Modern scientific computing and big data analytics in Python
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This is a tutorial on using the latest and most exciting tools in Python for scientific and engineering applications in 2013, with a focus on 'big data' applications. Using real-world data sets and a fully Python 3 environment, it will walk you through what's possible with modern tools like the machine-learning package scikit-learn, the image-processing package scikit-image, the Pandas toolkit for data analysis, and IPython-parallel. It will also review the upcoming generation of tools like Numba and Blaze.
Ed is well-known in the Python and scientific Python communities. Ed is known for his contributions to NumPy and SciPy, as the release manager of SciPy in 2005-6 and the author or coauthor of several of its modules.
Ed holds a PhD in computer science from Imperial College London, where his thesis was in statistical pattern recognition. He also holds BA and MA degrees in maths and computer science from Cambridge University. He has 20 years of experience in programming, teaching, and public speaking. He is the founder of Python Charmers, a Python training and consulting business based in Australia in Singapore.