PyCon AU 2016 Science and Data Miniconf
2016-08-12
Melbourne, Australia
PyCon AU ·
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who it's for
data scientists, scientific Python developers, research software engineers, computational biologists, geoscientists, machine learning engineers, research infrastructure operators
topics
TensorFlow basics and GPU demos; image processing with OpenCV and dlib; genomics FASTQ->BAM->VCF pipelines; interactive dashboards from Jupyter notebooks; interactive plotting with Plotly and Bokeh; Landsat and Earth-observation data workflows; xarray and Dask chunked processing; data science design patterns and pipelines; The Virtual Brain whole-brain simulation; C extensions, Cython and Numba acceleration; CFFI wrapping and JupyterHub deployment
| title | author | length |
|---|---|---|
| Learning TensorFlow | Robert Layton | 41m |
Overview of TensorFlow basics with code: building Variables, Placeholders, Custom Functions as graph nodes, using a session to run parts of the graph, and… | ||
| A Girls Guide To Growing A Moustache with Python | Heidi Baxter | 20m |
A pipeline that finds a donor moustache and transposes it onto another face using OpenCV and dlib runs on a Raspberry Pi 3; it detects faces with a landmark… | ||
| Big data biology for pythonistas: getting in on the genomics revolution | Darya Vanichkina | 30m |
Sequencing cost falls from $3 billion and 13 years to under $1,000 and ~2 days, producing hundreds of thousands of genomes; the talk shows raw FASTQ → BAM →… | ||
| Using Jupyter notebooks to develop and share interactive data displays | Ioanna Ioannou, Maoyuan Liu | 29m |
Notebooks produce a working prototype that becomes a browser dashboard: charts come from matplotlib (familiar but barely interactive), Bokeh (powerful but… | ||
| Geoscience Big Data Computing with Python | Fei Zhang | 34m |
Geoscience Australia runs a petabyte-scale archive of 30+ years of Earth-observation imagery, organized into spatial partitions (now ~100 km cells) and indexed… | ||
| Data Science Design Patterns | Tennessee J Leeuwenburg | 28m |
Claims that most data‑science projects reduce to a few repeatable patterns and demonstrates concrete ones: adapter pattern for hiding messy… | ||
| Breaking up arrays up into chunks for fun and science with Xarray and Dask | Andrew Hicks | 28m |
Xarray plus Dask implement chunked, lazy n-dimensional processing of Landsat stacks: datasets are built from many scene files, registered in a Postgres-backed… | ||
| Building brains | Paula Sanz-Leon | 32m |
The Virtual Brain (TVB) is an open-source TVB Python library and client‑server web app that simulates whole‑brain electrical activity by coupling mesoscale… | ||
| Python's Bright Future in Science | Juan Nunez-Iglesias | 44m |
Python already displaces IDL/Matlab in papers, powered by NumPy/SciPy for things like three‑line quantile normalization and fast sparse linear algebra that… | ||
| Python for bridging between researchers and service operators: from CFFI to Jupyterhub | Kevin Plastow | 22m |
A Python wrapper called PySWIFT exposes a C++ hydrology engine (SWIFT v2) through its C API and CFFI, wrapping about 150 functions and sharing memory via numpy… | ||
| nothing matches | ||