In recent years, data science has become a very important part of the Python's eco system. EuroPython has been dedicated to showcasing Python's use in data science for many years. This year, we have reserved two all day tracks to this topic.
Data Science Track
We are happy to announce a complete data science track as part of the main EuroPython conference this year.
Many of the talks rely on projects which are funded by NumFocus. Please find more information and the mission of NumFocus supporting Open Source Software below.
We will have two full day tracks featuring more than 18 talks and a keynote:
- Thursday and Friday in the Parrot room
- Keynote on Friday
- 30 Golden Rules of Deep Learning Performance by Siddha Ganju
- Meditations on First Deployment: A Practical Guide to Responsible Development by Alejandro Saucedo
- 15 Things You Should Know About Spacy by Alexander Hendorf
- Boosting simulation performance with Python by Eran Friedman
- Building smarter solutions with no expertise in machine learning by Laurent PICARD
- Deceptive Security using Python by Gajendra Deshpande
- Detecting and Analyzing Solar Panels in Switzerland using Aerial Imagery by Adrian Meyer
- Docker and Python: making them play nicely and securely for Data Science and ML by Tania Allard
- IPython: The Productivity Booster by Miki Tebeka
- Machine Learning for Everyone by Aaron Ma
- Making Pandas Fly by Ian Ozsvald
- Mastering a data pipeline with Python: 6 years of learned lessons from mistakes by Robson Junior
- Painless Machine Learning in Production by Chase Stevens
- Painting with GANs: Challenges and Technicalities of Neural Style Transfer by Anmol Krishan Sachdeva
- Ray: A System for High-performance, Distributed Python Applications by Dean Wampler
- Real Time Stream Processing for Machine Learning at Massive Scale by Alejandro Saucedo
- Sharing Reproducible Python Environments with Binder by Sarah Gibson
- Speed Up Your Data Processing by Chin Hwee Ong
- The Painless Route in Python to Fast and Scalable Machine Learning by Victoriya Fedotova, Frank Schlimbach
- The Python Data Visualization Landscape in 2020 by Bence Arató
- Developing a match-making algorithm between customers and Go-Jek products! by Gunjan Dewan
- Radio Astronomy with Python by Priscila Gutierres
- Simulation of logistic systems in Python with salabim by Ruud van der Ham
The mission of NumFOCUS is to promote open practices in research, data, and scientific computing by serving as a fiscal sponsor for open source projects and organizing community-driven educational programs.
NumFOCUS envisions an inclusive scientific and research community that utilizes actively supported open source software to make impactful discoveries for a better world.
Many known projects are supprted by NumFoucs, just to mention a few: NumPy, Pandas, Matplotlib, Jupyter, SciPy, SymPy, Bokeh, xarray,… The full list.
NumFOCUS is a 501(c)3 public charity in the United States.