PyCon Australia 2015 Science/Data Miniconf
2015-07-31
Brisbane, Australia
[PyCon Australia 2013, PyCon Australia 2014, PyCon Australia 2014 Science & Data miniconf, PyCon Australia 2015, PyCon Australia 2015 Science/Data Miniconf, PyCon Australia 2015 Education Miniconf]
who it's for
scientific Python developers, data scientists, machine learning engineers, research software engineers, data engineers, devops engineers, web developers
topics
interactive 3D visualization with pyqtgraph; pydicom DICOM processing; docopt command line scripting; Bullet physics engine integration; language-agnostic TCP/HTTP/ZeroMQ APIs; scikit-learn random forest models; median imputation for NaNs; out-of-bag cross-validation; pandas feature engineering; Blaze and Odo data abstraction; doit Python task runner; Django dashboards with CKAN data
| title | author | length |
|---|---|---|
| Custom Python Applications in Neuroscience | Simon Salinas, Gagan Sharma | 20m |
Claim: a tiny Python stack builds usable neuroscience tools. Code uses pyqtgraph to render interactive 3D views and turns a messy folder of DICOMs into… | ||
| A Scientific Game Engine in the Cloud | Oliver Nagy | 22m |
A game-engine-for-engineers claims accurate Newtonian physics, a microservice cloud design and a language-agnostic API served over TCP/HTTP/ZeroMQ… | ||
| Adventures in scikit-learn's Random Forest | Gregory Saunders | 36m |
Uses scikit-learn RandomForestRegressor on an ASX equities dataset, cleans NaNs with median imputation and replaces all-NaN features with zeros, and constrains… | ||
| Not Invented Here: Porting Scientific Software to Python | Andrew Walker | 32m |
Shows a concrete migration story: a MATLAB conformal‑mapping port stalls after five files then a one‑character conjugate‑transpose bug, so the practical… | ||
| An End-to-End Machine Learning Ecosystem in a Quarter | Chris Hausler | 35m |
Claims a working end-to-end pipeline built in roughly a quarter that trains and serves per-account models at scale: uses pandas, scikit-learn, flask and hadoop… | ||
| Blaze: crunching data in different formats through a single API. | Lex Hider | 33m |
Blaze gives a pandas/numpy-like API that runs the same expression on CSVs, SQL or NoSQL back ends; Odo moves data between formats (JSON, SQL, CSV, Python… | ||
| Automated Scientific Pipelines With Doit | Sam Hames | 25m |
doit is a Python task manager that claims to replace Make with pure Python task definitions and can run as a CLI or via its API. examples show a dodo.py that… | ||
| Predicting sports winners using data analytics with pandas and scikit-learn | Robert Layton | 39m |
Uses pandas to pull box scores from basketball-reference and scikit-learn to build models; creates features like last-game result, win streak, head-to-head… | ||
| Karaage Gardening | Brian May | 18m |
Karaage is an open source Python/Django app that replaces hand‑managed cluster accounts; the migration yields concrete fixes: an aggregating daily table cuts… | ||
| Government dashboards and open data. | Paul Haesler | 22m |
Builds dashboards for NSW and federal agencies using Django backend and a pure JavaScript front end talking to a small REST API, with widgets defined by… | ||
| nothing matches | ||