne for participating in the Data Science Interview Series 2020 by sharing such accurate and inspiring reflections from his extraordinary level of knowledge and experience, including:
His breakdown of the top skills required by data scientists and how to tackle the daily challenges they face.
His actionable insights for anyone in data science, from beginners to seniors.
His attitude towards failure and lifelong learning.
How did you first get into data science?
I have always worked with data, since high school, a long time ago in a galaxy far far away. Specifically, my background is astrophysics, with a Ph.D. in the subject.
I performed astronomical data analysis, modeling, and simulation for 25+ years, while also working on data repositories for space science satellite missions at NASA. I became very interested in the scientific discovery opportunities of very large datasets in the late 1990's, at which time I began my quest into machine learning, data mining, and data science.
The motivation for me has always been discovery, from my early days until now. Observational data, modeled scientifically, can deliver insights, discoveries, and understandings of all things, not just the Universe, but the universe of everything.
How is data science used to create value in your current project(s)?
Data is a source of discovery: insights, understanding how things work, important patterns, meaningful relationships, innovation, new value, predictive models of things to come, and new opportunities.
Data science can reveal new properties of known things, identify previously unknown things with known properties, and discover new things with previously unknown properties. The things that we explore are all-inclusive: people, processes, products, events, and behaviors -- in all domains and industries.
What are the key skills that you use every day as a data scientist, and how did you develop them?
The "everyday" skills for me are more about the talents of a data scientist: domain knowledge (data understanding), data collection (sensors, measurement, access, databases, data systems), communication (data storytelling, data visualization), curiosity (question generation), exploratory data analysis (data literacy, coding), inference (machine learning, statistics), inquiry (model-building, simulation), and scientific methodology (experimentation, assessment, validation, refinement).
What are the top challenges you currently face as a professional data scientist, and how do you go about tackling them?
The top challenges include the first-mile challenge (finding, accessing, cleaning, and integrating diverse distributed complex data sources) and the last-mile challenge (deriving actionable insights from all of those data).
The best approach to tackling these is to collaborate with...
