Open-source data science tools and libraries—including the Python libraries scikit-learn, statsmodels, matplotlib, and more—come together with the open-source IPython Notebook. The Notebook has evolved rapidly over the past couple of years, and people in diverse fields are taking it up with enthusiasm. The wide user base and varied use cases produce many opportunities for advancing data science in both academia and industry, but bring along unique challenges as well.
The IPython Notebook is perfect for:
This talk will explore some overall best practices for sharing IPython Notebook code within a data science team, with special attention to the above use cases and how to get the most out of the IPython Notebook for each one.
Jonathan Whitmore, PhD (physics), is a data scientist at Silicon Valley Data Science. He has a diverse range of interests and is excited by the challenges and possibilities in the field of data science and engineering. Dr. Whitmore completed an astrophysics postdoc in Melbourne, Australia, where his research focused on trying to determine whether the physical constants of the universe have changed over cosmological times. He has a long-standing commitment to the public understanding of science and technology, most notably by his co-starring in the 3D IMAX film Hidden Universe, which is currently playing in theatres around the world. He has also been an invited conference speaker on using the IPython Notebook for astrophysics research. Dr. Whitmore received his PhD in physics from the University of California in San Diego, and graduated with a bachelor of science from Vanderbilt with a triple major in physics, philosophy, and mathematics.
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