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 rockstars from industry and academic, who are passionate about molding the next generation of data scientists.

West 2018

Highly Experienced Instructors

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

ODSC Training Includes

Form a working relationship with some of the world’s top data scientists for follow up questions and advice.

Additionally, your ticket includes access to 50+ talks and workshops.

High quality recordings of each session, exclusively available to premium training attendees.

Equivalent training at other conferences costs much more.

Professionally prepared learning materials, custom tailored to each course.

Opportunities to connect with other ambitious like-minded data scientists.

2018 Training Instructors

We have some of the top names in data science siged up to host  training and workshops sessions.  More instructors will be added weekly.

Andreas Mueller, PhD, Author, Lecturer, and Core contributor to scikit-learn

Bio

Andreas is lecturer at the Data Science Institute at Columbia University and author of the O’Reilly book “Introduction to machine learning with Python,” which describes a practical approach to machine learning with python and scikit-learn. He is one of the core developers of the scikit-learn machine learning library, and he have been co-maintaining it for several years. Andreas is also a Software Carpentry instructor. In the past, he worked at the NYU Center for Data Science on open source and open science, and as Machine Learning Scientist at Amazon. Andreas’s mission is to create open tools to lower the barrier of entry for machine learning applications, promote reproducible science and democratize the access to high-quality machine learning algorithms.

Jared Lander, Statistics Professor at Columbia University and Author of R for Everyone

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.

Yves Hilpisch, PhD, Founder at Quant University, Lecturer, and Author of Derivatives Analytics with Python and Python for Finance

Bio

Yves has a Ph.D. in Mathematical Finance and is the founder and managing partner of The Python Quants GmbH. He is also the author of the books Python for Finance, Derivatives Analytics with Python and Listed Volatility & Variance Derivatives. He lectures for Data Science at htw saar University of Applied Sciences and for Computational Finance at the CQF Program and is the organizer of the Python for Quant Finance Meetup in London.

Michael Schmidt, PhD, Chief Scientist at DataRobot

Bio

Michael Schmidt is the Chief Scientists at DataRobot, and has been featured in the Forbes list of the world’s top 7 data scientists and MIT’s list of the most innovative 35-under-35. He has authored AI research in the journal Science and has appeared in media outlets such as the New York Times, NPR’s RadioLab, the Science Channel, and Communications of the ACM. In 2011, Michael founded Nutonian and led the development Eureqa, a machine learning application and service used by over 80,000 users and later acquired by DataRobot in 2017. Most recently, his work has focused on automated machine learning, feature engineering, and time series prediction

Jeffrey Yau, PhD, Chief Data Scientist at Alliance Bernstein

Bio

Jeffrey is the Chief Data Scientist at AllianceBernstein, a global investment firm managing over $500 billions. He is responsible for building and leading the data science group, partnering with investment professionals to create investment signals using data science, and collaborating with sales and marketing teams to analyze clients. Graduated with a Ph.D. in economics from the University of Pennsylvania, he has also taught statistics, econometrics, and machine learning courses at UC Berkeley, Cornell, NYU, the University of Pennsylvania, and Virginia Tech. Previously, Jeffrey held advanced analytic positions at Silicon Valley Data Science, Charles Schwab Corporation, KPMG, and Moody’s Analytics.

Anna Veronika Dorogush, ML Lead at Yandex

Bio

Anna Veronika Dorogush graduated from the Faculty of Computational Mathematics and Cybernetics of Lomonosov Moscow State University and from Yandex School of Data Analysis. She used to work at ABBYY, Microsoft, Bing and Google, and has been working at Yandex since 2015, where she currently holds the position of the head of Machine Learning Systems group and is leading the efforts in development of the CatBoost library.

Alan Rutter, Content Consultant, Trainer, Co-Founder at Clever Boxer

Bio

Alan Rutter is the co-founder of consultancy Clever Boxer. He first worked with infographics as a magazine journalist (Time Out, WIRED), before moving into technology roles (Condé Nast, Net-A-Porter) and then training and development (The Guardian, General Assembly). He has taught data visualisation techniques to thousands of students, and for organisations including the Home Office, Department of Health, Biotechnology and Biosciences Research Council, Capita, Novartis and Kings College London.

Marios Michailidis, Ranked #1 On Kaggle.Com, Data Scientist at H20.ai

Bio

Marios Michailidis is a Research data scientist at H2O.ai . He holds a Bsc in accounting Finance from the University of Macedonia in Greece and an Msc in Risk Management from the University of Southampton. He has also nearly finished his PhD in machine learning at University College London (UCL) with a focus on ensemble modelling. He has worked in both marketing and credit sectors in the UK Market and has led many analytics’ projects with various themes including: Acquisition, Retention, Recommenders, Uplift, fraud detection, portfolio optimization and more.

He is the creator of KazAnova(http://www.kazanovaforanalytics.com/), a freeware GUI for credit scoring and data mining 100% made in Java as well as is the creator of StackNet Meta-Modelling Framework (https://github.com/kaz-Anova/StackNet). In his spare time he loves competing on data science challenges and was ranked 1st out of 500,000 members in the popular Kaggle.com data competition platform. Here (http://blog.kaggle.com/2016/02/10/profiling-top-kagglers-kazanova-new-1-in-the-world/) is a blog about Marios being ranked at the top in Kaggle and sharing his knowledge with tricks and ideas

Evanthia Dimara, Research Scientist at the Institute for Intelligent Systems and Robotics

Bio

Evanthia Dimara is a postdoctoral research scientist at the Institute for Intelligent Systems and Robotics (ISIR) Laboratory (HCI group) of Sorbonne University. Her fields of research are human-computer interaction and information visualization. Her focus is on decision making — how to help people make unbiased and informed decisions alone or in groups.

Alex Peattie, co-founder and CTO of Peg

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 2000 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 the 2017 TechCrunch Disrupt conference.

Douglas Ashton, PhD

Bio

Doug is a Senior Data Scientist at Mango Solutions. A statistical physicist by training, he now practices and teaches a wide range of data science disciplines from machine learning pipelines to graph theory.

John Boersma, PhD, Director of Education at DataRobot

Bio

John Boersma is Director of Education for DataRobot. In this role he oversees the company’s client training operations and relations with academic institutions using DataRobot in analytics courses. Previously, John founded and led Adapt Courseware, an adaptive online college curriculum venture. John holds a PhD in computational particle physics and an MBA in general management.

Tsung-Hsien Wen, Co founder and Chief Scientist at PolyAI

Bio

Tsung-Hsien (Shawn) Wen is a co-founder and Chief Scientist of PolyAI, a London-based startup looking to use the latest developments in NLP and ML to create a general platform for deploying spoken dialogue systems. He holds a Ph.D. from the Dialogue Systems group, University of Cambridge, where he worked with Professor Steve Young. His research focuses on language generation and end-to-end dialogue modelling, specifically in learning to generate responses for task-oriented dialogue systems. He was the tutor of the “Deep Learning and NLG” tutorial at INLG 2016 and has given invited seminars to research groups at Google, Apple, Xerox, and Baidu China. Before PolyAI, He was the invited lecturer for Samsung’s corporate training course in Warsaw, a research consultant at IPSoft Amelia team, and a research intern at Google Brain. He received best paper awards at EMNLP 2015 and SigDial 2015.

ODSC EUROPE 2018 | September 19-22

Register Now

What To Expect

As we prepare out 2018 schedule take a look at some of our 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

  • Machine Learning with R

  • 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

  • Machine Learning with R

  • 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

  • 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

  • Machine Learning with R

  • R and Spark with Sparklyr

  • 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

Open Data Science Conference