Lorena Barba,
In this video from BIDS Spring 2017 Data Science Faire, Lorena Barba from The George Washington University presents: Data Science for All.
“Data Science—understood broadly as a merger between computation, statistics, data management, and real-world applications—permeates through every sector of modern society. Innovative companies are developing data products galore, creating wealth and changing our daily habits: how we shop, how we commute, how we learn. Beyond products, algorithms are used to feed us advertisement and “news,” marshal police patrols in line to crime predictions, and even select the “right” employee for a position. Automatic systems are judging us. And not only do they reflect the inequalities of society, they can inflame our differences.
In this new world, every citizen needs data science literacy. UC Berkeley is leading the way on broad curricular immersion with data science, and other universities will soon follow suit. The definitive data science curriculum has not been written, but the guiding principles are computational thinking, statistical inference, and making decisions based on data. “Bootcamp” courses don’t take this approach, focusing mostly on technical skills (programming, visualization, using packages). At many computer science departments, on the other hand, machine-learning courses with multiple pre-requisites are only accessible to majors. The key of Berkeley’s model is that it truly aims to be “Data Science for All.”
Lorena A. Barba is an associate professor of mechanical and aerospace engineering. She has M.Sc. and Ph.D. degrees in aeronautics from the California Institute of Technology and B.Sc. and PEng degrees in mechanical engineering from Universidad Técnica Federico Santa María in Chile. Prior to joining GW, she was an assistant professor of mechanical engineering at Boston University (2008–2013) and a lecturer/senior lecturer of applied mathematics at University of Bristol, UK (2004–2008).
Professor Barba is an Amelia Earhart Fellow of the Zonta Foundation (1999), a recipient of the EPSRC First Grant program (UK, 2007), an NVIDIA Academic Partner award recipient (2011), and a recipient of the NSF Faculty Early CAREER award (2012). She was appointed CUDA Fellow by NVIDIA Corporation (2012) and is an internationally recognized leader in computational science and engineering.
