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 2019

See Reasons

A Few of Our 2019 Training and Workshop Session Speakers

More training sessions to be added soon!

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

Author, Lecturer, and Core contributor to scikit-learn, Columbia University

Instructor Bio


Adam Breindel consults and teaches widely on Apache Spark, big data engineering, and machine learning. He supports instructional initiatives and teaches as a senior instructor at Databricks, teaches classes on Apache Spark and on deep learning for O’Reilly, and runs a business helping large firms and startups implement data and ML architectures. Adam’s 20 years of engineering experience include streaming analytics, machine learning systems, and cluster management schedulers for some of the world’s largest banks, along with web, mobile, and embedded device apps for startups. His first full-time job in tech was on a neural-net-based fraud detection system for debit transactions, back in the bad old days when some neural nets were patented (!) and he’s much happier living in the age of amazing open-source data and ML tools today.

Adam Breindel

Apache Spark Expert, Data Science Instructor and Consultant

Instructor Bio


Matt currently leads instruction for GA’s Data Science Immersive in Washington, D.C. and most enjoys bridging the gap between theoretical statistics and real-world insights. Matt is a recovering politico, having worked as a data scientist for a political consulting firm through the 2016 election. Prior to his work in politics, he earned his Master’s degree in statistics from The Ohio State University. Matt is passionate about making data science more accessible and putting the revolutionary power of machine learning into the hands of as many people as possible. When he isn’t teaching, he’s thinking about how to be a better teacher, falling asleep to Netflix, and/or cuddling with his pug.

Matt Brems

Global Lead Data Science Instructor, General Assembly

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, Lecturer, Core Contributer of scikit-learn, Columbia Data Science Institute

Instructor Bio


Lukas Biewald is the founder and Chief Data Scientist of CrowdFlower. Founded in 2009, CrowdFlower is a data enrichment platform that taps into an on-demand to workforce to help companies collect training data and do human-in-the-loop machine learning.

Following his graduation from Stanford University with a B.S. in Mathematics and an M.S. in Computer Science, Lukas led the Search Relevance Team for Yahoo! Japan. He then worked as a senior data scientist at Powerset, acquired by Microsoft in 2008. Lukas was featured in Inc Magazine’s 30 Under 30 list.

Lukas is also an expert level Go player.

Lukas Biewald

Founder, Weights & Biases

Instructor Bio


Kirk Borne is a data scientist and an astrophysicist who has used his talents at Booz Allen since 2015. He was professor of astrophysics and computational science at George Mason University (GMU) for 12 years. He served as undergraduate advisor for the GMU data science program and graduate advisor in the computational science and informatics Ph.D. program.

Kirk spent nearly 20 years supporting NASA projects, including NASA’s Hubble Space Telescope as data archive project scientist, NASA’s Astronomy Data Center, and NASA’s Space Science Data Operations Office. He has extensive experience in large scientific databases and information systems, including expertise in scientific data mining. He was a contributor to the design and development of the new Large Synoptic Survey Telescope, for which he contributed in the areas of science data management, informatics and statistical science research, galaxies research, and education and public outreach.

Dr. Kirk Borne

Principal Data Scientist, Booze Allen Hamilton

Instructor Bio


Francesco Mosconi. Ph.D. in Physics and 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.

Francesco Mosconi, PhD

Data Scientist, Catalit

Instructor Bio


Daniel Gerlanc has worked as a data scientist for more than decade and written software professionally for 15 years. He spent 5 years as a quantitative analyst with two Boston hedge funds before starting Enplus Advisors. At Enplus, he works with clients on data science and custom software development with a particular focus on projects requiring an expertise in both areas. He teaches data science and software development at introductory through advanced levels. He has coauthored several open source R packages, published in peer-reviewed journals, and is active in local predictive analytics groups.

Daniel Gerlanc

President, Enplus Advisors Inc.

Instructor 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.

John Boersma

Director of Education, DataRobot

Instructor Bio


Joshua Cook is a mathematician. He writes code in Bash, C, and Python and has done pure and applied for computational work in geospatial predictive modeling, quantum mechanics, semantic search, and artificial intelligence. He also has ten years experience teaching mathematics at the secondary and post-secondary level. His research interests lie in high-performance computing, interactive computing, feature extraction, and reinforcement learning. He is always willing to discuss orthogonality or to explain why Fortran is the language of the future over a warm or cold beverage.

Joshua Cook

Curriculum Designer, Databricks

Instructor Bio


Michael Mahoney is at the University of California at Berkeley in the Department of Statistics and at the International Computer Science Institute (ICSI). He works on algorithmic and statistical aspects of modern large-scale data analysis. Much of his recent research has focused on large-scale machine learning, including randomized matrix algorithms and randomized numerical linear algebra, geometric network analysis tools for structure extraction in large informatics graphs, scalable implicit regularization methods, and applications in genetics, astronomy, medical imaging, social network analysis, and internet data analysis. He received him PhD from Yale University with a dissertation in computational statistical mechanics, and he has worked and taught at Yale University in the mathematics department, at Yahoo Research, and at Stanford University in the mathematics department. Among other things, he is on the national advisory committee of the Statistical and Applied Mathematical Sciences Institute (SAMSI), he was on the National Research Council’s Committee on the Analysis of Massive Data, he runs the biennial MMDS Workshops on Algorithms for Modern Massive Data Sets, and he spent fall 2013 at UC Berkeley co-organizing the Simons Foundation’s program on the Theoretical Foundations of Big Data Analysis.

Michael Mahoney, PhD

Statistics Professor, UC Berkeley

Instructor Bio


For more than 20 years, Todd has been highly respected as both a technologist and a trainer. As a tech, he has seen that world from many perspectives: “data guy” and developer; architect, analyst and consultant. As a trainer, he has designed and covered subject matter from operating systems to end-user applications, with an emphasis on data and programming. As a strong advocate for knowledge sharing, he combines his experience in technology and education to impart real-world use cases to students and users of analytics solutions across multiple industries. He is a regular contributor to the community of analytics and technology user groups in the Boston area, writes and teaches on many topics, and looks forward to the next time he can strap on a dive mask and get wet. Todd is a Data and Business Systems Consultant, and is the former Director of Boston Operations for the Open Data Science Conference.

Todd Cioffi

Data Science Evangelist, DataRobot

Instructor 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.

Jeffrey Yau, PhD

Chief Data Scientist, AllianceBernstein

Instructor Bio


Naveen is a Senior Software Engineer and a member of Amazon AI at AWS and works on Apache MXNet. He began his career building large scale distributed systems and has spent the last 10+ years designing and developing it. He has delivered various Tech Talks at AMLC, Spark Summit, ApacheCon and loves to share knowledge. His current focus is to make Deep Learning easily accessible to Software Developers without the need for a steep learning curve. In his spare time, he loves to read books, spend time with his family and watch his little girl grow.

Naveen Swamy

Software Developer, Amazon AI – AWS

Instructor Bio


Douglas Blank is a professor of Computer Science at Bryn Mawr College outside of Philadelphia, PA. He has been working with neural networks for over 20 years, and developing easy to use software for even longer. He is one of the core developers of ConX.

Douglas Blank

Professor of Computer Science | Core Developer of ConX, Bryn Mawr College

Instructor Bio


Kishore K. Reddy is a Staff Research Scientist at the United Technologies Research Center (UTRC) working in the area of computer vision, human machine interaction (HMI) and machine learning. He is currently leading the Digital Initiative at UTRC primarily focusing on Deep Learning applications in aerospace and building systems to perform outliers and anomalies detection, multi-modal sensor fusion and data compression. Kishore earned his Ph.D. in 2012 from University of Central Florida, where he developed advanced video and image analysis algorithms, primarily segmentation and classification approaches, for multiple contracts funded by DARPA, IARPA, and NIH.

Kishore Kumar Reddy

Staff Research Engineer, United Technologies Research Center

Instructor Bio


Zachary Lipton is a mad scientist at Amazon AI and assistant professor at Carnegie Mellon University (2018-). He researches ML methods, applications (especially to healthcare), and social impacts. In addition to corralling deep neural neurons and starting fires on Twitter (@zacharylipton), he is the editor of the Approximately Correct blog and lead author of Deep Learning – The Straight Dope, an interactive book teaching deep learning and MXNet Gluon through Jupyter notebooks.

Zachary Chase Lipton

Data Scientist, Associate Professor, Amazon AI | Carnegie Mellon

Instructor Bio


Utkarsh Contractor is the Director of AI at Aisera, where he leads the data science team working on machine learning and artificial intelligence applications in the fields of Natural Language Processing and Vision. He is also pursuing his graduate degree at Stanford University, focussing his research and experiments on computer vision, using CNNs to analyze surveillance scene imagery and footages. Utkarsh has a decade of industry experience in Information Retrieval and Machine Learning working at companies such as LinkedIn and AT&T Labs.

Utkarsh Contractor

ML and AI Director, Aisera Inc.

Instructor Bio


Andrew Long is a Data Scientist at Fresenius Medical Care North America (FMCNA). Andrew holds a PhD in biomedical engineering from Johns Hopkins University and a Master’s degree in mechanical engineering from Northwestern University. Andrew joined FMCNA last year after participating in the Insight Health Data Fellows Program. At FMCNA, he is responsible for building predictive models using machine learning to improve the quality of life of every patient who receives dialysis from FMCNA. He is currently creating a model to predict which patients are at the highest risk of imminent hospitalization.

Andrew Long, PhD

Data Scientist, Fresenius Medical Care

Instructor Bio


Conor Jensen is an experienced Data and Analytics executive who has spent over a decade working in the analytics space across multiple industries, as both a consumer and developer of analytics solutions. He currently works as a Customer Success Manager at Domino Data Lab, helping customers make the most of their Data Science platform and guiding them through building teams and processes to be successful. Previously he has successfully built out analytics functions at multiple insurance companies. This includes building out data and analytics platforms, Business Intelligence capabilities, and Predictive Analytics serving both internal and external customers.

Conor Jensen

Customer Success Manager, Domino Data Lab

Instructor Bio


Haftan Eckholdt, PhD. is Chief Data Science Office at Plated. His career began with research professorships in Neuroscience, Neurology, and Psychiatry followed by industrial research appointments at companies like Amazon and AIG. He holds graduate degrees in Biostatistics and Developmental Psychology from Columbia and Cornell Universities. In his spare time he thinks about things like chess and cooking and cross country skiing and jogging and reading. When things get really really busy, he actually plays chess and cooks delicious meals and jogs a lot. Born and raised in Baltimore, Haftan has been a resident of Kings County, New York since the late 1900’s.

Haftan Eckholdt, PhD

Chief Data Science & Chief Science Officer,

Instructor Bio


Jacob Schreiber is a fifth year Ph.D. student and NSF IGERT big data fellow in the Computer Science and Engineering department at the University of Washington. His primary research focus is on the application of machine larning methods, primarily deep learning ones, to the massive amount of data being generated in the field of genome science. His research projects have involved using convolutional neural networks to predict the three dimensional structure of the genome and using deep tensor factorization to learn a latent representation of the human epigenome. He routinely contributes to the Python open source community, currently as the core developer of the pomegranate package for flexible probabilistic modeling, and in the past as a developer for the scikit-learn project. Future projects include graduating.

Jacob Schreiber

PhD Candidate, University of Washington

Instructor Bio


Jane Herriman is Director of Diversity and Outreach at Julia Computing and a PhD student at Caltech. She is a Julia, dance, and strength training enthusiast and is excited for the opportunity to teach you Julia.

Jane Herriman

Director of Diversity and Outreach, Julia Computing

Instructor Bio


Laura Norén is a data science ethicist and researcher currently working in cybersecurity at Obsidian Security in Newport Beach. She holds undergraduate degrees from MIT, a PhD from NYU where she recently completed a postdoc in the Center for Data Science. Her work has been covered in The New York Times, Canada’s Globe and Mail, American Public Media’s Marketplace program, in numerous academic journals and international conferences. Dr. Norén is a champion of open source software and those who write it.

Laura Norén, PhD

Director of Research, Professor, Obsidian Security, NYU Stern School of Business

Instructor Bio


Scott Haines is a Principal Software Engineer / Tech Lead on the Voice Insights team at Twilio. His focus has been on the architecture and development of a real-time (sub 250ms), highly available, trust-worthy analytics system. His team is providing near real-time analytics that processes / aggregates and analyzes multiple terabytes of global sensor data daily. Scott helped drive Apache Spark adoption at Twilio and actively teaches and consulting teams internally. Scott’s past experience was at Yahoo! where he built a real-time recommendation engine and targeted ranking / ratings analytics which helped serve personalized page content for millions of customers of Yahoo Games. He worked to build a real-time click / install tracking system that helped deliver customized push marketing and ad attribution for Yahoo Sports and lastly Scott finished his tenure at Yahoo working for Flurry Analytics where he wrote the an auto-regressive smart alerting and notification system which integrated into the Flurry mobile app for ios/android.

Scott Haines

Principal Software Engineer, Twilio

Instructor Bio


Ted Petrou is the author of Pandas Cookbook and founder of both Dunder Data and the Houston Data Science Meetup group. He worked as a data scientist at Schlumberger where he spent the vast majority of his time exploring data. Ted received his Master’s degree in statistics from Rice University and used his analytical skills to play poker professionally and teach math before becoming a data scientist.

Ted Petrou

Founder, Dunder Data

Instructor 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.

Anna Veronika Dorogush

ML Lead, Yandex

Instructor Bio


Yunus Genes is completing his Masters in Computer Science, and continuing his part time PhD at University of Central Florida. His research is focused on Applied Machine Learning, social media behavior, misinformation detection/diffusion. He has been working on this field over 4 years. His is currently working on a DARPA funded project to simulate social media under SocialSim project, teaching Data Science to Fortune 50 Company professionals and he has previously held Data Science positon at Silicon Valley as well as Florida, Orlando area.

Yunus Genes, PhD

Data Scientist, Royal Caribbean

Instructor Bio


Rajiv Shah is a data scientist at DataRobot, where his primary focus is helping customers improve their ability to make and implement predictions. Previously, Rajiv has been part of data science teams at Caterpillar and State Farm. He has worked on a variety of projects from a wide ranging set of areas including supply chain, sensor data, acturial ratings, and security projects. He has a PhD from the University of Illinois at Urbana-Champaign.

Rajiv Shah, PhD

Data Scientist, Data Robot

Instructor Bio


Anirudh is the Head of AI & Research at Aira (Visual interpreter for the blind), and was previously at Microsoft AI & Research where he founded Seeing AI – Talking camera app for the blind community. He is also the co-author of the upcoming book, ‘Practical Deep Learning for Cloud and Mobile’. He brings over a decade of production-oriented Applied Research experience on Peta Byte scale datasets, with features shipped to about a billion people. He has been prototyping ideas using computer vision and deep learning techniques for Augmented Reality, Speech, Productivity as well as Accessibility. Some of his recent work, which IEEE has called ‘life changing’, has been honored by CES, FCC, Cannes Lions, American Council of the Blind, showcased at events by White House, House of Lords, World Economic Forum, on Netflix, National Geographic, and applauded by world leaders including Justin Trudeau and Theresa May.

Anirudh Koul

Head of AI & Research, Aira

Instructor Bio


Anish Das Sarma is a seasoned industry leader with over 15 years of software engineering experience, and deep knowledge of machine-learning, data integration, and information extraction.

Having taken Trooly from inception until its acquisition by Airbnb, Anish has demonstrated significant leadership and entrepreneurial abilities , keen sense of business strategy and vision, and a proven ability to hire, build and manage large engineering organizations.

With a PhD in Computer Science from Stanford University, research experience at Google and Yahoo’s research labs, and a gold medal from IIT Bombay, Anish has a clear track record of academic excellence in solving challenging data problems. Anish has published over 40 research papers in top academic conferences, authored a book on data integration, and filed 10 patents.

Anish Das Sarma, PhD

Engineering Manager, Airbnb

Instructor Bio


Sean is the Head of Technical Product Management at DigitalGlobe helping build GBDX and next generation machine learning tools for satellite imagery. Sean received his PhD from George Mason University as the Provost’s High Potential Research Candidate, Fisher Prize winner and an INFORMS Dissertation Prize recipient.

Sean Patrick Gorman, PhD

Head of Technical Product Management, DigitalGlobe

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

Director of Developer Relations, DigitalGlobe

A Sample of Previous East 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

Sign Up for ODSC East 2019 | April 30-May 3

Register Now
Open Data Science Conference