ODSC Team Training
In-Person and Virtual Data Science Training
♦ ODSC East 2023 Training → May 9th to May 11th
Hands-on Training Sessions
Applied Workshops
Hours of Content
Networking Sessions
Attendees
Key Reasons to Send Your Team to ODSC
Upskill Your Team
3 days of learning and work-enhancing experiences with the latest languages, tools, frameworks, and models in Data Science.
Sessions presented by some of the top instructors and practitioners in the AI space.
Certificate of Completion after completing a post-event assessment.
The skills and knowledge acquired have an immediate impact on your organization. Instant ROI
Encourage Innovation
Training presents a prime opportunity to expand the knowledge base of all employees.
Propel your team out of its comfort zone; training and upskilling encourage new ideas.
Investing time and money shows employees they are being valued.
Build a strong company reputation by focusing on employee development. Keep the talent and reduce employee turnover.
Introduce Networked Learning
ODSC stands by the concept of Networked Learning, learning based on the idea of networks and connectivity.
Join the fastest growing network of AI practitioners, sharing knowledge, projects, failures…
Team bonding through learning together, interacting with thought leaders and peers, along with a bit of fun.
Learning and knowledge rest on the diversity of opinions.
Past Data Science Training Instructors
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)
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.
Dr. Jon Krohn
Jon Krohn is Co-Founder and Chief Data Scientist at the machine learning company Nebula. He authored the book Deep Learning Illustrated, an instant #1 bestseller that was translated into seven languages. He is also the host of SuperDataScience, the data science industry’s most listened-to podcast. Jon is renowned for his compelling lectures, which he offers at leading universities and conferences, as well as via his award-winning YouTube channel. He holds a PhD from Oxford and has been publishing on machine learning in prominent academic journals since 2010.
Deep Learning with PyTorch and TensorFlow(Training)
NLP with GPT-4 and other LLMs: From Training to Deployment with Hugging Face and PyTorch Lightning(Training)
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.
When SQL is Not the Best Answer: Identifying “Graph-y” Problems and When Graphs Can Help(Talk)
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.
Andreas Mueller, PhD
Andreas Mueller is a Principal Research SDE at Microsoft (previously Columbia, NYU, Amazon), and author of the O’Reilly book “Introduction to machine learning with Python”, describing a practical approach to machine learning with python and scikit-learn. He is one of the core developers of the scikit-learn machine learning library, and has been co-maintaining it for several years. Andreas is also a Software Carpentry instructor.
Automatic DataFrame Profiling and Visualization for Machine Learning(Talk)
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)
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)
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)
Allen Downey, PhD
Allen Downey is a Staff Scientist at DrivenData and professor emeritus at Olin College. He is the author of several books related to computer science and data science, including Think Python, Think Stats, Think Bayes, and Think Complexity. His blog, Probably Overthinking It, features articles about Bayesian statistics. He received his Ph.D. in Computer Science from U.C. Berkeley, and M.S. and B.S. degrees from MIT.
Causation, Collision, and Confusion: Avoiding the most dangerous error in statistics(Talk)
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.
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)
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.
Machine Learning in R Part I & II(Training)
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)
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)
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)
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)
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(Training)
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)
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)
“ The transformative power of people ”
There’s something powerful about bringing people together. More than simply networking, it breaks silos, creates connections, and presents solutions. Real transformative learning experiences happen in communities.
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Get In TouchConference Team Training
Each ODSC conference hosts many of the best and brightest AI and Data Science Experts on the planet. Why not have your team train with the best? Hosting your team at our venue has many benefits you can take advantage of. Steep group discounts are available.
Corporate Team Training
Let us bring the top data science and training experts to you. We can host our classes in your offices. Alternatively, we can host your team in many locations, including Singapore, Boston, San Francisco, New York City, Bengaluru, London, and Dublin, among others.
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A Few Teams We’ve Hosted
ODSC Training & Mini-Bootcamp
May 8th to May 11th
ODSC Newsletter
Stay current with the latest news and updates in open source data science. In addition, we’ll inform you about our many upcoming Virtual and in person events in Boston, NYC, Sao Paulo, San Francisco, and London. And keep a lookout for special discount codes, only available to our newsletter subscribers!