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 acclaimed instructor and bestselling author of Deep Learning Illustrated, Dr. Jon Krohn’s upcoming Deep Learning virtual Bootcamp. This bootcamp comprises of 6 courses, totaling 21 hours of online instruction, and is valued at $699. But, you’d better act fast, this offer ends Friday.

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 Deal Before EARLY BIRD OFFER Ends!

Register and Save 60%

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YOUR FULL POTENTIAL

Kick-start your career in data science in 3 immersive days. Get the skills you need, taught by world-renowned experts.

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

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 April 18 to 21, join our 3-day live immersive Kickstart 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 October 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 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. Here are examples from previous mini-boot camps. Full schedule coming soon.

  • Introduction to Mathematics and Statistics for Machine Learning
  • Introduction to SQL
  • Introduction to Python
  • Introduction to Data Analysis
  • Introduction to ML using scikit-learn
  • Introduction to Neural Networks for Deep Learning

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. Here are examples from previous mini-boot camps. Full schedule coming soon.

  • Introduction to Intermediate Deep Learning with Tensorflow
  • Deep Learning with Keras
  • Transfer Learning in NLP
  • Causal Reinforcement Learning
  • Reinforcement Learning with TF-Agents
  • Tackling Climate Change with Machine learning
  • Security and Privacy in ML
  • Kubeflow & Kubernetes
  • Building Machine Learning Pipelines

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. Here are examples from previous mini-boot camps. Full schedule coming soon.

  • Advanced Deep Learning with Tensorflow
  • Reinforcement Learning for Human Language
  • NLP Word Embeddings
  • Deep Learning for Detecting DeepFakes
  • Machine Learning at Scale using Apache Spark
  • Augmented & Decision Analytics

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 West 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 

$649 

Door price - $1623 - SAVE $974

4-Days


Register | Save 60%

In-Person 

Mini-Bootcamp 

$1279

Door price - $3198 - SAVE $1919 

4-Days


Register | Save 60%

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 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.

Practical Reinforcement Learning for Data Scientists(Workshop)

PresidentEnplus Advisors, Inc.

Daniel Gerlanc

Daniel Gerlanc has worked as a data scientist for more than decade and been writing sofware 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(Half-Day 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 Facebook

Clinton Brownley, PhD

Clinton Brownley, Ph.D., is a data scientist at 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 analytics techniques, Clinton is the author of two books, “Foundations for Analytics with Python” and “Multi-objective Decision Analysis,” and also teaches Python programming and interactive data visualization courses at Facebook and in the Bay Area. 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.

Visualizing and Analyzing Networks with Python or R (and Javascript)(Tutorial)

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.

 

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 Virtual 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 Previous ODSC Instructors

Software Engineer, Core DeveloperInria, Scikit-learn

Olivier Grisel

Olivier Grisel is a machine learning engineer at Inria. He is a member of the team of maintainers of the scikit-learn project. Scikit-learn is an Open Source machine learning library written in Python. His work is supported by the Fondation Inria and its partners.

Hands-on Machine Learning Engineer with scikit-learn(Full-Day Training)

Co-creator of the Julia language, Co-founder & CTOJulia Computing, Inc.

Jeff Bezanson, PhD

Jeff is one of the creators of Julia, co-founding the project at MIT in 2009 and eventually receiving a Ph.D. related to the language in 2015. He continues to work on the compiler and system internals, while also working to expand Julia’s commercial reach as a co-founder of Julia Computing, Inc.

Advances in Julia for Data Science and ML(Workshop)

deep learning with TensorFlow
Chief Data ScientistUntapt

Dr. Jon Krohn

Jon Krohn is Chief Data Scientist at the machine learning company untapt. He authored the book Deep Learning Illustrated, which was released by Addison-Wesley in 2019 and became an instant #1 bestseller that was translated into six languages. Jon is renowned for his compelling lectures, which he offers in-person at Columbia University, New York University, and the NYC Data Science Academy, as well as online via O’Reilly, YouTube, and his A4N podcast on A.I. news. Jon holds a doctorate in neuroscience from Oxford and has been publishing on machine learning in leading academic journals since 2010.

Linear Algebra, Calculus, and Probability: The Math ML Experts Master(Tutorial)

Data Science AdvocateNeo4j

Dr. Clair J. Sullivan

Dr. Clair Sullivan is currently a graph data science advocate at Neo4j, working to expand the community of data scientists and machine learning engineers using graphs to solve challenging problems. She received her doctorate degree in nuclear engineering from the University of Michigan in 2002. After that, she began her career in nuclear emergency response at Los Alamos National Laboratory where her research involved signal processing of spectroscopic data. She spent 4 years working in the federal government on related subjects and returned to academic research in 2012 as an assistant professor in the Department of Nuclear, Plasma, and Radiological Engineering at the University of Illinois at Urbana-Champaign. While there, her research focused on using machine learning to analyze the data from large sensor networks. Deciding to focus more on machine learning, she accepted a job at GitHub as a machine learning engineer while maintaining adjunct assistant professor status at the University of Illinois. In 2021 she joined Neo4j as a Graph Data Science Advocate. Additionally, she founded a company, La Neige Analytics, whose purpose is to provide data science expertise to the ski industry. She has authored 4 book chapters, over 20 peer-reviewed papers, and more than 30 conference papers. Dr. Sullivan was the recipient of the DARPA Young Faculty Award in 2014 and the American Nuclear Society’s Mary J. Oestmann Professional Women’s Achievement Award in 2015.

Machine Learning With Graphs: Going Beyond Tabular Data(Talk)

Author & Maintainer of PyTorch

Adam Paszke

Adam is an author and maintainer of PyTorch. He has worked with large organizations like Facebook AI Research, NVIDIA and Google, despite the fact that he has graduated from the Master’s program in Computer Science at the University of Warsaw only last year. Currently, he is also finishing his second major in Mathematics. His general interests include graph theory, programming languages, numerical computing and machine learning.

Step Up Your PyTorch with Custom Extension(Workshop)

Author, Research Scientist, Core Contributor of scikit-learnColumbia Data Science Institute

Andreas Mueller, PhD

Andreas Mueller is an Associate Research Scientist at the Data Science Institute at Columbia University and author of the O’Reilly book “”Introduction to machine learning with Python””. He is one of the core developers of the scikit-learn machine learning library and has co-maintained it for several years.

His mission is to create open tools to lower the barrier of entry for machine learning applications, promote reproducible science and democratize access to high-quality machine learning algorithms.

Introduction to Machine Learning with scikit-learn(Training)

Intermediate Machine Learning with scikit-learn(Training)

Advanced Machine Learning: Pipelines and Evaluation Metrics(Training)

Advanced Machine Learning with scikit-learn: Imbalanced Classification and Text Data(Training)

Associate ProfessorMIT

Tamara Broderick, PhD

Tamara Broderick is an Associate Professor in the Department of Electrical Engineering and Computer Science at MIT. She is a member of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), the MIT Statistics and Data Science Center, and the Institute for Data, Systems, and Society (IDSS). She completed her Ph.D. in Statistics at the University of California, Berkeley in 2014. Previously, she received an AB in Mathematics from Princeton University (2007), a Master of Advanced Study for completion of Part III of the Mathematical Tripos from the University of Cambridge (2008), an MPhil by research in Physics from the University of Cambridge (2009), and an MS in Computer Science from the University of California, Berkeley (2013). Her recent research has focused on developing and analyzing models for scalable Bayesian machine learning. She has been awarded an Early Career Grant (ECG) from the Office of Naval Research (2020), an AISTATS Notable Paper Award (2019), an NSF CAREER Award (2018), a Sloan Research Fellowship (2018), an Army Research Office Young Investigator Program (YIP) award (2017), Google Faculty Research Awards, an Amazon Research Award, the ISBA Lifetime Members Junior Researcher Award, the Savage Award (for an outstanding doctoral dissertation in Bayesian theory and methods), the Evelyn Fix Memorial Medal and Citation (for the Ph.D. student on the Berkeley campus showing the greatest promise in statistical research), the Berkeley Fellowship, an NSF Graduate Research Fellowship, a Marshall Scholarship, and the Phi Beta Kappa Prize (for the graduating Princeton senior with the highest academic average).

An Automatic Finite-Sample Robustness Metric: Can Dropping a Little Data Change Conclusions?(Workshop)

Kirk borne
Principal Data ScientistBooz Allen Hamilton

Dr. Kirk Borne

Dr. Kirk Borne is the Principal Data Scientist and an Executive Advisor at global technology and consulting firm Booz Allen Hamilton. In those roles, he focuses on applications of data science, data management, machine learning, A.I., and modeling across a wide variety of disciplines. He also provides training and mentoring to executives and data scientists within numerous external organizations, industries, agencies, and partners in the use of large data repositories and machine learning for discovery, decision support, and innovation. Previously, he was Professor of Astrophysics and Computational Science at George Mason University for 12 years where he did research, taught, and advised students in data science. Prior to that, Kirk spent nearly 20 years supporting data systems activities on NASA space science programs, which included a period as NASA’s Data Archive Project Scientist for the Hubble Space Telescope. Dr. Borne has a B.S. degree in Physics from LSU, and a Ph.D. in Astronomy from Caltech. In 2016 he was elected Fellow of the International Astrostatistics Association for his lifelong contributions to big data research in astronomy. As a global speaker, he has given hundreds of invited talks worldwide, including conference keynote presentations at many dozens of data science, A.I. and big data analytics events globally. 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. He was recently identified as the #1 digital influencer worldwide for 2018-2019. You can follow him on Twitter at @KirkDBorne.

Solving the Data Scientist’s Cold-Start Problem with Machine Learning Examples(Half-Day Training)

Atypical Applications of Typical Machine Learning Algorithms(Half-Day Training)

Data Science InstructorGeneral Assembly

Bethany Poulin

Bethany Poulin is a data scientist and educator with expertise in statistical analysis, data visualization, and complex algorithmic problem-solving. She has worked as a professional data scientist and educator for the last 4 years and loves sharing what she has learned with her students. Her unusual background in Fine Arts and experience teaching high school give her a unique perspective on both problem-solving and the learning-teaching process. Prior to teaching this part-time course, she was a lead instructor in our Data Science Immersive program on the Boston campus. She teaches simply because she loves students and enjoys being a part of their success. She holds a BFA in Professional Photography from Rochester Institute of Technology, did post bachelor’s studies and the University of Montana in Environmental Biology, where she was recognized nationally as a Morris K Udall Scholar, is one semester away from an MS in Data Science from the City University of New York. She is has presented at PyOhio 2018 and 2019 and gave a presentation at ODSC East in 2019. In her spare time, Bethany is an avid fly fisherman, potter, and maker.

Introduction to Shiny Application Development(Half-Day Training)

Computer Science ProfessorOlin College and Author of Think Python, Think Bayes, Think Stats

Allen Downey, PhD

Allen Downey is a Professor of Computer Science at Olin College of Engineering in Needham, MA. He is the author of several books related to computer science and data science, including Think Python, Think Stats, Think Bayes, and Think Complexity. Prof Downey has taught at Colby College and Wellesley College, and in 2009 he was a Visiting Scientist at Google. He received his Ph.D. in Computer Science from U.C. Berkeley, and M.S. and B.S. degrees from MIT.

Bayesian Statistics Made Simple(Workshop)

Supervisor, Data EducationChildren's Hospital of Philadelphia

Joy Payton

Joy Payton is a cloud engineer, data scientist, and adjunct professor who specializes in helping biomedical professionals conduct reproducible computational research. In addition to moving medicine forward through principles of open science and reproducibility, Joy also enjoys teaching citizen scientists how to use public data repositories to understand their own communities better and advocate for change from a data-centric perspective. Her various roles allow Joy to lead efforts to teach people how to write their first line of code and help anyone who’s interested climb the data science learning curve. Currently employed by the Children’s Hospital of Philadelphia and Yeshiva University, Joy is always open to hearing about open-source, data-centric volunteer opportunities for herself and her students.

Using Google BigQuery Public Data With Colab Notebooks: From Insight to Knowledge Using Cloud Computing, SQL, and Python to Map NYC Fall Tree Color!(Workshop)

Data Science ManagerEY

Lara Kattan

Lara is a Risk Management Specialist at Federal Reserve Bank of Chicago and occasional adjunct at the University of Chicago’s Booth School of Business, teaching Python and R. Previously she’s taught a data science Bootcamp and built risk models for large financial institutions at McKinsey & Co.

Probabilistic Programming and Bayesian Inference with Python (Half-Day Training)

Chief Data Scientist, Author of R for Everyone, ProfessorLander Analytics, Columbia Business School

Jared Lander

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 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 fundraising 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.

Manipulating and Visualizing Data with R(Half-Day Training)

Director of EngineeringUrsa Labs / RStudio

Neal Richardson, PhD

Experienced product manager and engineering leader with a focus on data science and data products, currently working on the Apache Arrow project with the Ursa Labs team. Previously, he led product development at Crunch.io, where he grew a geographically distributed, cross-functional team from 5 to 25, shaped the product vision and roadmap, and developed processes to deliver a high-quality product tailored to the needs of survey researchers.
He is also an open-source software developer, author of several packages for the R language, and contributor to many others in R and Python. Many of his projects sit at the intersection of data science and web services. In his projects and public talks, he is a strong advocate for intuitive user experience/API design and for comprehensive test coverage.
Prior to working in software development, he received a Ph.D. in Political Science from UC Berkeley and worked in data science at YouGov, where analyzed survey data and developed tools to streamline data pipelines and workflows. Among the statistical tools and methods he has used professionally and in peer-reviewed publications are experimental and quasi-experimental methods, text classification and sentiment analysis, survey weighting, and web scraping.

Fast Data Access in R and Python with Apache Arrow(Workshop)

Director, GPU-accelerated machine learningNVIDIA

John Zedlewski

John Zedlewski is the director of GPU-accelerated machine learning on the NVIDIA Rapids team. Previously, he worked on deep learning for self-driving cars at NVIDIA, deep learning for radiology at Enlitic, and machine learning for structured healthcare data at Castlight. He has an MA/ABD in economics from Harvard with a focus in computational econometrics and an AB in computer science from Princeton.

GPU-accelerated Data Science with RAPIDS (Workshop)

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.

Principal Software Engineer, AuthorDomino Data Lab

Dean Wampler, PhD

Dean Wampler is an expert in data engineering for scalable streaming data systems and applications of machine learning and artificial intelligence (ML/AI). He is a Principal Software Engineer at Domino Data Lab. Previously he worked at Anyscale and Lightbend, where he worked on scalable ML with Ray and distributed streaming data systems with Apache Spark, Apache Kafka, Kubernetes, and other tools. Dean is the author of “Programming Scala”, “What Is Ray?”, “Fast Data Architectures for Streaming Applications”, “Functional Programming for Java Developers”, and the coauthor of “Programming Hive”, all from O’Reilly. He is a contributor to several open source projects, a frequent conference speaker. He also co-organizes several conferences around the world and several user groups in Chicago. Dean has a Ph.D. in Physics from the University of Washington. Find Dean on Twitter: @deanwampler.

Hands-on Reinforcement Learning with Ray RLlib(Half-Day Training)

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)

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)

Machine Learning ScientistDraper

Rebecca Russell, PhD

Rebecca Russell is a Senior Machine Learning Scientist in the Perception and Autonomy group at Draper. She received her Ph.D. from MIT and B.S. from Caltech, both in physics. Since joining Draper in 2016, Dr. Russell has lead work on using deep learning to solve technical challenges in a wide variety of domains including robotics, autonomous vehicles, medical image analysis, and cybersecurity. Her current research is focused on creating trustworthy and competency-aware deep learning autonomous systems.

Uncertainty in Deep Learning(Workshop)

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Companies Represented at ODSC Bootcamp

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Cambridge, MA 02142
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