Tutorials last either 90 minutes or 180 minutes. In the latter case, they are split in two sessions. The exact hours are subject to change to account for the logistics of coffee and lunch breaks. Tutorial istructions can be found under https://github.com/euroscipy/www.euroscipy.org/wiki/Euroscipy-2018-tutorial-requirements
| Time | Introductory | Advanced | Applications |
|---|---|---|---|
| 9:00 | Introduction to Jupyter, M. Muller | Databases for Scientists / SQL Intro, Alexander CS Hendorf | Doing bioinformatics with scikit-bio and BioPython, Joris Vankerschaver |
| 10:30 | Coffee Break | Coffee Break | Coffee Break |
| 11:00 | Introduction to Python, M. Rastgoo & G. Lemaitre | Blending MongoDB's Aggregation Framework with Numpy and Pandas, A. Herlihy & N. Leniz | Handling Geospatial Data using Python, Part I: Base Modules, M. Christen |
| 12:30 | Lunch | Lunch | Lunch |
| 14:00 | Introduction to Python, M. Rastgoo & G. Lemaitre | From exploratory computing to performances, a tour of Python profiling and optimization, A. Ingargiola | Handling Geospatial Data using Python, Part II: Geopandas, Joris Van den Bosche |
| 15:30 | Coffee Break | Coffee Break | Coffee Break |
| 16:00 | NumPy I, G. Ingold | Privacy for Data Scientists, Katharine Jarmul | - |
| Time | Introductory | Advanced | Applications |
|---|---|---|---|
| 9:00 | NumPy II, G. Ingold | The Hitchhiker's Guide to Parallelism with Python, D. Valters | Advanced machine learning, Y. Peleg |
| 10:30 | Coffee Break | Coffee Break | Coffee Break |
| 11:00 | Matplotlib, Alexandre de Siqueira | Parallel Data Analysis with Dask, Ian Stokes Rees | Understanding and diagnosing your machine-learning models, G. Varoquaux |
| 12:30 | Lunch | Lunch | Lunch |
| 14:00 | Pandas, Marc Garcia & S. Greiner | Data visualization -- from default and suboptimal to efficient and awesome, B. Gorelik | Deep Diving into GANs: From Theory to Production Michele De Simoni, Paolo Galeone |
| 15:30 | Coffee Break | Coffee Break | Coffee Break |
| 16:00 | Scipy, M. Mueller and E. Gouillart | CFFI, Ctypes, Cython: the Good, the Bad and the Ugly, M. Picus | Deep Learning in Python using Chainer, C. Loomis |
| Time | Track 1 | Track 2 |
|---|---|---|
| 8:30 | Registration | |
| 9:00 | Opening Notes | |
| 9:15 | KEYNOTE How to drive business decisions with AI Felix Wick |
|
| 10:15 | Coffe Break | |
| 10:45 | Scikit-learn and tabular data: closing the gap Joris Van den Bossche |
Deep Learning for Human Pose Estimation Ale Solano |
| 11:15 | Numpy - where we are and where we want to be Matti Picus |
GMDH Neural Network for Short-term Electricity Load Forecasting Kostas Passadis |
| 11:30 | Extending Python 3.7s Data Classes Geir Arne Hjelle |
modAL: A modular active learning framework for Python Tivadar Danka |
| 11:45 | Searching efficiently through (genomic) sequences with vantage point trees Joris Vankerschaver |
Imbalanced-learn: a scikit-learn-contrib to tackle learning from imbalanced data set Guillaume Lemaitre |
| 12:00 | ModelXplore, a python based model exploration Nicolas Cellier |
Efficient Biomedical Named Entity Recognition in Python Tilia Ellendorff |
| 12:15 | Simpler data science: dirty categories and scikit-learn updates Gael Varoquaux |
Going full Python for Machine Learning in Biomedical engineering Jérémy Laforêt |
| 12.30 | Lunch | |
| 14:00 | Detecting anomalies using statistical distances Charles Masson |
Data visualizations for the web with Altair and Vega(-Lite) Patrick Muehlbauer |
| 14:30 | Apache Parquet as a columnar storage for large datasets Peter Hoffmann |
When less is more: dimensionality reduction in neuroscience Pietro Marchesi |
| 15:00 | How to not screw up with machine learning in production (and more about engineering in data science) Denys Kovalenko |
Navigating the Magical Data Visualisation Forest Margriet Groenendijk |
| 15:15 | KPIs implementation and decision tree algorithms as support tools in wastewater treatment plants management. Giuseppe Antonello |
Three most common mistakes in data visualization Boris Gorelik |
| 15:30 | Coffe Break | |
| 16:00 | How PyPy can help for high-performance computing Antonio Cuni |
Phase-space analysis of chaotic deterministic dynamics with Python: the case of biological systems with many degrees of freedom Paola Lecca |
| 16:30 | Pyccel, a Fortran static compiler for scientific High-Performance Computing Ratnani Ahmed |
Fission track counting in mineral samples using pytracks Alexandre de Siqueira |
| 16:45 | Poster Introduction | |
| 17:00 | Poster Session | |
| - | END | |
| Time | Track 1 | Track 2 |
|---|---|---|
| 8:30 | Registration | |
| 9:00 | KEYNOTE Python in medicine: signal processing - learning and visualization of temporal data Annalisa Barla |
|
| 10:30 | Coffe Break | |
| 11:00 | CatBoost - the new generation of Gradient Boosting Anna Veronika Dorogush |
Big geospatial data visualization and analysis in Jupyter Davide De Marchi |
| 11:30 | Teaching with JupyterHub - lessons learned Martin Christen |
Python Tools for Climate Science Robert Gieseke |
| 11:45 | Teaching programming with Jupyterhub and Nbgrader Gert-Ludwig Ingold |
Remapping or Regridding between Spherical Grids for Earth Modeling Ki-Hwan Kim |
| 12:00 | Reproducibility and exploratory computing with a Jupyter-based workflow Antonino Ingargiola |
Listening to Quasars and Shooting Satellites With Lasers Geir Arne Hjelle |
| 12.30 | Lunch | |
| 14:00 | Scientific computing for quantum technology Nathan Shammah and Shahnawaz Ahmed |
Scalable Data Science with Python Alejandro Saucedo |
| 14:30 | Benchmarking and performance analysis for scientific Python Roman Yurchak |
Pythonizing workflows with with modern and legacy chemistry softwares. Olav Vahtras |
| 14:45 | F2x - Automated FORTRAN wrapping without limits Michael Meinel |
Parselmouth: an efficient Python interface to the Praat phonetics software package Yannick Jadoul |
| 15:00 | RepSeP - Reproducible Self-Publishing for Python-Based Research. Christian Horea |
Explaining model predictions using Shapley values Ankur Ankan |
| 15:15 | Coffe Break | |
| 15:45 | Machine Learning for microcontrollers with Python and C Jon Nordby |
Data Science Security — Protect Against Data Privacy Breaches Justin Mayer |
16:00 | Lightning Talks |
| 17:30 | END | |