ODSC Europe 2017

ODSC Europe 2017

Presented By The Open Data Science Conference
The Open Data  Science Conference
The Open Data Science Conference

Presentation Description

On October 12th, ODSC Europe 2017 brought together some of the best and brightest minds currently working in the field of applied data science and AI. Over 3 days speakers and workshop presenters delivered key insights and takeaways around fundamental topics including, machine learning, deep learning, predictive analytics, quant finance, data visualization, AI research, the business of AI, and many more.

This comprehensive collection of more than 70 talks and workshops will give you a deeper understanding of the current topics, tools, and trends in data science and accelerate your knowledge in this rapidly evolving field. 

Presentation Curriculum

Focus Area - Open Data Science
Open Data Science Talk and Tutorial Details
A Gentle Introduction to Predictive Analytics with R - Dr. Colin Gillespie, Newcastle University
Data Science: Time for Professionalism? - Neil Lawrence, PhD, Amazon Research, University of Sheffield
The Future of Data Science: Less Game of Thrones, More Alliances - David Taieb, IBM Watson
Weather and Climate Data: Not Just for Meteorologists - Margriet Groenendijk, Developer Advocate at IBM
Notebooks for Developers - Glynn Bird, Developer Advocate at IBM
Introduction to Bayesian Analysis in Python - Peadar Coyle, Elevate
Bayesian A/B testing - Marc Garcia, Senior Data Scientists at Badoo
Apache Ignite: The In-Memory Hammer in Your Data Science Toolkit - Akmal Chaudri, PhD, GridGain
Next Generation Service Management with ITOA (Operations Analytics) - Rajesh Radhakrishnan, CSRA
Assessing Approaches to Anomaly Detection - Richard Perry, PhD, Data Scientist at SolarWinds
From Data Science to Data Stories: Automating Advanced Analytics for R&D and Manufacturing - Guido Smits, PhD, DataStories Int.
Writing Style Detection Using Word Embeddings
Learning from User Interactions - Rishabh Mehrotra, Top Conferences
What, How and Why: Perceptron - Caspar Wylie, Developer of Perceptron
Data Driven User Value Enhancement in a Big Internet Company - Pawel Ladyzynski, Naspers Classifieds
Agile Data Science 2.0 - Russell Jurney, Data Syndrome
Shipping Your Analysis: Open Containerization Tooling for Web-Users Bringing On-Demand Computation - Phil Weir, PhD, Flax and Teal Limited
TDD-ing a Bayesian Classifier - Robert Hardy, PhD, MAN GLG
Uncovering Complex Causes from Observational Data - Samantha Kleinberg, PhD, Stevens Institute of Technology
Visualisation for Data Science: Advances and Opportunities in Visualisation Research - Cagatay Turkay, PhD, City, University of London
On Constructing Cyber-Analytics - Niall Adams, PhD, Imperial College
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Workshops on Deep Learning/Machine Learning, Data Science for Quant Finance and More
Workshop Details
Drug Discovery with KNIME - Dr. Michael Mazanetz, Novadata Solutions Ltd.
Graph Data – Modelling and Quering with Neo4j and Cypher - Iryna Feuerstein, PRODYNA AG
Telling Stories with Data - Alan Rutter, General Assembly, Clever Boxer
Interactive Visualisation with R (and just R) - Martin Hadley, University of Oxford
PipelineAI High Performance, Distributed Spark ML, Tensorflow AI, and GPU - Chris Fregly, PipelineAI
Deep Learning with Tensorflow for Absolute Beginners - Kaz Sato, Google and Matthias Feys, Datatonic
Deep Learning in Keras - Leonardo De Marchi, Badoo
Running Intelligent Applications inside a Database: Deep Learning with Python Stored Procedures in SQL - Dr. Miguel Gonzalez-Fierro, Microsoft UK
Distributed Deep Learning on Hops - Fabio Buso and Robin Andersson, RISE SICS
Deep Learning Ensembles in Toupee - Alan Mosca, PhD, Sendence
Machine Learning with R - Jared Lander, Columbia University
Data Science Learnathon. From Raw Data to Deployment: the Data Science Cycle with KNIME - Rosaria Silipo, PhD, KNIME.com
Data Science for Executives - Tom de Godoy, DataRobot
Introduction to Algorithmic Trading - Max Margenot, Quantopian
Julia for Data Scientists - Avik Sengupta, Julia Computing, Inc
Analyze Data, Build a UI and Deploy on the Cloud with Apache Spark, Notebooks and PixieDust - David Taieb, IBM Watson
Win Kaggle Competitions Using StackNet Meta Modelling Framework - Marios Michaelidis, H2O.ai
Algorithmic Trading with Machine and Deep Learning - Yves Hilpisch, PhD, Founder of PyQuants
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