PyData Amsterdam 2016
2016-03-12..2016-03-13
Amsterdam, Netherlands
[PyData Amsterdam 2016, PyData Amsterdam 2017, PyData Amsterdam 2018, PyData Amsterdam 2019, PyData Amsterdam 2024, PyData Amsterdam 2025]
| title | author | length |
|---|---|---|
| PyData Amsterdam 2016 | 04m | |
The page reports concrete wins: a face recognition tutorial that runs hands-on and produces working demos, ongoing PySpark API improvements born from a real… | ||
| The Duct Tape of Heroes: Bayes Rule | Vincent D Warmerdam | 39m |
Shows concrete claims and demos: uses pomegranate probabilistic programming, builds Bayesian probabilistic graphs for disease testing, model selection, feature… | ||
| More psychologists need to learn python | Titus von Köller | 28m |
Argues that programming makes psychological processes concrete by implementing a from scratch agent-based model in Python and IPython Notebook that simulates… | ||
| Jupyter: Notebooks in Multiple Languages for Data Science | Thomas Kluyver, Min Ragan Kelley | 43m |
Explains how a Python-centric notebook becomes a multi-language platform with 50+ kernels, concrete kernel types (native, wrapper, replica), and the 0MQ… | ||
| Measuring Search Engine Quality using Spark and Python | Sujit Pal | 26m |
A system built with Python and Apache Spark replays a reference set of 80,000+ user queries against Apache Solr configurations and computes Average Click Rank… | ||
| From Data Science to Production - deploy, scale, enjoy! | Sergii Khomenko | 39m |
PyData Amsterdam 2016
Description
Data cleaning is the first step of every Data Science project. Next one does Data Science. The talk covers a missing step… | ||
| Data driven literary analysis | Serena Peruzzo | 29m |
Works from 28 Shakespeare plays with ~11,000-word vocabulary, uses tf-idf to score terms, reduces dimensionality with LDA topic modelling (gensim) to represent… | ||
| Scaling Up Genomics with Spark | Sean Owen | 25m |
Genomics is framed as a data and compute problem: cost per genome falls to about a thousand dollars, and variant calling and recalibration improve when many… | ||
| Building a live face recognition system | Rodrigo Agundez | 01h 12m |
A runnable pipeline shows how to build live face recognition with OpenCV: video capture, release camera or crash, Haar cascades (Viola‑Jones with integral… | ||
| The PyData map: Presenting a map of the landscape of PyData tools | Peadar Coyle | 31m |
A practical map of the PyData landscape that names tools and shows where each actually helps: pure Python and toolz for small fast work, NumPy/Pandas for… | ||
| Networks meet Finance in Python | Miguel Vaz | 28m |
Correlation networks from real market data become a practical tool: code uses pandas, bokeh, networkx, ipywidgets to build correlation matrices, extract… | ||
| Winning Ways for Your Visualization Plays | Mark Grundland | 41m |
Argues that layout choices materially change interpretation, demonstrates how aspect ratio, color maps and projections mislead and how Mercator distortion vs… | ||
| Store and manage data effortlessly with HDF5 | Margaret Mahan | 36m |
Claims that HDF5 gives accessible compressed organized storage with real code and numbers: uses h5py and an IPython notebook to show a processing pipeline that… | ||
| CART: Not only Classification and Regression Trees | Marc Garcia | 40m |
Decision trees are simple but practical: a Python implementation that builds axis-aligned splits, uses min_samples_leaf=4 and max_depth to avoid overfitting… | ||
| Hybrid Recommender Systems in Python | Maciej Kula | 35m |
Claims a hybrid matrix‑factorization that embeds item and user metadata and therefore handles cold start, reducing parameters by sharing feature embeddings… | ||
| Gotta catch'em all: recognizing sloppy work in crowdsourcing tasks | Maciej Gryka | 25m |
Trains a random forest on hand‑labeled clicks keystrokes and network logs from tens of thousands of tests to flag lazy workers with about 90% accuracy. Uses a… | ||
| Tools and Tricks from a Pragmatic Data Scientist | Lucas Javier Bernardi | 40m |
Claims concrete wins: cosine→Euclidean via normalization lets KD/ball‑trees return exact kNN results (no approximation) and empirical timings on MNIST and up… | ||
| Closing Notes and Lightning Talks | 01h 16m | |
www.pydata.org
PyData is a gathering of users and developers of data analysis tools in Python. The goals are to provide Python enthusiasts a place to share… | ||
| Using random search for efficient hyper-parameters optimization with H2O | Jo-fai Chow | 24m |
Claims that random discrete search matches grid and manual search for model quality while using far less compute and cites the 2012 paper as evidence… | ||
| `from __past__ import print_statement`: a Dadaist Rejection of Python 2 vs 3 | James Powell | 20m |
Presents a working, pip-installable hack that patches a running CPython by inserting a runtime C-Python trampoline to override eval behavior, plus a module… | ||
| Improving PySpark Performance: Spark performance beyond the JVM | Holden Karau | 43m |
PySpark bottlenecks center on the double serialization cost between Python and the JVM and DataFrames move heavy work inside the JVM so Python code runs much… | ||
| The Role of Python in the Oil & Gas Industry | Giuseppe Pagliuca | 37m |
Python automates sensitivity and uncertainty workflows rather than just showing examples: a text‑file model generates 800 simulations automated from parameter… | ||
| Pandas: from bdate_range to wide_to_long | Giovanni Lanzani | 01h 21m |
Hands-on pandas tutorial that walks a Jupyter notebook (http://s.lanzani.nl/pydataamsterdam) through Series and DataFrame ops with working code… | ||
| the idea behind Automatic Relevance Determination and Bayesian Interpolation | Florian Wilhelm | 39m |
Explains the actual claim that Automatic Relevance Determination recovers sparse weights by placing independent precisions on each coefficient and selecting… | ||
| Realtime Bayesian A-B testing with Spark Streaming | Dennis Bohle, Ben Teeuwen | 37m |
Realtime pipeline prototype uses Spark Streaming with Kafka to feed live experiment data into a Bayesian engine that models conversions with a beta-binomial… | ||
| How big are your banks? Because the ones in the U.S. are pretty big! | David R Pugh | 38m |
Analyzes a 5+ GB FDIC balance sheet panel for Q1 1992–Q4 2015 using Python, Pandas, Scikit-learn, Bokeh with a GitHub Python scraper to bulk the raw reports… | ||
| Python based predictive analytics | Danny Bickson | 40m |
PyData Amsterdam 2016
One of the most exciting areas in data science is the development of new predictive applications; apps used to drive product… | ||
| Finding relations in documents with IEPY | Daniel Moisset | 30m |
IEPY is an open source Python system that combines a rule-based + machine learning hybrid pipeline with a document store, a learning engine, a web UI and… | ||
| Do Angry People Have Poor Grammar? | Ben Fields | 41m |
Uses a 1% sample of every public reddit comment (filtered to >=5 words, ~319,000 comments) and applies Python NLP tools: tokenization via NLTK, sentiment… | ||
| Machine Learning with Scikit-Learn | Andreas Mueller | 01h 17m |
Scikit-learn shows concrete recipes: tokenize with a CountVectorizer to build a 75,000-word vocabulary, vectorize text into sparse matrices, train a linear SVM… | ||
| Running (snippets of) Python in the browser | Almar Klein | 23m |
Three approaches to run Python in the browser are compared and concrete claims are given: PyScript is a transpiler that generates JavaScript, installs via… | ||
| NoSQL Python: making data frames work for you in a non-rectangular world | Aileen Nielsen | 34m |
Shows concrete techniques: use json_normalize and ijson to parse nested JSON and ragged CSV fields, subclass list-like column objects and swap class methods to… | ||
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