jorahn/pydata_berlin2016_materials

Collection of pointers to slides and repositories from speakers at PyData Berlin 2016

PyData Berlin 2016 Materials

Keynotes

Olivier Grisel, Predictive Modelling with Python

• http://ogrisel.github.io/decks/2016_pydata_berlin/
• https://github.com/ogrisel/docker-distributed

Julia Evans, How to trick a neural network

• http://jvns.ca/blog/2016/05/21/a-few-notes-from-my-pydata-berlin-keynote/

We McKinney, Python Data Ecosystem: Thoughts on Building for the Future

• http://de.slideshare.net/wesm/python-data-ecosystem-thoughts-on-building-for-the-future

Regular

Daniel Kirsch, Functional Programming in Python

• https://github.com/kirel/functional-python

Trent McConaghy, BigchainDB: a Scalable Blockchain Database, in Python

• https://github.com/bigchaindb/bigchaindb

David Higgins, Introduction to Julia for Python programmers

• https://github.com/daveh19/pydataberlin2016

Katharina Rasch, What every Data Scientist should know about data anonymization

• https://github.com/krasch/presentations/blob/master/pydata_Berlin_2016.pdf

Alexander Sibiryakov, Frontera: open source, large scale web crawling framework

• https://github.com/scrapinghub/frontera

Thomas Reineking, Plumbing in Python: Pipelines for Data Science Applications

• Yamal: Not yet Opensourced

Ryan Henderson, image-match: a python library for searching for similar images in large corpora

• https://github.com/ascribe/image-match

Ian Ozsvald, Statistically Solving Sneezes and Sniffles (a work in progress)

• https://speakerdeck.com/ianozsvald/statistically-solving-sniffles-step-by-step-a-work-in-progress
• http://ianozsvald.com/2016/05/07/statistically-solving-sneezes-and-sniffles-a-work-in-progress-report-at-pydatalondon-2016/

Felix Biessmann, Predicting Political Views From Text

• https://github.com/felixbiessmann/

Jie Bao, ExpAn - A Python Library for A/B Testing Analysis

• https://github.com/zalando/expan
• http://www.slideshare.net/JieBao3/expan-presentation-pydata-berlin-2016

Anne Matthies, Zero-Administration Data Pipelines using AWS Simple Workflow

• https://github.com/babbel/floto

Daniel Moisset, Bridging the gap: from Data Science to service

• https://github.com/machinalis/slides/tree/master/data-science-to-service

Katharine Jarmul, Holy D@t*! How to Deal with Imperfect, Unclean Datasets

• https://docs.google.com/presentation/d/1G-lgHKTdrqeeJhcvVmd7C9gOIfTRe429zhBN6lmKKzA/

Nora Neumann, Usable A/B testing – A Bayesian approach

• https://speakerdeck.com/nneu/b-testing-a-bayesian-approach

Frank Kaufer, Building Polyglot Data Science Platform on Big Data Systems

• https://speakerdeck.com/fkaufer/polyglot-data-science-platforms-on-big-data-systems

Lukasz Czarnecki, Brand recognition in real-life photos using deep learning

• http://de.slideshare.net/ukaszCzarnecki/brand-recognition-in-reallife-photos-using-deep-learning-lukasz-czarnecki-pydata-berlin-2016/

Edouard Fouché, Accelerating Python Analytics by In-Database Processing

• https://ibmdbanalytics.github.io/pydata-berlin-2016-ibmdbpy.slides.html

Delia Rusu, Estimating stock price correlations using Wikipedia

• https://speakerdeck.com/deliarusu/estimating-stock-price-correlations-using-wikipedia
• https://github.com/deliarusu/wikipedia-correlation

Jakob van Santen, The IceCube data pipeline from the South Pole to publication

• http://icecube.wisc.edu/~jvansanten/pasties/slides/2016-05-21%20PyData.pdf

Moritz Neeb, Bayesian Optimization and it's application to Neural Networks"

• https://slack-files.com/T18U1ASNQ-F1AHX36HG-22a535f1a2

Kashif Rasul, What's new in Deep Learning?

• https://bitly.com/new-deep-learning
• https://bitly.com/cifar10-resnet

Nathan Epstein, Machine Learning at Scale

• https://github.com/NathanEpstein/pydata-berlin

Ronert Obst and Dat Tran, PySpark in Practice

• http://pydata2016.cfapps.io/#/

Jose Quesada, A full Machine learning pipeline in Scikit-learn vs in scala-Spark: pros and cons

• https://files3.mixmaxusercontent.com/Qb5xzixaAsFjdsNbn/f/TZIGovHLNm7Is9Z5P/?messageId=lC47ZAHVG9riuwJkc&rn=Iibh1mclh2RgUnbpRkI&re=ISZk5ibpxmclJWLulmLul2dyFGZA5WYtJXZodmI

Martina Pugliese, Spotting trends and tailoring recommendations: PySpark on Big Data in fashion

• https://github.com/martinapugliese/talks_presentations/tree/master/pydata_berlin_2016

Angelos Kapsimanis, The Simple Leads To The Spectacular (Cancelled)

Anton Dubrau, Using small data in the client instead of big data in the cloud

• did not respond, yet

Nils Magnus, Dealing with TBytes of Data in Realtime

• did not respond, yet

Abhishek Thakur, Classifying Search Queries without User Click Data

• did not respond, yet

Jessica Palmer, Python and TouchDesigner for Interactive Experiments

• did not respond, yet

Maciej Gryka, Removing Soft Shadows with Hard Data

• did not respond, yet

Andreas Lattner, Setting up predictive analytics services with Palladium

• did not respond, yet

Andrej Warkentin, Visualizing FragDenStaat.de

• did not respond, yet

James Powell, The kwarg problem

• did not respond, yet

Matthew Honnibal, Designing spaCy: A high-performance natural language processing (NLP) library written in Cython

• did not respond, yet

Valentine Gogichashvili, Data Integration in the World of Microservices

• did not respond, yet

Michelle Tran Chain, Loop & Group: How Celery Empowered our Data Scientists to Take Control of our Data Pipeline

• did not respond, yet

Guertel Idai, Artificial Body Representation in Robots, Expectation and Surprise

• did not respond, yet

Robert Meyer, pypet: A Python Toolkit for Simulations and Numerical Experiments

• did not respond, yet

Juha Suomalainen, Visualizing research data: Challenges of combining different datasources

• did not respond, yet

Danny Bickson, Python based predictive analytics with GraphLab Create

• did not respond, yet

Fang Xu, Connecting Keywords to Knowledge Base Using Search Keywords and Wikidata

• did not respond, yet

Dr. Markus Abel, Python Learns to Control Complex Systems

• did not respond, yet

Tutorials

Frank Gerhardt, Using Spark - with PySpark

• https://gitlab.com/gerhardt.io/pyspark-workshop

Mike Müller, Single-source Python 2/3

• http://www.python-academy.com/download/pydatabln2016/Single_Source_Python_2_3.pdf

Katharine Jarmul, Data Wrangling with Python

• https://github.com/kjam/data-wrangling-pycon

Lev Konstantinovskiy, Practical Word2vec in Gensim

• https://github.com/RaRe-Technologies/movie-plots-by-genre

Shoaib Burq, Which city is the cultural capital of Europe? An introduction to Apache PySpark for GeoAnalytics

Lightning Talks

Oliver Zeigermann

• https://djcordhose.github.io/big-data-visualization/2016_pydata_berlin_lightning.html#/

Piotr Migdał, Teaching machine learning

• https://speakerdeck.com/pmigdal/teaching-machine-learning
• http://p.migdal.pl/2016/03/15/data-science-intro-for-math-phys-background.html

Mentioned tools:

• Pybuilder: Tired of writing setup.py? http://pybuilder.github.io/
• Sputnik: Package manager for Data https://github.com/spacy-io/sputnik