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 the industry and academia, who are passionate about molding the next generation of data scientists.

Beginner to Advanced Level Training

From the Leading Instructors in the Industry

Machine Learning

  • Meta-learning for Machine Learning

  • Self Supervised learning; new techniques

  • Federated Learning for Data Privacy

  • Explainable AI and Bias in machine learning

  • Machine Learning at Scale using Apache Spark

  • Safety & Robustness in Machine Learning Modeling

  • Semi-supervised learning

  • Causal Inference with Machine Learning

Deep Learning

  • Deep Reinforcement learning

  • Deep Learning with PyTorch & Tensorflow

  • Deep Learning Deep Dive

  • Computer Vision 1/2 Day Training

  • Deep Learning with Keras

  • Introduction to Deep learning

  • Deepfakes Tutorial

  • Graph Representation Learning

NLP

  • Self Supervised learning; new techniques

  • Transfer Learning in NLP

  • Introduction to NLP and Topic Modeling

  • NLP Pre-trained Transformer Models with Bert, Ernie,, and GPT-2

  • State-of-the-Art NLP with PyTorch and Tensorflow

  • Semi-supervised learning

  • Hugging Face Transformer Library Workshop

  • Applications of NLP; Sentiment Analysis, Dialog Systems, and Semantic Search

ADDITIONAL TUTORIALS & WORKSHOPS

  • Machine Learning for Cyber Security

  • Real-time Streaming Analytics

  • MLOps and Machine Learning Pipelines

  • Introduction to Machine Learning Using scikit-learn

  • Auto Machine Learning (AutoML)

  • Distributed Machine Learning

  • Introduction to Data Analysis with Python Pandas

  • Machine Learning Workflow with Kubeflow & Kubernetes

Some of Our Current Data Science Training Instructors

Professor for Data ScienceUniversity of South Westphalia

Thomas Kopinski, PhD

Thomas is a professor in data science and machine learning living in Essen, Germany. His research focus is in applied machine learning and deep learning with a specification in object detection and bias mitigation. Currently, he is leading two research projects, one in embedded in the field of predictive maintenance for automobile part manufacturing. The other project is embedded in the field skill-based people analytics within the context of deconstruction of nuclear power plants in the context of energy transition. He is passionate about teaching in data science and machine learning with a focus on data-based ML systems real-time applications.

Dealing with Bias in Machine Learning(Tutorial)

Advanced ResearcherInria

Julie Josse, PhD

Julie Josse is a senior researcher in statistics and machine learning applied to health at Inria, a French research institute in digital sciences, and Professor at Ecole Polytechnique (Paris). She is an expert in the treatment of missing values (inference, multiple imputation, matrix completion, MNAR, supervised learning with missing values) and has created a website on the topic (https://rmisstastic.netlify.app/) for users. Her research also focuses on causal inference techniques (causal inference with missing values, combining RCT and observational data) for personalized medicine. Julie Josse is dedicated to reproducible research with R statistical software: she has developed packages including FactoMineR and missMDA to transfer her work.

Research Director | Director, Scikit-learnINRIA

Gaël Varoquaux, PhD

Gaël Varoquaux is a research director working on data science and health at Inria (French Computer Science National research). His research focuses on using data and machine learning for scientific inference, with applications to health and social science, as well as developing tools that make it easier for non-specialists to use machine learning. He has long applied it to brain-imaging data to understand cognition. Years before the NSA, he was hoping to make bleeding-edge data processing available across new fields, and he has been working on a mastermind plan building easy-to-use open-source software in Python. He is a core developer of scikit-learn, joblib, Mayavi and nilearn, a nominated member of the PSF, and often teaches scientific computing with Python using the scipy lecture notes.

 

Data Science ManagerGreenhouse Software

Mona Khalil

Mona is a Data Science Manager at Greenhouse Software in New York City, where they contribute to data-informed decision making across the company and machine learning solutions to improve the hiring process for Greenhouse customers. They’ve previously worked in government, creating analytics and machine learning solutions to improve the lives of New Yorkers, and continue to be involved in civic projects through a number of volunteer and non-profit organizations. They’ve also been a statistics and data science educator with DataCamp, Emeritus, and in university settings. They hold a graduate degree in Developmental Psychology, and are passionate about contributing to the ethical use of data science methodology in the public and private sector.

Senior Software EngineerQuansight Labs

Thomas Fan

Thomas J. Fan is a Senior Software Engineer at Quansight Labs, working to sustain and evolve the PyData open-source ecosystem. He is a maintainer for scikit-learn, an open-source machine learning library written for Python. Previously, he worked at Columbia University, improving the interoperability between scikit-learn and AutoML systems. Thomas holds a Masters in Physics from Stony Brook University and a Masters in Mathematics from New York University.

 

Author of nxviz Package

Eric Ma, PhD

Eric is an Investigator at the Novartis Institutes for Biomedical Research, where he solves biological problems using machine learning. He obtained his Doctor of Science (ScD) from the Department of Biological Engineering, MIT, and was an Insight Health Data Fellow in the summer of 2017. He has taught Network Analysis at a variety of data science venues, including PyCon USA, SciPy, PyData, and ODSC, and has also co-developed the Python Network Analysis curriculum on DataCamp. As an open-source contributor, he has made contributions to PyMC3, matplotlib, and bokeh. He has also led the development of the graph visualization package nxviz, and a data cleaning package pyjanitor (a Python port of the R package).

Network Analysis Made Simple(Half-Day Training)

ML LeadYandex

Anna Veronika Dorogush

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.

 

Professor | Founding Director | Co-founder and CTOStatistics and Data Science, MIT | IkigaiLabs

Devavrat Shah, PhD

Devavrat Shah is Andrew (1956) and Erna Viterbi Professor with the department of Electrical Engineering and Computer Science at Massachusetts Institute of Technology. He is the founding director of Statistics and Data Science at MIT. He is also a member of IDSS, LIDS, CSAIL and ORC at MIT. He co-founded Celect, Inc. (now part of Nike) in 2013 to help retailers decide what to put where by accurately predicting demand using omni-channel data. He is a co-founder and CTO of IkigaiLabs with the mission to build self-driving organizations by enabling data-driven operations with human-in-the-loop. His research focuses on statistical inference and stochastic networks. His contributions span a variety of areas including resource allocation in communications networks, inference and learning on graphical models, algorithms for social data processing including ranking, recommendations and crowdsourcing and more recently causal inference. He has made foundational contributions to the development of “gossip” protocols and “message-passing” algorithms for statistical inference which have been the building blocks of modern distributed data processing systems.His work spans a range of areas across electrical engineering, computer science and operations research. His work has received broad recognition, including prize paper awards in Machine Learning, Operations Research and Computer Science, and career prizes including 2010 Erlang prize from the INFORMS Applied Probability Society, awarded bi-annually to a young researcher who has made outstanding contributions to applied probability. He is a distinguished alumni of his alma mater IIT Bombay from where he graduated with the honor of President of India Gold Medal. His work has been covered in popular press including NY Times, Forbes, Wired and Reditt.

Senior Data ScientistGreenhouse Software

Andrew Zirm, PhD

Andrew is a Ph.D. Astrophysicist who made the switch from academia to data science (via the Insight Data Science program) in 2014. He was the first data scientist hired at Greenhouse Software where he has worked on many internal data science projects and a few customer-facing data-powered product features. Andrew lives in New Jersey with his wife and son.

 

Software Engineer Anyscale

Stephanie Wang

Stephanie is a final-year PhD student at UC Berkeley and a software engineer at Anyscale. She is interested in abstractions for distributed computing and problems in fault tolerance. Towards this end, she is also a maintainer for the open-source project Ray, which provides a simple, universal API for building distributed applications in Python.

Associate ProfessorSyracuse University

Jeffrey Saltz, PhD

Since joining Syracuse University in 2014, Jeff has focused on how to effectively manage and coordinate data science projects within and across teams. Via his research and consulting, Jeff has become an expert in this field, having worked across a range of organizations and published 30+ peer-reviewed academic papers that (1) explore the challenges in executing data science projects, and (2) evaluate potential frameworks via experiments and real-world case studies.

Prior to joining Syracuse University and consulting on data science efforts, Jeff worked at JPMorgan Chase, where he reported to the firm’s Global Chief Information Officer and drove technology innovation across the organization, while also having other leadership roles within the bank, such as leading the IT effort for credit risk analytics within the consumer bank.

 

Senior R&D Scientist | Research Group LeaderGeoAI, Oak Ridge National Laboratory

Dalton Lunga, PhD

Dalton Lunga is a senior R&D scientist and research group leader for GeoAI at the Oak Ridge National Laboratory. His research interests are in domain adaptation, manifold learning, unsupervised representation learning using deep learning approaches for geospatial imagery analytics. His technical background includes image processing, statistical machine learning, remote sensing, and geospatial data analysis. He currently conducts research and development in machine learning techniques and advanced workflows for handling large volumes of geospatial data. Before ORNL, Dalton worked as machine learning research scientist at the council for scientific and industrial research in South Africa on various projects. He received his Ph.D. in electrical and computer engineering from Purdue University, West Lafayette, Indiana.

Director Of EngineeringMobileIron

Yong Tang, PhD

Yong Tang, Ph.D., is Director of Engineering at Ivanti. He is a core contributor of many open-source projects in machine learning and cloud native areas. He is a maintainer and SIG I/O lead of the TensorFlow project, and received the Open Source Peer Bonus award from Google for his contributions to TensorFlow. He is also a maintainer of Docker/Moby, the widely used open-source container platform, and a core maintainer of CoreDNS, a Cloud Native Computing Foundation (CNCF) graduated project for service discovery.

 

Staff Data ScientistChegg

Sanghamitra Deb, PhD

Sanghamitra Deb is a Staff Data Scientist at Chegg, she works on problems related school and college education to sustain and improve the learning process. Her work involves recommendation systems, computer vision, graph modeling, deep NLP analysis , data pipelines and machine learning. Previously, Sanghamitra was a data scientist at a Accenture where she worked on a wide variety of problems related data modeling, architecture and visual story telling. She is an avid fan of python and has been programming for more than a decade. Trained as an astrophysicist (she holds a PhD in physics) she uses her analytical mind to not only work in a range of domains such as: education, healthcare and recruitment but also in her leadership style. She mentors junior data scientists at her current organization and coaches students from various field to transition into Data Science. Sanghamitra enjoys addressing technical and non-technical audiences at conferences and encourages women into joining tech careers. She is passionate about diversity and has organized Women In Data Science meetups.

Co-founder and CEOStreamlit

Adrien Treuille, Phd

Adrien is co-founder and CEO of Streamlit which is pioneering next-generation tools for machine learning engineers. Dr. Treuille has been VP of Simulation Zoox, lead a Google X project, and was a Professor of Computer Science and Robotics at Carnegie Mellon. He gives talks around the world, including to the President’s Council of Advisors on Science and Technology, and has won numerous scientific awards, including the MIT TR35. Adrien and his work have been featured in the documentaries “What Will the Future Be Like” by PBS/NOVA, and “Lo and Behold” by Werner Herzog.

Staff Research Scientist Google Brain

Balaji Lakshminarayanan, PhD

Balaji is currently a Staff Research Scientist at Google Brain working on Machine Learning and its applications. Previously, he was a research scientist at DeepMind for 4.5+ years. Before that, he received a PhD in machine learning from Gatsby Unit, UCL supervised by Yee Whye Teh. His research interests are in scalable, probabilistic machine learning. More recently, he has focused on: – Uncertainty and out-of-distribution robustness in deep learning – Deep generative models including generative adversarial networks (GANs), normalizing flows and variational auto-encoders (VAEs) – Applying probabilistic deep learning ideas to solve challenging real-world problems.

CEO, Security & Forensics Analyst, Incident Responder | Senior InstructorOne eSecurity | SANS Institute

Jess Garcia

Jess Garcia is the Founder of the global Cybersecurity/DFIR firm One eSecurity and a Senior Instructor with the SANS Institute. During his 25 years in the field, Jess has led a myriad of complex multinational investigations for Fortune 500 companies and global organizations. As a SANS Instructor, Jess stands as one of the most prolific and veteran ones, having taught 10+ different highly technical Cybersecurity/DFIR courses in hundreds of conferences world-wide over the last 19 years. Jess is also an active Cybersecurity/DFIR Researcher. With the mission of bringing Data Science/AI to the DFIR field, Jess launched in 2020 the DS4N6 initiative (www.ds4n6.io), under which he is leading the development of multiple open source tools, standards and analysis platforms for DS/AI+DFIR interoperability.

Data Science for Digital Forensics & Incident Response (DFIR)(Half-Day Training)

 

Chief Scientistuniv.ai | lxprior.com | Harvard University

Dr. Rahul Dave

Rahul Dave is a lecturer in Bayesian Statistics and Machine Learning at Harvard University, and consults on the same topics at LxPrior. He holds a Ph.D. from the University of Pennsylvania in Computational Astrophysics, and has programmed device drivers for telescopes, bespoke databases for astrophysical data, and machine learning systems in various fields. His new startup, univ.ai, helps students and companies upgrade the skill and understanding of both their developers and managers for this new AI driven world, by providing both corporate training and consulting.

Software ArchitectTwilio

Scott Haines

Scott Haines is a full-stack engineer with a current focus on real-time, highly available, trustworthy analytics systems. He is currently working at Twilio as a Software Architect and previously worked as Principal Software Engineer on the Voice Insights team, where he helped drive spark adoption and streaming pipeline architectures, and build out a massive stream-processing platform.

Prior to Twilio, he worked on writing the backend Java APIs for Yahoo! Games, as well as the real-time game ranking/rating engine (built on Storm) to provide personalized recommendations and page views for 10 million customers. He finished his tenure at Yahoo! working for Flurry Analytics where he wrote the alerts/notifications system for mobile.

Founder & Lead Data ScientistApplied Artificial Intelligence

Stefan Jansen

Stefan is the founder and Lead Data Scientist at Applied AI. He advises Fortune 500 companies, investment firms and startups across industries on data & AI strategy, building data science teams, and developing machine learning solutions. Before his current venture, he was a partner and managing director at an international investment firm where he built the predictive analytics and investment research practice. He also was a senior executive at a global fintech company with operations in 15 markets.
Earlier, he advised Central Banks in emerging markets, worked for the World Bank, raised $35m from the Gates Foundation to cofound the Alliance for Financial Inclusion, and has worked in six languages across Asia, Africa, and Latin America. Stefan holds Master degrees in Computer Science from Georgia Tech and in Economics from Harvard and Free University Berlin and is a CFA Charterholder. He is the author for ‘Machine Learning for Algorithmic Trading’ and has been teaching data science at Datacamp and General Assembly.

 

Systems Software EngineerNVIDIA

Bryan Van de Ven

Bryan currently works at NVidia, on data visualization efforts around RAPIDs, and is also a Core Team member of the Bokeh project. Previously, he worked at Microsoft on the Azure SDK for Python, and at Anaconda on Conda, Bokeh, and client facing-training and services.

Python & Data Science Corporate Trainer | ConsultantMetaSnake

Matt Harrison

Matt Harrison has been using Python since 2000. He runs MetaSnake, a Python and Data Science consultancy and corporate training shop. In the past, he has worked across the domains of search, build management and testing, business intelligence, and storage. He has presented and taught tutorials at conferences such as Strata, SciPy, SCALE, PyCON, and OSCON as well as local user conferences.

Data Visualization DeveloperObservable

Ian Johnson

Ian Johnson is a User Experience Engineer at Google. He also organizes of Bay Area d3, starts with SVG and then dives deep into d3 including DOM manipulation, categorical and quantitative scales, axis, brushes, color schemes, events and histograms. Ian likes to make sense of data by exploring it visually with D3.js!

Painting with Data: Introduction to d3.js(Half-Day Training)

Sr Research EngineerGrid AI | PyTorch Lightning

Jirka Borovec, PhD

Jirika is working in Machine learning and Data science for several years. He has done Ph.D. in Medical Imaging. In parallel, he gains practical experience while he has been working for a few IT companies as a consultant or data scientist. Actually,he is focusing on exploring interesting world problems and solving them with state-of-the-art techniques. He has developed several open-source python packages, He is the core contributor of `PyTorch-Lightning` and `TorchMetrics` and actively participating in other well-known projects.

Lead Data ScientistJohn Snow Labs

Veysel Kocaman, PhD

Veysel is a well known thought leader in healthcare NLP and works as a Lead Data Scientist and ML Engineer at John Snow Labs, improving the Spark NLP for the Healthcare library and delivering hands-on projects in Healthcare and Life Science. He is a seasoned data scientist with a strong background in every aspect of data science including NLP, machine learning, deep learning, and big data with over ten years of experience. He’s also pursuing his Ph.D. in ML at Leiden University, Netherlands, and delivers graduate-level lectures in Auto ML and Distributed Data Processing. Veysel has broad consulting experience in Statistics, Data Science, Software Architecture, MLOps, Machine Learning, and AI to several start-ups, boot camps, and companies around the globe. He also speaks at Data Science & AI events, conferences and workshops, and has delivered more than a hundred talks at international as well as national conferences and meetups.

Spark NLP for Healthcare: Modular Approach to Solve Problems at Scale in Healthcare NLP(Half-Day Training)

 

Data Science and ML Solution Engineer Salesforce

Amir Meimand, PhD

Amir Meimand is a Principal Solution Engineering at Salesforce strategic solution team focusing on Data Science and Machine Learning area. Amir has 10+ years experiences in building, deploying, and applying advance analytics to solve enterprise business problems. Previously, he was the director of Data Science at Zilliant a SASS company to provide machine learning solution for price optimization and sale maximization and lately acquired by Madison Dearborn. Amir’s current area of focused is scaling advance analytic solutions by democratizing data science and machine learning. Amir holds a Ph.D. degree in Statics and Operations Research from Pennsylvania State University graduated in 2013.

Senior Clinical ProgrammerBioMarin

Martin Frigaard

Martin is a Senior Clinical Programmer at BioMarin, where he builds dashboards and tools for making data-informed decisions. Previously, Martin built statistical tools and dashboards for the Diabetes Technology Society, a contributing author for Data Journalism in R on the Northeastern University School of Journalism blog/website, and other volunteer and non-profit organizations. He’s a data journalism instructor for California State University, Chico. Martin holds a graduate degree in Clinical Research and is passionate about data literacy and open source technologies.

Research ScientistGeographic Data Sciences, Oak Ridge National Laboratory

Jesse Piburn

Jesse Piburn is a Research Scientist in Geographic Data Science at Oak Ridge National Laboratory. His work includes research and development in spatiotemporal analytics, time series data mining, and machine learning. As a member of the GeoAI group at ORNL, he has had the opportunity to work across multiple domains including population dynamics, socio-economic modeling, and image analysis. Jesse has a background in spatial statistics and geographic information science, he received his M.S. in Geography, and is currently pursuing a PhD in Data Science and Engineering from the University of Tennessee.

Research EngineerGrid AI | PyTorch Lightning

Ananya Harsh Jha

Ananya Harsh Jha is a research engineer at PyTorch Lightning and Grid AI. Before this, he completed his masters in computer science from NYU, primarily working on self-supervised representation learning.

Geospatial Data ScientistGeographic Data Sciences, Oak Ridge National Laboratory

Jacob Arndt

Jacob Arndt received the B.S. degree in geography and the master’s degree in geographic information science (MGIS) from the University of Minnesota – Twin Cities, Minneapolis, MN, USA, in 2016 and 2018, respectively. He is a Geospatial Data Scientist with the Geospatial Artificial Intelligence (GeoAI) Group, Oak Ridge National Laboratory, Oak Ridge, TN, USA. His research interests include remote sensing, machine learning, and geographic information systems.

Previous East Workshops/Training Sessions

East 2022 Confirmed Sessions Coming Soon

Milecia McGregor | Senior Software Engineer | Iterative

Tuning Hyperparameters with Reproducible Experiments 

Ajay Baranwal | Center Director | Center for Deep Learning in Manufacturing

GANs: Theory and Practice, Image Synthesis With GANs Using TensorFlow

Jayeeta Putatunda Data Scientist | MediaMath

Build a Question Answering System using DistilBERT in Python

Rongpeng Li | Senior Data Scientist | Galvanize

A Complete Real-Time Data Application in 90 Minutes: from Kafka to Streamlit

Ryan Kasichainula | Sr. Data Science | Galvanize

Sound Classification and Detection with STFT and CNNs

Jennifer Dawn Davis, PhD and Andrea Lowe, PhD | Staff Field Data Scientist and Training & Enablement Engineer | Domino Data Labs

Practical Reinforcement Learning for Data Scientists

Yong Tang, PhD, Director of Engineering

Data, I/O, and TensorFlow: Building a Reliable Machine Learning Data Pipeline

Victor Lo, PhD | Head of Data Science & Artificial Intelligence | Fidelity Investments

How to Optimize using Uplift Predictive Models and Uplift Prescriptive Analytics

Kai-Wei Chang | Director, Assistant Professor | UCLA NLP, UCLA CS

Transferable Representation Learning in NLP

Hannes Hapke | Machine Learning | Digits

Unstructured Text with Transformers

Filipa Peleja, PhD | Lead Data Scientist | Levi Strauss & Co

MLOps (Machine Learning Operations) From Model to Production

Kumaran Ponnambalam | Big Data, Data Science & Analytics Leader | Cisco

Building a ML Serving Platform at Scale for Natural Language Processing

Sourav Mazumder | Data Scientist, AI & ML Operationalization Leader | IBM

Operationalization of Models Developed and Deployed in Heterogeneous Platforms

Oliver Zeigermann | Software Developer | Embarc Software Consulting GmbH

Deep Dive into Reinforcement Learning with PPO using TF-Agents & TensorFlow 2.0

Karl Weinmeister | Developer Relations Engineering Manager | Google

Get Started with Time-Series Forecasting using the Google Cloud AI Platform

Jess Garcia | Senior Instructor | SANS Institute

Digital Forensics and Incident Response

WHY ODSC?

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

Access to 50+ talks and workshops.

Recordings of workshop and talks sessions for later review.

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.

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 EAST 2022 | Apr 19th-Apr 21st

Register and save 60%

2021 Confirmed Trainings

Half-Day Training

Focus Area: Machine Learning  Session: Half Day Training 

Solving the Data Scientist’s Cold-Start Problem with Machine Learning Examples

Instructor: Dr. Kirk Borne, Principal Data Scientist | Booz Allen Hamilton

Hands-on 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 rock stars from industry and academia, who are passionate about molding the next generation of data scientists.
Open Data Science

 

 

 

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