During the livestream, Kirk Borne, principal data scientist at Booz Allen Hamilton and former professor of astrophysics and computational science at George Mason University, outlined five of the most important parts of data science.
1. The Data. “Data science is driven by the data–the fuel of what we’re doing is data,” Borne said. “Nothing is possible without fuel.” A real-world example: Sensor data collected from U.S. commercial jet engines for one year. In one year, those engines produce more than 1 zettabyte of data, Borne said. What’s interesting about data is not so much the volume, but the combinations of different types of data that can be put together.
2. The science. “Thinking about data science as a scientific cycle is extremely important,” he said. Data collection, hypothesis formulation, deduction and formulation of a predictive test, experimental design and testing, evaluation, and reviewing results are critical steps in the process.
3. Data storytelling. This is more than just showing a plot–it’s being able to express it in human terms, being able to describe the “so what,” and illustrate the importance. Being able to tell the data story can help clients and colleagues understand exactly what a data scientists has been doing in a way that’s human and consumable.
4. Data ethics. Data scientists should learn computational thinking and statistical thinking skills and be aware of their biases, Borne said. “If you torture your data long enough it will confess to anything,” he said, citing a well-known quote from economist Ronald Coase.
5. Data literacy. This includes understanding types of data, speaking intelligently about data, and being able to articulate what you can learn from it. Data literacy to me comes in 2 major subcategories, Borne said– how to use data, which is data science, and how to use data correctly, which is data ethics.
Borne also has created a reading list focusing on data science and data literacy, available here.
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