Training 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.

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.

10+ reasons people are attending ODSC East 2018

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A Few of Our 2018 Training Sessions and Workshops

More training sessions to be added soon!

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General Training Session: Machine Learning in R Part I with Jared Lander, Statistics Professor at Columbia University and Author of R for Everyone

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General Training Session: Getting Started with TensorFlow with Joshua Gordon, Machine Learning Lead at Google

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General Training Session: A Tour of Machine Learning Algorithms: The Usual Suspects in Some Unusual Applications with Dr. Kirk Borne, Principal Data Scientist, Executive Advisor at Booz Allen Hamilton

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General Training Session: Turning a Data Science Brain Dump into Software with Katie Malone, PhD, Data Science Director at Civis Analytics

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General Training Session: Plotly and Dash for Interactive Dashboards with Jose Portilla, Head of Data Science at Pierian Data Inc.

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General Training Session: Apache Spark for Data Science Part I with Adam Breindel Data Science Instructor and Consultant

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General Training Session: Fake News Detection with Machine Learning (ML) and Natural Language Processing (NLP) with Yunus Genes, PhD, Data Scientist / Data Science Instructor / Researcher at University of Central Florida

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General Training Session: Introduction to Text Analytics Using R with Ted Kwartler Data Scientist at Liberty Mutual

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General Training Session: Machine Learning in R Part I with Jared Lander, Statistics Professor at Columbia University and Author of R for Everyone

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General Training Session: Machine Learning in R Part II with Jared Lander, Statistics Professor at Columbia University and Author of R for Everyone

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General Training Session: NLP with Python: Linguistics with SpaCy, Topic Modeling, and word2vec for Word & Phrase Translation with Stefan Jansen, Founder and Lead Data Scientist at Applied Artificial Intelligence

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General Training Session: Geometric deep learning on graphs and manifolds – going beyond Euclidean data with Michael Bronstein Fellow, Radcliffe Institute for Advanced Study, Harvard University / Professor of Computer Science, USI Lugano

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General Training Session: Geometric deep learning on graphs and manifolds – going beyond Euclidean data with Federico Monti, Faculty of Infomatics at Institute of Computational Science

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General Training Session: Introduction to Python for Data Science with Skipper Seabold Data Science Director at Civis Analytics

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General Training Session: Synthesizing Data Visualization and User Experience with Mark Schindler Co-founder and Managing Director at GroupVisual.io

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General Training Session: Synthesizing Data Visualization and User Experience with Bang Wong, Creative Director of the Broad Institute of MIT and Harvard

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General Training Session: Apache Spark for Data Science Part II with Adam Breindel, Data Science Instructor and Consultant 

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General Training Session: Networking Analysis Made Simple with Eric Ma, Data Carpentry Instructor and Author of nxviz Package

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General Training Session: Advanced Data Analysis, Dashboards and Visualization Using Tableau with Nirav Shah, Founder at OnPoint Insights

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General Training Session: Advanced Data Analysis, Dashboards and Visualization Using Tableau with Priyanka Gagneja, Research Assistant at Boston College Lynch School

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General Training Session: Feature Engineering for Time Series Data with Michael Schmidt, PhD, Chief Scientist at DataRobot

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General Training Session: Feature Engineering for Time Series Data with Mark Steadman, PhD, Architect, Data Science Engineering at DataRobot

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General Training Session: Introduction to Machine Learning with Andreas Mueller, PhD, Core Contributor of scikit-learn and Author of Introduction to Machine Learning with Python

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General Training Session: Serverless Machine Learning with Tensorflow Part I with Carl Osipov, ML and Data Analytics Program Manager at Google

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General Training Session: Python Tutorial for Computational Finance with Fatena El-Masri, PhD, Senior Financial Analyst at FDIC

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General Training Session: Pre-trained models, Transfer Learning and Advanced Keras Features with Francesco Mosconi, PhD, Data Scientist, Consultant, and Trainer at CATALIT

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General Training Session: Getting to grips with the tidyverse (R) with Dr. Colin Gillespie, O’Reilly Author and R Training Academic at Jumping Rivers, Ltd.

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General Training Session: Practical Deep Learning with Dan Becker, PhD, Head of Kaggle Learn at Kaggle

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General Training Session: Introduction to Bayesian Workflow with Sean Talts, Core Developer of Stan

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General Training Session: Introduction to RMarkdown in Shiny with Jared Lander, Statistics Professor at Columbia University and Author of R for Everyone 

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General Training Session: Intermediate Machine Learning with scikit-learn with Andreas Mueller, PhD, Core Contributor of scikit-learn and Author of Introduction to Machine Learning with Python

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General Training Session: Serverless Machine Learning with Tensorflow Part II with Carl Osipov, ML and Data Analytics Program Manager at Google

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General Training Session: Optimizing Hadoop Environments with William Dailey, Senior Hadoop Engineer, Educator at Hortonworks

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General Training Session: Programming with Data: Python and Pandas with Daniel Gerlanc, President at Enplus Advisors Inc.

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General Training Session: Healthcare AI: Current Trends, Use Cases, Operationalizing and an Applied Walkthrough with John Langton, PhD, Director of Applied Data Science at Wolters Kluwer

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General Training Session: Healthcare AI: Current Trends, Use Cases, Operationalizing and an Applied Walkthrough with Krishna Srihasam, PhD, Senior Data Scientist at Wolters Kluwer Health 

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General Training Session: Healthcare AI: Current Trends, Use Cases, Operationalizing and an Applied Walkthrough with Zhixiang Luo, PhD, Applied Data Scientist at Wolters Kluwer Health

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General Training Session: Healthcare AI: Current Trends, Use Cases, Operationalizing and an Applied Walkthrough with Peter Miller, Senior Software Architect at Wolters Kluwer Health

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General Training Session: Generative Adversarial Networks: Models that Create Dan Becker, PhD, Head of Kaggle Learn at Kaggle

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General Training Session: Network/Graph Analysis in Python with Noemi Derzsy, PhD, Data Science Fellow at Insight Data Science

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General Training Session: Intermediate RMarkdown in Shiny with Jared Lander, Statistics Professor at Columbia University and Author of R for Everyone 

Any ticket grants access to even more training with many more additional workshops


  • Reducing Model Risk with Automated Machine Learning

  • How to Visualize Your Data: Beyond the Eye into the Brain

  • Matrix Math at Scale with Apache Mahout and Spark

  • Tutorial on Anomaly Detection at Scale: Data Engineering Challenges meet Data Science Difficulties

  • Crunching your Data with CatBoost – New Gradient Boosting Library

  • Crunching your Data with CatBoost – New Gradient Boosting Library

  • Deep Learning in Finance : An experiment and a reflection

  • Real-Time Machine Learning on the Mainframe

  • Power up your Computer Vision skills with TensorFlow-Keras

  • Bayesian Networks with pgmpy

  • Bayesian Hieratical Model for Predictive Analytics

  • Standardized Data Science: The Team Data Science Data Process – with a practical, example in Python

  • Interpretable Representation Learning for Visual Intelligence

  • Henosis – a generalizable, cloud-native Python form recommender framework for Data Scientists

  • Bayesian Statistics Made Simple

  • CNNs for Scene Classification in Videos

  • Accelerated mapping from the Sky: object detection with high resolution remote sensing images

  • Applications of Deep Learning in Aerospace and Building Systems

  • Democratise Conversational AI – Scaling Academic Research to Industrial Applications

  • Latest Developments in GANs

  • Multivariate Time Series Forecasting Using Statistical and Machine Learning Models

  • Networks and Large Scale Optimization

  • Blockchain and Data Governance – Validating Information for Data Science

  • Why Machine Learning needs its own language, and why Julia is the one

  • Machine Learning in Chainer Python

  • Buying Happiness – Using LSTMs to Turn Feelings into Trades

  • Multi-Paradigm Data Science

  • Agile Data Science 2.0

  • Keras for R

  • R Packages as Collaboration Tools

  • Uplift Modeling and Uplift Prescriptive Analytics: Introduction and Advanced Topics

  • Using AWS SageMaker, Kubernetes, and PipelineAI for High Performance, Hybrid-Cloud Distributed TensorFlow Model Training and Serving with GPUs

  • Deep Learning Methods for Text Classification

  • Applying Deep Learning to Article Embedding for Fake News Evaluation

  • Experimental Reproducibility in Data Science with Sacred

  • Visual Analytics for High Dimensional Data

  • Running Data Science Projects and integration within the Organizational Ecosystem

  • Data Science Learnathon. From Raw Data to Deployment: The Data Science Cycle with Knime

  • Salted Graphs – A (Delicious) Approach to Repeatable Data Science

  • A Primer on Neural Network Models for Natural Language Processing

  • Help! I have missing data. How do I fix it (the right way)?

  • Applying Color to Visual Analytics in Data Science

  • Under The Hood: Creating Your Own Spark Datasources

  • #NOBLACKBOXES: How To Solve Real Data Science Problems with Automation, Without Losing Transparency

  • Solving Real World Problems in Machine Learning and Data Science

  • The Power of Monotonicity to Make ML Make Sense

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