We'll look at the common performance problems when doing typical data science tasks and the ways to fix them with better tools or smarter algorithms.
Target audience: this talk would be most suitable for those familiar with the Python scientific stack (numpy, scipy, pandas, h5py, scikit-learn), at least on the surface level.
About Ivan Smirnov
"I’m a quant and a programmer interested in scientific computing, data science, web technologies and finance.
I currently work as a quantitative researcher for Susquehanna International Group, where I use Python on daily basis to analyze high-frequency financial data. Aside from that I do some open source development and I spend a good portion of my free time hacking on various side projects, mostly in Python and Rust as of recent.
I’m originally from Moscow, Russia and I got my undergrad in Applied Math & Computer Science from Moscow State University. Later on I joined the graduate program in University of Alberta, Canada where I got my PhD in Mathematical Finance."
Talk is about configuration and usage of virtual environment on windows. Using in popular IDEs (Eclipse and PyCharm) is explained. And notes about current state and recommendation of using it.
About Mladen Uzelac
IT professional with Masters degree interested in Open Source. I have programming experience in various languages (Java, Python, ...) and lately I am interested in Linux, Docker and PostgreSQL.
