Training & Workshop Sessions

– Taught by World-Class Data Scientists –

Learn the latest data science concepts, tools, and techniques from the best. Forge a connection with these rock stars from industry and academia, who are passionate about molding the next generation of data scientists.

Highly Experienced Instructors

Our instructors are highly regarded in data science, coming from both academia and renowned companies.

Real World Applications

Gain the skills and knowledge to use data science in your career and business, without breaking the bank.

Cutting Edge Subject Matter

Find training sessions offered on a wide variety of data science topics from machine learning to data visualization to DevOps.

Training, Workshops & Tutorials Sessions

ODSC Europe 2019 will host training and workshop session on some of the latest and in-demand technique, models and frameworks including:

Training Focus Areas

  • Deep Learning and Reinforcement Learning

  • Machine Learning, Transfer Learning and Adversarial Learning 

  • Computer Vision

  • NLP, Speech, Text Analytics and Spatial Analysis

  • Data Visualization

Quick Facts

  • Choose from 25 Training sessions

  • Choose from 30+ workshops

  • Hands-on training session are 4 hours in duration

  • Workshops and tutorial are 2 hours in duration

Frameworks

  • TensorFlow, PyTorch, and MXNet

  • Scikit-learn, PyMC3, Pandas, Theano, NLTK, NumPy, SciPy

  • Kera, Apache Spark, Apache Storm, Airflow, Apache Kafka

  • Kubernetes, Kubeflow, Apache Ignite, Hadoop

Europe 2019 Confirmed Instructors

Training Sessions

More sessions added weekly

Instructor Bio

Dr. Colin Gillespie is Senior lecturer (Associate Professor) at Newcastle University, UK. His research interests are high performance statistical computing and Bayesian statistics. He is regularly employed as a consultant by Jumping Rivers and has been teaching R since 2005 at a variety of levels, ranging from beginners to advanced programming.

Dr. Colin Gillespie

Senior Lecturer at Newcastle University

Instructor Bio

Dr. Jiahang Zhong is the leader of the data science team at Zopa, one of UK’s earliest fintech company. He has broad experience of data science projects in credit risk, operational optimization and marketing, with keen interests in machine learning, optimization algorithms and big data technologies. Prior to Zopa, he worked as a PhD and Postdoctoral researcher on the Large Hadron Collider Project at CERN, with focus on data analysis, statistics and distributed computing

Jiahang Zhong, PhD

Head of Data Science at Zopa Ltd

Training: All The Cool Things You Can Do With Postgresql To Next Level Your Data Analysis

The intention of this VERY hands on workshop is to get you introduced and playing with some of the great features you never knew about in PostgreSQL. You know, and probably already love, PostgreSQL as your relational database. We will show you how you can forget about using ElasticSearch, MongoDB, and Redis for a broad array of use cases. We will add in some nice statistical work with R embedded in PostgreSQL. Finally we will bring this all together using the gold standard in spatial databases, PostGIS. Unless you have a specialized use case, PostgreSQL is the answer. The session will be very hands on with plenty of interactive exercises.

By the end of the workshop participants will leave with hands on experience doing:
Spatial Analysis
JSON search
Full Text Search
Using R for stored procedures and functions
All in PostgreSQL

Instructor Bio

Steve is the Developer Relations lead for DigitalGlobe. He goes around and shows off all the great work the DigitalGlobe engineers do. Steve has a Ph.D. in Ecology from University of Connecticut.

Steven Pousty, PhD

Director of Developer Relations at Crunchy Data

Instructor Bio

Jared Lander is the Chief Data Scientist of Lander Analytics a data science consultancy based in New York City, the Organizer of the New York Open Statistical Programming Meetup and the New York R Conference and an Adjunct Professor of Statistics at Columbia University. With a masters from Columbia University in statistics and a bachelors from Muhlenberg College in mathematics, he has experience in both academic research and industry. His work for both large and small organizations ranges from music and fund raising to finance and humanitarian relief efforts.
He specializes in data management, multilevel models, machine learning, generalized linear models, data management and statistical computing. He is the author of R for Everyone: Advanced Analytics and Graphics, a book about R Programming geared toward Data Scientists and Non-Statisticians alike and is creating a course on glmnet with DataCamp.

Jared Lander

Chief Data Scientist, Author of R for Everyone, Professor at Lander Analytics, Columbia Business School

Training: Introduction to Machine Learning

Machine learning has become an indispensable tool across many areas of research and commercial applications. From text-to-speech for your phone to detecting the Higgs boson, machine learning excells at extracting knowledge from large amounts of data. This talk will give a general introduction to machine learning, as well as introduce practical tools for you to apply machine learning in your research. We will focus on one particularly important subfield of machine learning, supervised learning. The goal of supervised learning is to “”learn”” a function that maps inputs x to an output y, by using a collection of training data consisting of input-output pairs. We will walk through formalizing a problem as a supervised machine learning problem, creating the necessary training data and applying and evaluating a machine learning algorithm. The talk should give you all the necessary background to start using machine learning yourself.

Instructor Bio

Andreas Mueller received his MS degree in Mathematics (Dipl.-Math.) in 2008 from the Department of Mathematics at the University of Bonn. In 2013, he finalized his PhD thesis at the Institute for Computer Science at the University of Bonn. After working as a machine learning scientist at the Amazon Development Center Germany in Berlin for a year, he joined the Center for Data Science at the New York University in the end of 2014. In his current position as assistant research engineer at the Center for Data Science, he works on open source tools for machine learning and data science. He is one of the core contributors of scikit-learn, a machine learning toolkit widely used in industry and academia, for several years, and has authored and contributed to a number of open source projects related to machine learning.

Andreas Mueller, PhD

Author, Research Scientist, Core Contributor of scikit-learn at Columbia Data Science Institute

Training: TFX: Production ML Pipelines with TensorFlow

Putting machine learning models into production is now mission critical for every business – no matter what size.

TensorFlow is the industry-leading platform for developing, modeling, and serving deep learning solutions. But putting together a complete pipeline for deploying and maintaining a production application of AI and deep learning is much more than training a model. Google has taken years of experience in developing production ML pipelines and offered the open source community TensorFlow Extended (TFX), an open source version of tools and libraries that Google uses internally.

Learn what’s involved in creating a production pipeline, and walk through working code in an example pipeline with experts from Google. You’ll be able to take what you learn and get started on creating your own pipelines for your applications.

Instructor Bio

Coming soon!

Robert Crowe

TensorFlow Developer Advocate at Google

Training: Opening The Black Box -- Interpretability In Deep Learning

The recent application of deep neural networks to long-standing problems has brought a break-through in performance and prediction power. However, high accuracy often comes at the price of loss of interpretability, i.e. many of these models are black-boxes that fail to provide explanations on their predictions. This tutorial focuses on illustrating some of the recent advancements in the field of interpretable artificial intelligence. We will show some common techniques that can be used to explain predictions on pretrained models and that can be used to shed light on their inner mechanisms. The tutorial is aimed to strike the right balance between theoretical input and practical exercises. The tutorial has been designed to provide the participants not only with the theory behind deep learning interpretability, but also to offer a set of frameworks, tools and real-life examples that they can implement in their own projects.

Instructor Bio

Matteo is a Research Staff Member in Cognitive Health Care and Life Sciences at IBM Research Zürich. He’s currently working on the development of multimodal deep learning models for drug discovery using chemical features and omic data. He also researches in multimodal learning techniques for the analysis of pediatric cancers in a H2020 EU project, iPC, with the aim of creating treatment models for patients. He received his degree in Mathematical Engineering from Politecnico di Milano in 2013. After getting his MSc he worked in a startup, Moxoff spa, as a software engineer and analyst for scientific computing. In 2019 he obtained his doctoral degree at the end of a joint PhD program between IBM Research and the Institute of Molecular Systems Biology, ETH Zürich, with a thesis on multimodal learning approaches for precision medicine.

Matteo Manica, PhD

Research Staff Member at Cognitive Health Care & Life Sciences, IBM Research Zürich

Workshop Sessions

More sessions added weekly

Instructor Bio

Alex Peattie is the co-founder and CTO of Peg, a technology platform helping multinational brands and agencies to find and work with top YouTubers. Peg is used by over 1500 organisations worldwide including Coca-Cola, L’Oreal and Google.

An experienced digital entrepreneur, Alex spent six years as a developer and consultant for the likes of Grubwithus, Huckberry, UNICEF and Nike, before joining coding bootcamp Makers Academy as senior coach, where he trained hundreds of junior developers. Alex was also a technical judge at this year’s TechCrunch Disrupt conference.

Alex Peattie

Co-founder, CTO at Peg

Instructor Bio

Avik Sengupta has worked on risk and trading systems in investment banking for many years, mostly using Java interspersed with snippets of the exotic R and K languages. This experience left him wondering whether there were better things out there. Avik’s quest came to a happy conclusion with the appearance of Julia in 2012. He has been happily coding in Julia and contributing to it ever since.

Avik Sengupta

VP of Engineering at Julia Computing

Instructor Bio

Jesús (aka J.) is a lead data scientist with a strong background and interest in insight, based on simulation, hypothesis generation and predictive analytics. He has substantial practical experience with statistical analysis, machine learning and optimisation tools in product development and innovation, finance, media and others.

Jesús is a Director of Data Science at Barclays. He has a background in physics and held positions both in academia and industry, including Imperial College, IBM Data Science Studio, Prudential and Dow Jones to name a few. Jesús is the author of “Essential MATLAB and Octave,” a book for students in physics, engineering, and other disciplines. He also authored the upcoming data science book entitled “Data Science and Analytics with Python.”

Jesus Rogel-Salazar, PhD

Director of Data Science at Barclays

Instructor Bio

Dr. Schulz is an Assistant Professor of Laboratory Medicine and computational health care researcher at Yale School of Medicine. He received a PhD in Microbiology, Immunology, and Cancer Biology and an MD from the University of Minnesota. He is the Director of Informatics for the Department of Laboratory Medicine, Director of the CORE Center for Computational Health, and Medical Director of Data Science for Yale New Haven Health System. Dr. Schulz has over 20 years’ experience in software development with a focus on enterprise system architecture and has a research interests in the management of large, biomedical data sets and the use of real-world data for predictive modeling. At Yale, he has led the implementation of a distributed data analysis and predictive modeling platform, for which he received the Data Summit IBM Cognitive Honors award. Other projects within his research group include computational phenotyping and the development of clinical prescriptive models for precision medicine initiatives. His clinical areas of expertise include molecular diagnostics and transfusion medicine, where he has ongoing work assessing the use, safety, and efficacy of pathogen-reduced blood products.

Wade Schulz, MD, PhD

Assistant Professor and Director of Computational Health at Yale School of Medicine

Instructor Bio

Yuriy Guts is a Machine Learning Engineer at DataRobot with over 10 years of industry experience in data science and software architecture. His primary interests are productionalizing data science, automated machine learning, time series forecasting, and processing spoken and written language. He teaches AI and ML at UCU, competes on Kaggle, and has led multiple international data science and engineering teams.

Yuriy Guts

Machine Learning Engineer at DataRobot

Tutorial Sessions

More sessions added weekly

Instructor Bio

Olga Isupova received the Specialist (eq. to M.Sc.) degree in applied mathematics and computer science in 2012 from Lomonosov Moscow State University, Moscow, Russia, and the Ph.D. degree in 2017 from the University of Sheffield, Sheffield, U.K. She is a Research Assistant in machine learning with the Department of Engineering Science, University of Oxford, Oxford, U.K. Her research interests include machine learning for disaster response and environment protection, Bayesian nonparametrics, and anomaly detection.

Olga Isupova, PhD

Principal Researcher at University of Oxford, Machine Learning Research Group

Instructor Bio

Alan Rutter is the founder of consultancy Fire Plus Algebra, and is a specialist in communicating complex subjects through data visualisation, writing and design. He has worked as a journalist, product owner and trainer for brands and organisations including Guardian Masterclasses, WIRED, Time Out,the Home Office, the Biotechnology and Biological Sciences Research Council and Liverpool School of Tropical Medicine.

Alan Rutter

Founder at Fire Plus Algebra

Tutorial: The Perks of On Board Deep Learning: Train, Deploy and Use Neural Nets on a Raspberry Pi

Machine learning applications are new in the software development landscape, and tend to be hard to build. As Google noted in an article (source : https://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems.pdf), it is mainly because the application is much broader than the model itself. Surprisingly though, Machine Learning applications follow a double Pareto’s law. On the one hand, 80% of the time spent on building those applications deals with machine learning problems whereas 20% of the remaining time is spent on integrating the model to a running application. On the other hand, only 20% of the code lines are specific to machine learning ; the vast rest is about integration and run.
I would like to first explore the foundations of this trend, to then show why it kills machine learning application development and sustainability.
In order to illustrate the tips and tricks of shipping a deep learning model to production, I would use a live demo of a model designed to recognised car drawings via the camera of a Raspberry Pi. It would allow me to. Furthermore, by identifying the main stages of the application life cycle (training, deploying and using), I will lay the emphasis on the common mistakes one does not bear in mind to make a successful machine learning product.

Instructor Bio

Constant is strongly interested in the creation of value out of data and helps those who believe in such a potential by accelerating their transition toward a data driven company. In order to address these new problematics, he focuses on mastering every skill of a complete Data Geek : architecture expertise (data, applications, network), data science mastering (statistical learning, data visualisation, algorithmic theory), customer and business understanding (model prediction consumption, business metrics, customer needs).

Constant has been working for about two years for OCTO Technology. He is an an expert in the industry sector and works on several types of mission, ranging from predictive maintenance of production site, to prediction of critical KPIs in video games, via real time monitoring of manufacturing devices. Prior to joining OCTO, Constant was working as a researcher in data61 (formerly known as NICTA), the best research institute in ICT in Australia on applying Machine Learning to profile GUI users and provide the best amount of information to help them make a decision based on a machine learning prediction

Constant Bridon

Data Science Consultant at OCTO Technology

ODSC EUROPE 2019 | November 19th – 22nd

Register Now

What To Expect

As we prepare our 2019 schedule, take a look at some of the previous training and workshops we have hosted at ODSC Europe for an idea of what to expect.

  • High Performance, Distributed Spark ML, Tensorflow AI, and GPU

  • Machine Learning with R

  • Deep Learning with Tensorflow for Absolute Beginners

  • Algorithmic Trading with Machine and Deep Learning

  • Deep Learning in Keras

  • Deep Learning – Beyond the Basics

  • Running Intelligent Applications inside a Database: Deep Learning with Python Stored Procedures in SQL

  • Distributed Deep Learning on Hops

  • R and Spark with Sparklyr

  • Towards Biologically Plausible Deep Learning

  • Deep Learning Ensembles in Toupee

  • A Gentle Introduction to Predictive Analytics with R

  • Deep Learning with Tensorflow for Absolute Beginners

  • Graph Data – Modelling and Quering with Neo4j and Cypher

  • Introduction to Data Science with R

  • Introduction to Python in Data Science

  • High Performance, Distributed Spark ML, TensorFlow AI, and GPU

  • The Magic of Dimensionality Reduction

  • Analyze Data, Build a UI and Deploy on the Cloud with Apache Spark, Notebooks and PixieDust

  • Data Science for Executives

  • Data Science Learnathon. From Raw Data to Deployment: the Data Science Cycle with KNIME

  • Distributed Deep Learning on Hops

  • Drug Discovery with KNIME

  • Interactive Visualisation with R (and just R)

  • Introduction to Algorithmic Trading

  • Introduction to Data Science – A Practical Viewpoint

  • Julia for Data Scientists

  • Running Intelligent Applications inside a Database: Deep Learning with Python Stored Procedures in SQL

  • Telling Stories with Data

  • Towards Biologically Plausible Deep Learning

  • Win Kaggle Competitions Using StackNet Meta Modelling Framework