A Pythonista’s Guide to Julia for Data Science Applications

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Whenever you ask a data scientist about this new programming language, you usually get one of the following two answers: “what’s this?”, or “it’s not ready yet for data science”. However, if you look closely to the language itself and its relationship with the data science community, it’s easy to see that this view is debatable. In fact, Julia has come a long way since the time it was branding itself as a technical computing language. Now, more and more people recognize its value as a data science tool and contrary to other languages out there that present themselves as the best thing since C, Julia aims to be part of an ecosystem of data science language, with APIs for Python, R, and other programming platforms.

Julia can be integrated in the pipeline immediately and to whatever degree you feel comfortable with, without having you abandon your platform of choice. You can have your data stored in NumPy arrays and still employ Julia to do all the heavy-lifting that Python may not be able to do fast enough, without the use of a C function on the backend. The best part is that you don’t even have to leave the all-too-comfortable IPython Notebook interface. So, if you want to learn more about how you can integrate Julia in your pipeline, having the best of both worlds, this is the talk for you.

About the Speaker

Dr. Zacharias Voulgaris was born in Athens, Greece. He studied Production Engineering and Management at the Technical University of Crete, shifted to Computer Science through a Masters in Information Systems & Technology (City University of London), and then to Data Science through a PhD on Machine Learning (University of London). He has worked at Georgia Tech as a Research Fellow, at an e-marketing startup in Cyprus as an SEO manager, and as a Data Scientist in both Elavon (GA) and G2 (WA). He also was a Program Manager at Microsoft, on a data analytics pipeline for Bing.

Zacharias has authored two books and several scientific articles. His first book, Data Scientist - The Definitive Guide to Becoming a Data Scientist (Technics Publications), has been translated into Korean and Chinese, while his latest one (a hands-on guide on the Julia language for Data Science applications) is coming out this Autumn. He has also reviewed a number of data science books (mainly on Python) and has a passion for new technologies, literature, and music.

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