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 R Part I with 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.

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General Training Session: Machine Learning in Part II with 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.

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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 and Executive Advisor at Booz Allen Hamilton

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Dr. Kirk Borne is the Principal Data Scientist and an Executive Advisor at global technology and consulting firm Booz Allen Hamilton (since 2015). He previously spent 12 years as Professor of Astrophysics and Computational Science at George Mason University where he taught and advised students in the graduate and undergraduate Data Science degree programs. Before that, he worked 18 years on NASA projects in various roles, developing and managing large data systems for space science research, including program manager in NASA’s Space Science Data Operations Office and Data Archive Project Scientist for the Hubble Space Telescope. He has a PhD in Astronomy from Caltech and a BS in Physics from LSU. In 2014 he was named an IBM Big Data and Analytics Hero, and in 2016 he was elected Fellow of the International Astrostatistics Association. He is an active contributor on social media, where he has been named consistently among the top worldwide influencers in big data and data science since 2013.

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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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Fatena El-Masri, PhD is a Senior Financial Analyst at FDIC, with many years of experience in financial engineering, applied statistics, and quantitative risk modeling and management (including market, credit and operational risk). For her Computational Science and Informatics PhD dissertation research at GMU, she used Python and agent-based models to study the stability of the banking network and to identify instability conditions for failing banks.

While Fatena was working for an assignment with the Royal Australian Navy, she managed a Proof of Concept (POC) Artificial Intelligence (AI) project for the Australian Maritime Warfare Center, using IBM Watson, Watson Explorer, and Blue Prism for Natural Language Processing, Robotic Process Automation (RPA), & Video Analytics. She coded Python & TensorFlow for video analytics, and provided oversight management of the RPA contract to automate processes.

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

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Katie Malone is the Director of Data Science Research and Development at Civis Analytics, a data science software and services company. At Civis she leads the Data Science Research and Development department, which tackles some of Civis’ most novel and challenging data science consulting engagements as well as writing the core data science code that powers the Civis Data Science Platform. A physicist by training, Katie spent her PhD searching for the Higgs boson at CERN and is also the instructor for Udacity’s Introduction to Machine Learning course. As a side project she hosts a weekly podcast about data science and machine learning, Linear Digressions.

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

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Skipper is a Director of Data Science at Civis Analytics, a data science technology and advisory firm. He leads a team of data scientists from all walks of life from physicists and engineers to statisticians and computer scientists. He is an economist by training, and has a decade of experience working in the Python data community. He started and led the statsmodels Python project, was formerly on the core pandas team, and has contributed to many projects in Python data stack.

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

Bio

Andreas is a lecturer at the Data Science Institute at Columbia University and author of the O’Reilly book “Introduction to machine learning with Python”, describing 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 has been co-maintaining it for several years. He is also a Software Carpentry instructor. In the past, Andreas worked at the NYU Center for Data Science on open source and open science, and as Machine Learning Scientist at Amazon. 

His 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.

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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

Bio

Andreas is a lecturer at the Data Science Institute at Columbia University and author of the O’Reilly book “Introduction to machine learning with Python”, describing 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 has been co-maintaining it for several years. He is also a Software Carpentry instructor. In the past, Andreas worked at the NYU Center for Data Science on open source and open science, and as Machine Learning Scientist at Amazon. 

His 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.

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

Bio

Andreas is a lecturer at the Data Science Institute at Columbia University and author of the O’Reilly book “Introduction to machine learning with Python”, describing 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 has been co-maintaining it for several years. He is also a Software Carpentry instructor. In the past, Andreas worked at the NYU Center for Data Science on open source and open science, and as Machine Learning Scientist at Amazon. 

His 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.

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

Bio

Andreas is a lecturer at the Data Science Institute at Columbia University and author of the O’Reilly book “Introduction to machine learning with Python”, describing 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 has been co-maintaining it for several years. He is also a Software Carpentry instructor. In the past, Andreas worked at the NYU Center for Data Science on open source and open science, and as Machine Learning Scientist at Amazon. 

His 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.

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General Training Session: Introduction to TensorFlow Lattice for Interpretable Machine Learning with Maya Gupta, PhD, Glassbox ML R&D Team Lead at Google

Bio

Maya Gupta leads Google’s Glassbox Machine Learning R and D team, which focuses on designing and developing controllable and interpretative machine learning algorithms that solve Google product needs. Prior to Google, Gupta was an Associate Professor of Electrical Engineering at the University of Washington from 2003-2013. Her PhD is from Stanford, and she holds a BS EE and BA Econ from Rice. Gupta founded and runs the wooden jigsaw puzzle company, Artifact Puzzles.

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

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Mark Schindler is co-founder and Managing Director of GroupVisual.io. For over 15 years, he has designed user-interfaces for analytic software products and mobile apps for clients ranging from Fortune 50 companies to early-stage startups. In addition to design services, Mark and his team mentor startup companies and conduct workshops on data visualization, analytics and user-experience design

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General Training Session: A Gentle Introduction to Predictive Analytics with R with Dr. Colin Gillespie, Author of Efficient R Programming, R Trainer and Consultant, and Senior Lecturer at Newcastle University

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.

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

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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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Francesco Mosconi has a PhD in Physics and is a Data Scientist at Catalit LLC. Instructor at Udemy. Formerly co-founder and Chief Data Officer at Spire, a YC-backed company that invented the first consumer wearable device capable of continuously tracking respiration and physical activity. Machine Learning and python expert. Also served as Data Science lead instructor at General Assembly and The Data incubator.

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

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Ted Kwartler is a Data Scientist at Liberty Mutual. He advocates for and integrates customer innovation into everyday culture and work. He helps to define and organize all customer service functions and key performance indicators. Thus, he incorporates data-driven customer analytics decisions balanced with qualitative feedback to continuously innovate for the customer experience. Specialties: Statistical forecasting and data mining, IT service management, customer service process improvement and project management, business analytics.

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

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

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General Training Session: Deep Learning with Python using Keras and TensorFlow with Jose Portilla, Head of Data Science at Pierian Data Inc.

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General Training Session: Learn D3 essentials to get started with web based data visualization with Jan Willem Tulp, Data Experience Designer at Tulp Interactive

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General Training Session: Geometric deep learning on graphs and manifolds – going beyond Euclidean data with Michael Bronstein, Computer Science professor at Università della Svizzera italiana (Switzerland), Tel Aviv University

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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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Workshop: Deep Learning with Apache MxNEt with Zachary Chase Lipton, Research Scientist at Amazon

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Workshop: Under The Hood: Creating Your Own Spark Datasources with Jayesh Thakrar, Senior Software Engineer at Conversant Inc

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Workshop: Matrix Math at Scale with Apache Mahout and Spark with Andrew Musselman, Committer, PMC Member at Apache Mahout

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Workshop: Reducing Model Risk with Automated Machine Learning with Seph Mard, Head of Model Validation at DataRobot

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Workshop: Applying Color to Visual Analytics in Data Science with Theresa-Marie Rhyne, Visualization Consultant 

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Workshop: Tutorial on Anomaly Detection at Scale: Data Engineering Challenges meet Data Science Difficulties with Dusan Randjelovic, Senior Data Scientist at Smartcat.io

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Workshop: Deep Learning methods for Medical Image Classification with Natalia Antropova, PhD Candidate at University of Chicago

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Workshop: How to visualize your data: beyond the eye into the brain with Evanthia Dimara, PhD Candidate at INRA

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Workshop: Running Data Science Projects and integration within the Organizational Ecosystem with Cameron Sim, CEO at CrewSpark

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Workshop: Intelligent price optimization in retail with Sergii Shelpuk, Head of Data Science Office at Eleks

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Workshop: Machine Learning in Chainer Python with Crissman Loomis, Engineer/Business Development at Preferred Networks

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Workshop: Experimental Reproducibility in Data Science with Sacred with Karthik Rajasethupathy, Data Scientist at HBC

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Workshop: Applications of Deep Learning in Aerospace and Building Systems with Kishore Kumar Reddy, Staff Research Engineer at United Technologies Research Center

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Workshop: Multi-Paradigm Data Science with Erez Kaminski, Technology Specalist at Wolfram Reserach

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Workshop: Bayesian Hierarchical Models for Predictive Analysis with Amir Meimand, Director of Science, Research and Development at Zilliant

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Workshop: Networks and large scale optimization with José Bento, Assistant Professor at Boston College

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Workshop: Solving Real World Problems in Machine Learning and Data Science with Dan Shiebler, Machine Learning Modeling Engineer at Twitter

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Workshop: Human-in-the-loop Probabilistic Graphical Modeling with Karthik Dinakar, Research Assistant at MIT Media Lab

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Workshop: Help! I have missing data. How do I fix it (the right way)? with Matt Brems, Global Lead Data Science Instructor Engineer at General Assembly

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Workshop: Visual Analytics for High Dimensional Data with Shenghui Cheng, Research Scientist at Shenzhen Research Institute of Big Data

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Workshop: Deep Learning in Finance: An experiment and A reflection with Miquel Noguer Alonso, Ph.D, Executive Director at UBS

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Workshop: Deep learning on graphs with Xavier Bresson, Ph.D, Professor of Computer Science at NTU, Singapore

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Workshop: Applying Deep Learning to Article Embedding for Fake News Evaluation with Michael Tamir, PhD, Head of Data Science at Uber

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Workshop: Deep Learning Methods for Text Classification with Garrett Hoffman, Senior Data Scientist at StockTwits

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Workshop: A Primer on Neural Network Models for Natural Language Processing with Sri Krishnamurthy, President, Chief Data Scientist at QuantUniversity.com

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Workshop: Multivariate Time Series Forecasting Using Statistical and Machine Learning Models with Jeffrey Yau, PhD, Chief Data Scientist at AllianceBernstein

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Workshop: Using AWS SageMaker, Kubernetes, and PipelineAI for High Performance, Hybrid-Cloud Distributed TensorFlow Model Training and Serving with GPUs with Chris Fregly, Founder at PipelineAI

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Workshop: R Packages as Collaboration Tools with Stephanie Kirmer, Data Scientist at Uptake

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Workshop: Keras for R with Douglas Ashton, PhD, Principal Data Scientist at Mango Solutions

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Workshop: Uplift Modeling and Uplift Prescriptive Analytics: Introduction and Advanced Topics with Victor Lo, PhD, Head of Data Science & Artificial Intelligence at Fidelity Investments

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Workshop: From Stored Data to Data Stories: Building Data Narratives with Open-Source Tools

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Workshop: Agile Data Science 2.0 with Russell Jurney, Principal Consultant at Data Syndrome

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Workshop: The Magic of Dimensionality Reduction with Alex Peattie, Co-founder, CTO at Peg

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Workshop: Latest Developments in GANs with Seth Weidman, Senior Data Scientist at Metis

BIO

Ted Kwartler is a Data Scientist at Liberty Mutual. He advocates for and integrates customer innovation into everyday culture and work. He helps to define and organize all customer service functions and key performance indicators. Thus, he incorporates data-driven customer analytics decisions balanced with qualitative feedback to continuously innovate for the customer experience. Specialties: Statistical forecasting and data mining, IT service management, customer service process improvement and project management, business analytics.

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Real-Time Machine Learning on the Mainframe with Charles Aydin, PhD, Senior Lab Services Engineer at Rocket Software

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Workshop: Buying Happiness – Using LSTMs to Turn Feelings into Trades with Maxwell Margenot, Data Science Lead at Quantopian

BIO

Ted Kwartler is a Data Scientist at Liberty Mutual. He advocates for and integrates customer innovation into everyday culture and work. He helps to define and organize all customer service functions and key performance indicators. Thus, he incorporates data-driven customer analytics decisions balanced with qualitative feedback to continuously innovate for the customer experience. Specialties: Statistical forecasting and data mining, IT service management, customer service process improvement and project management, business analytics.

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

BIO

Ted Kwartler is a Data Scientist at Liberty Mutual. He advocates for and integrates customer innovation into everyday culture and work. He helps to define and organize all customer service functions and key performance indicators. Thus, he incorporates data-driven customer analytics decisions balanced with qualitative feedback to continuously innovate for the customer experience. Specialties: Statistical forecasting and data mining, IT service management, customer service process improvement and project management, business analytics.

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Workshop: Democratise Conversational AI – Scaling Academic Research to Industrial Applications with Tsung-Hsien Wen, Chief Scientist at PolyAI

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Workshop: Power up your Computer Vision skills with TensorFlow-Keras with Mo Patel, AI Researcher

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Workshop: Why Machine Learning needs its own language, and why Julia is the one with Viral Shah, PhD, Co-founder, CEO at Julia Computing, Inc.

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Workshop: Interpretable Representation Learning for Visual Intelligence with Bolei Zhou, AI Researcher at MIT

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Workshop: CNNs for Scene Classification in Videos with Utkarsh Contractor, ML and AI Director at Aisera Inc.

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Workshop: Blockchain and Data Governance – Validating Information for Data Science with Wade Schulz, PhD, Senior Solution Architect at Yale School of Medicine

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Workshop: Bayesian Networks with pgmpy with Harish Kashyap, Chief Data Scientist at Colaberry

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Workshop: Crunching your Data with CatBoost – New Gradient Boosting Library with Anna Veronika Dorogush, ML Lead at Yandex

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Workshop: Standardized Data Science: The Team Data Science Data Process – with a practical, example in Python with Buck Woody, Applied Data Scientist at Microsoft

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Buck Woody works at Microsoft Research, using data and technology to solve business and science problems. With over thirty years of professional and practical experience in computer technology, he is also a popular speaker at many conferences around the world; the author of over 700 articles and seven books on databases, machine learning and R, teaches and sits on the Data Science Board at the University of Washington, and specializes in data analysis techniques.

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


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