Special Offer 2 for 1 Deal: Free Access to the Deep Learning Bootcamp

Register for an ODSC East virtual or in-person Bootcamp Pass to get free access to an acclaimed instructor and bestselling author of Deep Learning Illustrated, Dr. Jon Krohn’s upcoming Deep Learning virtual Bootcamp. This Bootcamp comprises 6 courses, totalling 21 hours of online instruction, and is valued at $699. But, you’d better act fast, this offer ends soon.

Key Facts

  • Become a deep learning expert in 2022!
  • Live virtual with Dr. Jon Krohn
  • Each course includes hands-on training & exercises
  • 6 live courses (3.5 hours)
  • 21 hours total instruction       
  • On-demand will be available
  • Course costs $699; Free with ODSC Bootcamp  

Get This Before Last 100 Tickets Deal Ends!

Register and Save 20%

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YOUR FULL POTENTIAL

Kick-start your career in data science in 4 immersive days. Get the skills you need, taught by world-renowned experts.

ODSC Mini-Bootcamp is the best way to gain in-demand machine learning skills in the shortest time with minimum investment. 

In a truly immersive experience, you will access a range of industry-focused data science topics on a scale not offered elsewhere. Uniquely, you will also gain invaluable insights by networking and connecting with hundreds of data science attendees, world-renowned instructors, industry experts, and dozens of top companies seeking the next wave of talent.

WHY ODSC BOOTCAMPS?  
BUILD YOUR SKILLS AND BUILD YOUR NETWORK

Get Prepped

With Prerequisites training, you can start learning the fundamentals. Learn programming, SQL, and basic math skills.

 3 or 4-day Immersive

From May 8 to 11, join our 4-day live immersive Mini-Bootcamp. Get hands-on experience in machine learning and deep learning.

Continue Learning

After immersive training, you will be perfectly positioned to continue learning. WIth Ai+ on-demand training and discounted live training, you can learn at your own pace. 

Get Help 

Each attendee gets a certificate of completion and access to office hours. Jobseeker will be featured in our Ai+ Career job portal with 40+ hiring companies. 

Specialization Tracks

  • Machine Learning with Python

    • Mathematics for Data Science
    • Programming with Data: Python and Pandas
    • Hands-on Machine Learning with scikit-learn
    • Machine Learning at Scale using Apache Spark
    • Supervised Learning for Missing Data
    • Causal Inference for Data Science
    • Meta-learning for Machine Learning
    • Cybersecurity in ML
    • Reinforcement Learning with TF-Agents
    • Reinforcement Learning for Human Language

  • Deep Learning

    • Intro to Neural Networks for Deep Learning
    • PyTorch Hands-on Training
    • Deep Reinforcement Learning
    • Deep Learning with Tensorflow
    • Recommendation Systems with Deep Learning
    • Deep Learning with Keras
    • TFF (Tensorflow Federated Learning)
    • How to Identify Deepfakes
    • Deep Learning CNNs and GANs

  • Data Engineering / MLOps

    • Mathematics for Data Science
    • SQL for Data Science
    • Deploying ML Pipelines with Open-data
    • Kubeflow & Kubernetes
    • Auto Machine Learning
    • Debugging Machine Learning
    • Introducing Flyte
    • Reproducible Data Science in Pachyderm
    • Data Science Best Practices: Continuous Delivery for Machine Learning

  • NLP

    • Introduction to NLP and Topic Modeling
    • Transfer Learning in NLP
    • Training NLP Models with Deep Reinforcement Learning
    • State-of-the-Art NLP in TensorFlow and PyTorch
    • Active Learning for Data Labeling
    • NLP with LSTMs (Deep learning)
    • Using HuggingFace Transformers for NLP

  • Data Analytics

    • Statistics and Probability Distribution
    • Big Data using Spark
    • Streaming Analytics
    • Augmented Analytics
    • Decision Analytics
    • Graph Analytics
    • No-Sql
    • In-memory computing

Live sessions include, but are not limited to, the ones listed below.

Dive Deeper

Get hands-on experience in job-ready skills

East 2022 Mini-Bootcamp Overview

Begin your data science training with pre-conference, live, and on-demand training to build confidence in the fundamentals. Steadily progress through the Bootcamp week with our hands-on training and expert-led sessions and workshops. Post-conference, take advantage of our continuing on-demand training sessions. Refer to the timeline below.

Pre-Bootcamp On-demand Training

Get started in your learning track with free access to introductory concepts with short, to-the-point and industry driven on-demand courses.

Schedule: Self-paced

Note: only accessible to 3-Day and 4-Day Bootcamp registrants.

Pre-Bootcamp and Live Training Warm up

Get started in your learning track with free access to March Live Training courses to learn concepts with short, to-the-point, and industry-driven training. Full schedule coming soon and previous sessions included:

Note: only accessible to Mini-Bootcamp registrants.

Bootcamp Day 1 : Beginner Level SEssions (April 18th) (Virtual Only)

Choose from in-person and virtual live sessions. Includes training sessions, workshops, and tutorials. Full schedule coming soon.

  • Introduction to Mathematics and Statistics for Machine Learning
  • Introduction to SQL
  • Programming with Data: Python and Pandas
  • NLP Fundamentals
  • Manipulating and Visualizing Data with R

Bootcamp Day 2 & 3 : Beginner to Intermediate Level (April 19th - 20th) (Virtual/In-Person)

Choose from in-person and virtual live sessions. Includes training sessions, workshops, and tutorials. Full schedule coming soon.

  • Building and Deploying Machine Learning Models with TensorFlow and Keras
  • Transfer Learning in NLP
  • Multi-Task Reinforcement Learning
  • Security and Privacy in ML
  • Kubeflow & Kubernetes
  • Building Machine Learning Pipelines
  • Introduction to ML using scikit-learn

  • End to end Machine Learning with XGBoost

  • Automation for Data Professionals
  • Hugging Face Transformers

Bootcamp Day 4: Beginner-Intermediate-Advanced Level (April 21st) (Virtual/In-Person)

Choose from in-person and virtual live sessions. Includes training sessions, workshops, and tutorials. Full schedule coming soon.

  • Hands-on overview to drift detection
  • Spark NLP for Healthcare
  • Deep Learning for Detecting DeepFakes
  • Machine Learning at Scale using Apache Spark

Post Conference : 1-year Premium Subscription on the Ai+ Training

What is included

  • Foundations for Machine Learning Bootcamp: 14 On-Demand Sessions
  • Machine Learning Certification
  • Certification Assessments and Badges
  • Entire On-Demand Training Library (50 + courses) and All Previous ODSC Conference recordings (500+ sessions)
  • Monthly Webinars
  • All Assessments (Skills, Feedback, Coding Proficiency and Coding Proficiency Exam)
  • Certificate of Completion for Every course 

And much more. See everything that is included  here

Bootcamp Office Hours

Office Hours will be offer in the months prior to the bootcamp

  • Answer questions on the ODSC East Bootcamp program
  • Understand program content
  • Find out what courses are available on Ai+
  • Get help with assignments on Ai+


Choose your Pass

Monday | Virtual Mini-Bootcamp Training Sessions 

Premium 1-Year Subscription to AI+ Training (value =  $700)

Access to All Virtual Sessions & Events (Tue-Thu)

ODSC Keynotes & Talks (Wed-Thu)

Prep Training: live and On-demand (value = $499)

On-demand Access to All Conference recordings 

Access to AI Solution Showcase Expo Area (Wed-Thu) 

Access to ODSC In-Person Workshops & Training Sessions (Tue&Thu) 

Access to In-person Mini-Bootcamp Training Sessions

Live

Virtual Mini-Bootcamp 

$799 

Door price - $999 - SAVE $200

4-Days


Register | Save 20%

In-Person 

Mini-Bootcamp 

$1399

Door price - $1749 - SAVE $350 

4-Days


Register | Save 20%

Bootcamp Experience

Virtual Experience

In-Person Experience

Learn from the Best

Our mission is to provide you with applied training from some of the top instructors in the field. Confirmed ODSC East instructors Coming Soon:
Training and Enablement EngineerDomino Data Labs

Andrea Lowe, PhD

Andrea Lowe, PhD is the Training and Enablement Engineer at Domino Data Labs where she develops training on topics including overviews of coding in Python, machine learning, Kubernetes, and AWS. She trained over 1000 data scientists and analysts in the last year. She has previously taught courses including Numerical Methods and Data Analytics & Visualization at the University of South Florida and UC Berkeley Extension. Her conference experience includes a deep learning tutorial at PyCon, 2 invited talks, 21 poster presentations, and 4 chair positions.

Practical Reinforcement Learning for Data Scientists(Workshop)

Staff Field Data ScientistDomino Data Labs

Jennifer Dawn Davis, PhD

Jennifer Davis, Ph.D. is a Staff Field Data Scientist at Domino Data Labs, where she empowers clients on complex data science projects. She has completed two postdocs in computational and systems biology, trained at a supercomputing center at the University of Texas, Austin, and worked on hundreds of consulting projects with companies ranging from start-ups to the Fortune 100. Jennifer has previously presented topics at conferences for Association for Computing Machinery on LSTMs and Natural Language Generation and at conferences across the US and in Italy. Jennifer was part of a panel discussion for an IEEE conference on artificial intelligence in biology and medicine. She has practical experience teaching both corporate classes and at the college level. Jennifer enjoys working with clients and helping them achieve their goals.

Large Scale Deep Learning using the High-Performance Computing Library OpenMPI and DeepSpeed(Workshop)

Sr. Director - Data Science & ML EngineeringAmpersand

Daniel Gerlanc

Daniel Gerlanc has worked as a data scientist for more than decade and been writing software for nearly 20 years. He frequently teaches live trainings on oreilly.com and is the author of the video course Programming with Data: Python and Pandas. He has coauthored several open source R packages, published in peer-reviewed journals, and is a graduate of Williams College.

Programming with Data: Python and Pandas(Training)

Software EngineerStride Consulting

Boris Paskhaver

Boris Paskhaver is a full-stack web developer based in New York City with experience building apps in React / Redux and Ruby on Rails. His favorite part of programming is the never-ending sense that there’s always something new to master — a secret language feature, a popular design pattern, an emerging library or — most importantly — a different way of looking at a problem.

Getting Started with Pandas for Data Analysis(Half-Day Training)

Data Scientist Meta

Clinton Brownley, PhD

Clinton Brownley, Ph.D., is a data scientist at Meta (formerly Facebook), where he’s responsible for a variety of analytics projects designed to empower employees to do their best work. Prior to this role, he was a data scientist at WhatsApp, working to improve messaging and VoIP calling performance and reliability. Before WhatsApp, he worked on large-scale infrastructure analytics projects to inform hardware acquisition, maintenance, and data center operations decisions at Facebook.

As an avid student and teacher of modern data analysis and visualization techniques, Clinton teaches a graduate course in interactive data visualization for UC Berkeley’s MIDS program, taught a short-term graduate course in regression analysis and machine learning workshop for NYU’s A3SR program, leads an annual machine learning in Python workshop, and is the author of two books, “Foundations for Analytics with Python” and “Multi-objective Decision Analysis”.

Clinton is a past-president of the San Francisco Bay Area Chapter of the American Statistical Association and is a council member for the Section on Practice of the Institute for Operations Research and the Management Sciences. Clinton received degrees from Carnegie Mellon University and American University.

Machine Learning with Python: A Hands-On Introduction(Training)

Senior Inventive ScientistAT&T Chief Data Office

Noemi Derzsy, PhD

Noemi Derzsy is a Senior Inventive Scientist at AT&T Chief Data Office within the Data Science and AI Research organization. Her research is centered on understanding and modeling customer behavior and experience through large-scale consumer and network data, using machine learning, network analysis/modeling, Spatio-temporal mining, text mining, and natural language processing techniques.
Prior to joining AT&T, Noemi was a Data Science Fellow at Insight Data Science NYC and a postdoctoral research associate at Social Cognitive Networks Academic Research Center at Rensselaer Polytechnic Institute. She holds a Ph.D. in Physics, MS in Computational Physics, and has a research background in Network Science and Computer Science.
Noemi is also involved in volunteering in the data science community. She is a NASA Datanaut and former organizer of the Data Umbrella meetup group and NYC Women in Machine Learning and Data Science meetup group.

Exploring the Interconnected World: Network/Graph Analysis in Python(Tutorial)

Data Scientist, Software Engineer, Author of Hands-On Data Analysis with PandasBloomberg

Stefanie Molin

Stefanie Molin is a data scientist and software engineer at Bloomberg in New York City, where she tackles tough problems in information security, particularly those revolving around anomaly detection, building tools for gathering data, and knowledge sharing. She is also the author of “Hands-On Data Analysis with Pandas,” which is currently in its second edition. She holds a bachelor’s of science degree in operations research from Columbia University’s Fu Foundation School of Engineering and Applied Science. She is currently pursuing a master’s degree in computer science, with a specialization in machine learning, from Georgia Tech. In her free time, she enjoys traveling the world, inventing new recipes, and learning new languages spoken among both people and computers.

Beyond the Basics: Data Visualization in Python(Training)

Managing Partner, Principal Data Scientist | Distinguished FacultyBetaVector | General Assembly

Matt Brems

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.

Good, Fast, Cheap: How to do Data Science with Missing Data(Half-Day Training)


On-Demand Post Conference Training  
With Each ODSC Bootcamp pass, you get a 1 year Ai+ on-demand hands-on training subscription (Value $700)

Ai+ is the only hands-on training platform solely developed for AI practitioners. Keep training with the top names in the industry. Free for 1 year with an ODSC East Bootcamp pass.

Learn More
Discover Your Bootcamp Learning Path

East Mini-Bootcamp Overview

What You’ll Learn

 Learn Fundamentals

Prep for your immersive training by learning core fundamentals, including:

  • Fundamentals of Python Programming

  • Data Analysis in Python

  • Introduction to Statistics for Data Science

  • Fundamental Analytics for Data Engineering

Learn Frameworks & Tools

Get hands-on experience with in-demand data science tools and frameworks, including:

  • Python, Jupyter Notebooks

  • R programming, Julia, Scala, Stan

  • Apache Spark, MLlib, Streaming

  • Tensorflow, MXNet, Caffe, CNTK

  • Scikit-learn, Theano, Shogun, Pylearn2

Learn Core Topics

Get hands-on experience in the most important data science topics with over 40 beginner to intermediate sessions to choose from, including

  • Deep Learning

  • Reinforcement Learning and Deep Reinforcement Learning

  • Machine Learning

  • Transfer Learning

  • Natural Language Processing

  • Text Analytics

  • Data Visualization

  • Data Modeling and Data Wrangling

  Build your Network

A career in data science opens up the possibility of rewarding and exciting work. Build your network and help grow your career

  • Learn from and connect with world-leading data science experts

  • Stop by our Career Expo and meet top hiring companies like Google, DataRobot, and dozens of others

  • Join our various networking events, such as Meet the Speakers, Meet the Experts, Dinner with Data Scientists; plus other networking opportunities

  • Network with hundreds of data science attendees to learn what it’s like to be a data scientist

Some of Our Past ODSC Instructors

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.

Leveling Up Your Organization’s Capacity for Data-informed Decisions(Talk)

SQL for Data Science(Training)

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)

Assistant ProfessorStanford Graduate School of Business

Stefan Wager, PhD

Stefan Wager is an Associate Professor of Operations, Information and Technology at Stanford Graduate School of Business, and an Associate Professor of Statistics (by courtesy). He received his PhD in Statistics from Stanford in 2016, and has worked with or consulted for several Silicon Valley companies, including Dropbox, Facebook, Google and Uber. His research lies at the intersection of causal inference, optimization, and statistical learning. He is particularly interested in developing new solutions to problems in statistics, economics and decision making that leverage recent advances in machine learning.

Machine Learning for Causal Inference(Tutorial)

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.

Introduction to Scikit-learn: Machine Learning in Python(Training)

Intermediate Machine Learning with Scikit-learn: Cross-validation, Parameter Tuning, Pandas Interoperability, and Missing Values(Training)

Intermediate Machine Learning with Scikit-learn: Evaluation, Calibration, and Inspection(Training)

Advanced Machine Learning with Scikit-learn: Text Data, Imbalanced Data, and Poisson Regression(Training)

Data Scientist, Software Engineer, Author of Hands-On Data Analysis with PandasBloomberg

Stefanie Molin

Stefanie Molin is a data scientist and software engineer at Bloomberg in New York City, where she tackles tough problems in information security, particularly those revolving around anomaly detection, building tools for gathering data, and knowledge sharing. She is also the author of “Hands-On Data Analysis with Pandas,” which is currently in its second edition. She holds a bachelor’s of science degree in operations research from Columbia University’s Fu Foundation School of Engineering and Applied Science. She is currently pursuing a master’s degree in computer science, with a specialization in machine learning, from Georgia Tech. In her free time, she enjoys traveling the world, inventing new recipes, and learning new languages spoken among both people and computers.

Beyond the Basics: Data Visualization in Python(Training)

Technology Research DirectorElsevier Labs

Sujit Pal

Sujit Pal is an applied data scientist at Elsevier Labs, an advanced technology group within the Reed-Elsevier Group of companies. His areas of interests include Semantic Search, Natural Language Processing, Machine Learning and Deep Learning. At Elsevier, he has worked on several machine learning initiatives involving large image and text corpora, and other initiatives around recommendation systems and knowledge graph development. He has co-authored Deep Learning with Keras (https://www.packtpub.com/big-data-and-business-intelligence/deep-learning-keras) and Deep Learning with Tensorflow 2.x and Keras (https://www.packtpub.com/data/deep-learning-with-tensorflow-2-0-and-keras-second-edition), and writes about technology on his blog Salmon Run (https://sujitpal.blogspot.com/).

Transformer Based Approaches to Named Entity Recognition (NER) and Relationship Extraction (RE)(Training)

 

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.

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)

 

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.

Distributed Python with Ray: Hands-on with the Ray Core APIs (Tutorial)

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.

Tutorial: Building and Deploying Machine Learning Models with TensorFlow and Keras(Training)

 

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.

Machine Learning with XGBoost(Workshop)

Idiomatic Pandas(Workshop)

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.

Automation for Data Professionals(Training)

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.

Intro to NLP: Text Categorization and Topic Modeling(Workshop)

Research EngineerHugging Face

Patrick von Platen

Patrick von Platen is a research engineer at Hugging Face and one of the core maintainers of the popular Transformers library. He specializes in speech recognition, encoder-decoder models and long-range sequence modeling. Before joining Hugging Face, Patrick conducted research in speech recognition at Uber AI, Cambridge University, and RWTH Aachen University.

:hugging_face: Transformers & :hugging_face: Datasets for Research and Production(Training)

Head of Developer RelationSeldon

Ed Shee

Ed Shee, Head of Developer Relations at Seldon. Having previously led a tech team at IBM, Ed comes from a cloud computing background and is a strong believer in making deployments as easy as possible for developers. With an education in computational modelling and an enthusiasm for machine learning, Ed has blended his work in ML and cloud native computing together to cement himself firmly in the emerging field of MLOps.

An Introduction to Drift Detection(Workshop)

M.S. Data Science at Harvard University, Z by HP Global Data Science Ambassador

Max Urbany

As Max progresses through his Master’s Program, he is particularly interested in intelligent digital accessibility design, along with the ethical analysis of existing predictive models. His passion for creating quality user-centered tools drives him to understand as much as he can about end users while leveraging what data can reveal.

Z by HP Panel Discussion on the Diverse Role of Data Science in Education(Talk)

VP, Enterprise AI / Data Science Solutions at Future Tech

Dan Chaney

Dan Chaney is the VP, Enterprise AI / Data Science Solutions, for Future Tech Enterprise, Inc., an award-winning global IT solutions provider. He oversees all sales, marketing, and technical activities focused on Future Tech’s comprehensive range of AI and data science workstation solutions. Prior to joining Future Tech, Dan spent 20 years at Northrop Grumman, most recently serving as the company’s Enterprise Director of IT Solution Architecture & Engineering. Dan earned his bachelor’s and master’s degrees in communication and computer science from the University of Kentucky. Dan is a Certified Information Systems Security Professional (CISSP) and adjunct instructor for the University of Louisville’s cybersecurity workforce program sponsored by the National Centers of Academic Excellence in Cybersecurity.

Z by HP Panel Discussion on the Diverse Role of Data Science in Education(Talk)

North America Business Development Manager, Z by HP

Kristin Hempstead

Kristin has been with HP for 11 years and is currently the North America business development manager for HP’s data science and artificial intelligence solutions focusing on federal, education, and public sector customers. She has an MBA from University in South Florida with a specialization in Finance and MIS and a BS in Agriculture from the University of Georgia.

Z by HP Panel Discussion on the Diverse Role of Data Science in Education(Talk)

Sr. Director - Data Science & ML EngineeringAmpersand

Daniel Gerlanc

Daniel Gerlanc has worked as a data scientist for more than decade and been writing software for nearly 20 years. He frequently teaches live trainings on oreilly.com and is the author of the video course Programming with Data: Python and Pandas. He has coauthored several open source R packages, published in peer-reviewed journals, and is a graduate of Williams College.

Programming with Data: Python and Pandas(Training)

Learn More About Our Program



Companies Represented at ODSC Bootcamp

We’re Proud to Have the Best and Brightest in Attendance

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