Past Training & Workshop Sessions
2024 TRAINING COMING SOON
Workshop: Introduction to R
Workshop: Introduction to Large Language Models
Workshop: Personalizing LLMs with a Feature Store
Half-Day Training: Data Wrangling with Python & Pandas
Workshop: Prompting with OpenAI and Prompting Safety Guardrails
Half-Day Training: MLOps: Monitoring and Managing Drift
Workshop: What is a Time-series Database and Why do I Need One?
Workshop: Causal AI: from Data to Action
Workshop: Building LLM-powered Knowledge Workers over your Data with LlamaIndex
Workshop: Aligning Open-source LLMs Using Reinforcement Learning from Feedback
Workshop: Stable Diffusion: A New Frontier for Text-to-Image Paradigm
Tutorial: Evaluation Techniques for Large Language Models
Workshop: Idiomatic Pandas
Tutorial: How to Practice Data-Centric AI and Have AI improve its Own Dataset
Workshop: Overview of Mojo🔥: Usability of Python, Performance of C
Tutorial: Machine Learning for High-Risk Applications – Techniques for Responsible AIO
Workshop: Using Graphs for Large Feature Engineering Pipelines
Tutorial: Automating Business Processes Using LangChain
Tutorial: No-Code and Low-Code AI: A Practical Project Driven Approach to ML
Workshop: Data for Social Good – Find Your Paradise!
Workshop: The AI Paradigm Shift: Under the Hood of a Large Language Models
Workshop: Learn how to Efficiently Build and Operationalize Time Series Models in 2023
Tutorial: Deploying Trustworthy Generative AI
Workshop: Building Using Llama 2
Half-Day Training: Introduction to Math for Data Science
Workshop: Introduction to Natural Language Processing(NLP)
Workshop: Bridging the Gap: Light Code Solutions to Uniting Social Science and Modern Knowledge Graphs
Workshop: Anomaly Detection for CRM Production Data
Half-Day Training: Generative AI, Autonomous AI Agents, and AGI – How new Advancements in AI will Improve the Products we Build
Tutorial: Massively Speed-Up your Learning Algorithm, with Stochastic Thinning
Workshop: Introduction to Prompt Engineering
Workshop: Machine Learning with XGBoost
Workshop: Fine Tuning Large Language Models and Embedding Models
Half-Day Training: Uncertainty Quantification: Approaches and Methods
Workshop: Building a Q&A Bot with Large Language Models, Vector Search, and LangChain
Workshop: Advance LLMs – Agents, Parameter Efficient Fine-Tuning, and RAG
Tutorial: Machine Learning Has Become Necromancy
Workshop: Beyond Demos and Prototypes: How to Build Production-Ready Applications Using Open-Source LLMs
Tutorial: Prompt Optimization with GPT-4 and Langchain
Workshop: Missing Data: A Synthetic Data Approach for Missing Data Imputation
Workshop: Graphs: The Next Frontier of GenAI Explainability
Workshop: Facial Recognition from Scratch with Python and JS
Tutorial: A Background to LLMs and Intro to PaLM 2: A Smaller, Faster and More Capable LLM
Half-day Training: Architecting Data: A Deep Dive Into the World of Synthetic Data
Workshop: The Rise of a Full Stack Data Scientist: Powered by Python
Half-day Training: Retrieval Augmented Generation (RAG) 101: Building an Open-Source “ChatGPT for Your Data” with Llama 2, LangChain, and Pinecone
Half-day Training: Statistic for Data Science
Featured Past Top Instructor
2024 INSTRUCTORS COMING SOON
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.
Oliver Zeigermann
Oliver Zeigermann has been developing software with different approaches and programming languages for more than 3 decades. In the past decade, he has been focusing on Machine Learning and its interactions with humans.
MLOps: Monitoring and Managing Drift(Training)
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.
Hao Zhang, PhD
Hao is currently a postdoctoral researcher at the Sky Lab, UC Berkeley, working with Prof. Ion Stoica. He is recently working on the Alpa project and the Sky project, aiming at democratizing large models like GPT-3. He is an Assistant Professor at Halıcıoğlu Data Science Institute and Department of Computer Science and Engineering (affiliate) at UC San Diego in Fall 2023.
He research is primarily focused on large-scale distributed ML in the joint context of ML and systems, concerning performance, usability, cost, and privacy. His work spans across distributed ML algorithms, large models, parallelisms, performance optimizations, system architectures, ML privacy, and AutoML, with applications in computer vision, natural language processing, and healthcare.
Jeff Tao
Jeff Tao is the founder and CEO of TDengine. He has a background as a technologist and serial entrepreneur, having previously conducted research and development on mobile Internet at Motorola and 3Com and established two successful tech startups. Foreseeing the explosive growth of time-series data generated by machines and sensors now taking place, he founded TDengine in May 2017 to develop a high-performance time-series database purpose-built for modern IoT and IIoT businesses.
What is a Time-series Database and Why do I Need One?(Workshop)
Alison Cossette
Alison Cossette is a dynamic Data Science Strategist, Educator, and Podcast Host. As a Developer Advocate at Neo4j specializing in Graph Data Science, she brings a wealth of expertise to the field. With her strong technical background and exceptional communication skills, Alison bridges the gap between complex data science concepts and practical applications.
Alison’s passion for responsible AI shines through in her work. She actively promotes ethical and transparent AI practices and believes in the transformative potential of responsible AI for industries and society. Through her engagements with industry professionals, policymakers, and the public, she advocates for the responsible development and deployment of AI technologies.
Alison’s academic journey includes pursuing her Master of Science in Data Science program, specializing in Artificial Intelligence, at Northwestern University and research with Stanford University Human-Computer Interaction Crowd Research Collective. Alison combines academic knowledge with real-world experience. She leverages this expertise to educate and empower individuals and organizations in the field of data science.
Overall, Alison Cossette’s multifaceted background, commitment to responsible AI, and expertise in data science make her a respected figure in the field. Through her role as a Developer Advocate at Neo4j and her podcast, she continues to drive innovation, education, and responsible practices in the exciting realm of data science and AI.
Bridging the Gap: Light Code Solutions to Uniting Social Science and Modern Knowledge Graphs(Workshop)
From Nodes to Natural Language: Grounding LLMs with Graphs & Graph Data Science(Talk)
Dr. Andre Franca
Andre joined causaLens from Goldman Sachs, where he was an executive director in the Model Risk Management group in Hong Kong and Frankfurt. Today he is working with industry leading, global organisations to apply cutting edge Causal AI research in production level solutions that empower individuals and teams to make better decisions. Andre received his PhD in theoretical physics from the University of Munich, where he studied the interplay between quantum mechanics and general relativity in black-holes.
Causal AI: from Data to Action(Workshop)
Geeta Shankar
Geeta Shankar is a software engineer who specializes in leveraging data for business success. With expertise in computer science, data science, machine learning, and artificial intelligence, she stays updated with the latest data-driven innovations. Her Indian classical music background has taught her the value of sharp thinking, spontaneity, and connecting with diverse individuals. Geeta uses these skills to translate complex data into meaningful insights that enhance performance and customer experiences.
Rajiv Shah, PhD
Rajiv Shah is a machine learning engineer at Hugging Face who focuses on enabling enterprise teams to succeed with AI. Rajiv is a leading expert in the practical application of AI. Previously, he led data science enablement efforts across hundreds of data scientists at DataRobot. He was also a part of data science teams at Snorkel AI, Caterpillar, and State Farm. Rajiv is a widely recognized speaker on AI, published over 20 research papers, and received over 20 patents, including sports analytics, deep learning, and interpretability. Rajiv holds a PhD in Communications and a Juris Doctor from the University of Illinois at Urbana Champaign. While earning his degrees, he received a fellowship in Digital Government from the John F. Kennedy School of Government at Harvard University. He also has a large following on AI-related short videos on Tik Tok and Instagram at @rajistics.
Evaluation Techniques for Large Language Models(Tutorial)
Brian Lucena, PhD
Brian Lucena is Principal at Numeristical, where he advises companies of all sizes on how to apply modern machine learning techniques to solve real-world problems with data. He is the creator of three Python packages: StructureBoost, ML-Insights, and SplineCalib. In previous roles he has served as Principal Data Scientist at Clover Health, Senior VP of Analytics at PCCI, and Chief Mathematician at Guardian Analytics. He has taught at numerous institutions including UC-Berkeley, Brown, USF, and the Metis Data Science Bootcamp.
Uncertainty Quantification: Approaches and Methods(Training)
Jeffrey Yau, PhD
Jeffrey Yau is currently Chief Data & A.I. Officer at Fanatics Collectibles. Most recently, he served as Global Head of Data Science, Analytics & Engineering at Amazon Music where he oversaw multiple teams who developed both insights-packed analytics and end-to-end statistical and machine learning systems. Prior to Amazon, Jeffrey worked at WalmartLabs as the VP of Data Science & Engineering where he led the team responsible for powering Walmart store mobile apps and the entire store finance system. Further, his team created end-to-end machine learning systems for key business initiatives and had a multi-billion dollar impact annually on Walmart U.S.
Over the years, he has held various senior level positions in quantitative finance at global investment management firm AllianceBernstein, consulting firm Data Science at Silicon Valley Data Science, multinational financial services company Charles Schwab Corporation, and the world’s leading professional services firm KPMG. He began his career as a tenure-track Assistant Professor of Economics at Virginia Tech, and he was an adjunct professor at UC Berkeley, Cornell, and NYU, teaching machine learning and advanced statistical modeling for finance and business.
Jerry Liu
Jerry is the co-founder/CEO of LlamaIndex, an open-source tool that provides a central data management/query interface for your LLM application. Before this, he has spent his career at the intersection of ML, research, and startups. He led the ML monitoring team at Robust Intelligence, did self-driving AI research at Uber ATG, and worked on recommendation systems at Quora. He graduated from Princeton in 2017 with a degree in CS.
Building LLM-powered Knowledge Workers over your Data with LlamaIndex(Workshop)
Sinan Ozdemir
Sinan Ozdemir is a mathematician, data scientist, NLP expert, lecturer, and accomplished author. He is currently applying my extensive knowledge and experience in AI and Large Language Models (LLMs) as the founder and CTO of LoopGenius, transforming the way entrepreneurs and startups market their products and services.
Simultaneously, he is providing advisory services in AI and LLMs to Tola Capital, an innovative investment firm. He has also worked as an AI author for Addison Wesley and Pearson, crafting comprehensive resources that help professionals navigate the complex field of AI and LLMs.
Previously, he served as the Director of Data Science at Directly, where my work significantly influenced their strategic direction. As an official member of the Forbes Technology Council from 2017 to 2021, he shared his insights on AI, machine learning, NLP, and emerging technologies-related business processes.
He holds a B.A. and an M.A. in Pure Mathematics (Algebraic Geometry) from The Johns Hopkins University, and he is an alumnus of the Y Combinator program. Sinan actively contribute to society through various volunteering activities.
Sinan’s skill set is strongly endorsed by professionals from various sectors and includes data analysis, Python, statistics, AI, NLP, theoretical mathematics, data science, function analysis, data mining, algorithm development, machine learning, game-theoretic modeling, and various programming languages.
Aligning Open-source LLMs Using Reinforcement Learning from Feedback(Workshop)
Martin Musiol
Long before the buzz surrounding generative AI, Martin Musiol was already advocating for its significance in 2015. Since then, he has been a frequent speaker at conferences, podcasts, and panel discussions, addressing the technological advancements, practical applications, and ethical considerations of generative AI. Martin Musiol is a founder of generativeAI.net, a lecturer on AI to over 3000 students, and publisher of the newsletter ‘Generative AI: Short & Sweet’. As the lead for GenAI Projects in Europe at Infosys Consulting (previously at IBM), Martin Musiol helps companies globally harness the power of generative AI to gain a competitive advantage. -> https://www.linkedin.com/in/martinmusiol1/ and his webpage: https://generativeai.net/
Sandeep Singh
Sandeep Singh is a leader in applied AI and computer vision in Silicon Valley’s mapping industry, and he is at the forefront of developing cutting-edge technology to capture, analyze and understand satellite imagery, visual and location data. With a deep expertise in computer vision algorithms, machine learning and image processing and applied ethics, Sandeep is responsible for creating innovative solutions that enable mapping and navigation software to accurately and efficiently identify and interpret features to remove inefficiencies of logistics and mapping solutions. His work includes developing sophisticated image recognition systems, building 3D mapping models, and optimizing visual data processing pipelines for use in logistics, telecommunications and autonomous vehicles and other mapping applications. With a keen eye for detail and a passion for pushing the boundaries of what’s possible with AI and computer vision, Sandeep’s leadership is driving the future of applied AI forward.
Stable Diffusion: A New Frontier for Text-to-Image Paradigm(Workshop)
Jonas Mueller
Jonas Mueller is Chief Scientist and Co-Founder at Cleanlab, a software company providing data-centric AI tools to efficiently improve ML datasets. Previously, he was a senior scientist at Amazon Web Services developing AutoML and Deep Learning algorithms which now power ML applications at hundreds of the world’s largest companies. In 2018, he completed his PhD in Machine Learning at MIT, also doing research in NLP, Statistics, and Computational Biology.
Jonas has published over 30 papers in top ML and Data Science venues (NeurIPS, ICML, ICLR, AAAI, JASA, Annals of Statistics, etc). This research has been featured in Wired, VentureBeat, Technology Review, World Economic Forum, and other media. He has also contributed open-source software, including the fastest-growing open-source libraries for AutoML (https://github.com/awslabs/autogluon) and Data-Centric AI (https://github.com/cleanlab/cleanlab).
How to Practice Data-Centric AI and Have AI improve its Own Dataset(Tutorial)
Fabiana Clemente
Fabiana Clemente is the co-founder and CDO of YData, combining Data Understanding, Causality, and Privacy as her main fields of work and research, with the mission to make data actionable for organizations. Passionate for data, Fabiana has vast experience leading data science teams in startups and multinational companies. Host of “When Machine Learning meets privacy” podcast and a guest speaker at Datacast and Privacy Please, the previous WebSummit speaker, was recently awarded “Founder of the Year” by the South Europe Startup Awards.
Missing Data: A Synthetic Data Approach for Missing Data Imputation(Workshop)
Amy Hodler
Amy Hodler is an evangelist for graph analytics and responsible AI. She’s the co-author of O’Reilly books on Graph Algorithms and Knowledge Graphs as well as a contributor to the Routledge book, Massive Graph Analytics and Bloomsbury book, AI on Trial. Amy has decades of experience in emerging tech at companies such as Microsoft, Hewlett-Packard (HP), Hitachi IoT, Neo4j, Cray, and RelationalAI. Amy is the founder of GraphGeeks.org promoting connections everywhere.
Gwendolyn D. Stripling, Ph.D.
Gwendolyn Stripling, Ph.D., is an Artificial Intelligence and Machine Learning Content Developer at Google Cloud. Stripling is author of the widely popular YouTube video, “Introduction to Generative AI” and of the O’Reilly Media book “Low-Code AI: A Practical Project Driven Approach to Machine Learning”. They are also the author of the LinkedIn Learning video “Introduction to Neural Networks”. Stripling is an Adjunct Professor and member of Golden Gate University’s Masters in Business Analytics Advisory Board. Stripling enjoys speaking on AI/ML, having presented at Dominican University of California’s Barowsky School of Business Analytics, Golden Gate University’s Ageno School of Business Analytics, and numerous Tech conferences.
No-Code and Low-Code AI: A Practical Project Driven Approach to ML(Tutorial)
Michelle Yi
Michelle is a technology leader that specializes in machine learning and cloud computing. She has 15 years of experience in the technology industry, contributed to the original IBM Watson showcased on Jeopardy, and enjoys building and leading teams that develop and deploy AI solutions to solve real-world problems. Michelle is passionate about diversity, STEM education/careers for our minority communities, and serves both on the board of Women in Data and as an avid volunteer for Girls Who Code.
Building Generative AI Applications: An LLM Case Study(Talk)
James Phoenix
James is a full-stack engineer that specialises in automating marketing and business processes with AI based solutions.
Serg Masis
Serg Masís has been at the confluence of the internet, application development, and analytics for the last two decades. He’s an Agronomic Data Scientist at Syngenta, a leading agribusiness company with a mission to improve global food security. Before that role, he co-founded a search engine startup, incubated by Harvard Innovation Labs, that combined the power of cloud computing and machine learning with principles in decision-making science to expose users to new places and events efficiently. Whether concerning leisure activities, plant diseases, or customer lifetime value, Serg is passionate about providing the often-missing link between data and decision-making. He wrote the bestselling book “Interpretable Machine Learning with Python” and is currently working on a new book titled “DIY AI” with do-it-yourself projects for AI hobbyists and practitioners alike.
Andrew Dai
Andrew Dai did his PhD at the University of Edinburgh before joining Google Brain 9 years ago in 2014 where he did research on language models, story generation and conversational agents and products including SmartReply. He moved to Google Health in 2017 to research deep learning for medical records. He then returned to continue research at Google Brain (now Google Deepmind) in 2020 and since then has co-led the development and training of LLMs including PaLM 2 and GLaM. Andrew also is a lead for Google SGE modelling, Gemini and data research and is excited by the new abilities we see from LLMs.
A Background to LLMs and Intro to PaLM 2: A Smaller, Faster and More Capable LLM(Tutorial)
Valentina Alto
Valentina is a Data Science MSc graduate and Cloud Specialist at Microsoft, focusing on Analytics and AI workloads within the manufacturing and pharmaceutical industry since 2022. She has been working on customers’ digital transformations, designing cloud architecture and modern data platforms, including IoT, real-time analytics, Machine Learning, and Generative AI. She is also a tech author, contributing articles on machine learning, AI, and statistics, and recently published a book on Generative AI and Large Language Models.
In her free time, she loves hiking and climbing around the beautiful Italian mountains, running, and enjoying a good book with a cup of coffee.
The AI Paradigm Shift: Under the Hood of a Large Language Models(Workshop)
Vino Duraisamy
Vino is a Developer Advocate for Snowflake. She started as a software engineer at NetApp, and worked on data management applications for NetApp data centers when on-prem data centers were still a cool thing. She then hopped onto the cloud and big data world and landed at the data teams of Nike and Apple. There she worked mainly on batch processing workloads as a data engineer, built custom NLP models as an ML engineer and even touched upon MLOps a bit for model deployments. When she is not working with data, you can find her doing yoga or strolling the golden gate park and ocean beach.
The Rise of a Full Stack Data Scientist: Powered by Python(Workshop)
Fabio Buso
Fabio Buso is a co-founder and VP of Engineering at Hopsworks, leading the Feature Store development team. Fabio holds a master’s degree in Cloud Computing and Services with a focus on data intensive applications.
Personalizing LLMs with a Feature Store(Workshop)
Thomas J. Fan
Thomas J. Fan is a Staff Software Engineer at Quansight Labs and is a maintainer for scikit-learn, an open-source machine learning library for Python. Previously, Thomas worked at Columbia University to improve interoperability between scikit-learn and AutoML systems. He is a maintainer for skorch, a neural network library that wraps PyTorch. Thomas has a Masters in Mathematics from NYU and a Masters in Physics from Stony Brook University.
Introduction to scikit-learn: Machine Learning in Python (Training)
Leonardo De Marchi
Leonardo De Marchi holds a Master in Artificial intelligence and has worked as a Data Scientist in the sports world, with clients such as the New York Knicks. He now works in Thomson Reuters as VP of Labs, and also provides consultancy and training for small and large companies. His previous experience includes being Head of Data Science and Analytics in Bumble, the largest dating site with over 500 million users, heading the team through acquisition and an IPO.
Generative AI(Training)
Nikolay Manchev, PhD
Nikolay is an experienced Data Science professional who currently leads the EMEA Data Science team at Domino Data Lab. He holds an MSc in Software Technologies, an MSc in Data Science, and is currently undertaking postgraduate research at King’s College London. His area of expertise is Statistics, Mathematics, and Data Science in general, and his research interests are in Neural Networks with emphasis on biological plausibility. He writes articles and blogs regularly and speaks at various European conferences (ODSC, Big Data Spain, Strata, Big Data London etc.) to build awareness about data science and artificial intelligence. He is also the organizer of the London Data Science and Machine Learning meetup and recipient of several technical mastery awards like the Oracle ACE Award and the IBM Outstanding Technical Achievement Award.
Julien Simon
Julien is currently Chief Evangelist at Hugging Face. He’s recently spent 6 years at Amazon Web Services where he was the Global Technical Evangelist for AI & Machine Learning. Prior to joining AWS, Julien served for 10 years as CTO/VP Engineering in large-scale startups.
Hyper-productive NLP with Hugging Face Transformers(Workshop)
More instructors added weekly
More Instructors Coming Soon
Free Pre-Bootcamp Primer Courses
Data, Coding, and AI preparation courses for ODSC Mini-Bootcamps
ODSC Bootcamp Primer Courses
These primer courses can be taken stand alone or as part of our Mini-Bootcamp series. This foundations series is built from the ground up to boost your understanding of data-centric AI
Hosted on Ai+ Training and included FREE as part of your ODSC AI Mini-Bootcamp Pass.
Pre-Bootcamp Workshop Dates *
Pre-Bootcamp Warmup Workshops are available both live and on-demand (post-date) * schedule is subject to change
Data Primer – available on-demand;
SQL – available on-demand;
Programming Primer Course with Python – available on-demand;
AI Primer – available on-demand;
Data Wrangling with Python – available on-demand;
LLMs, Prompt Engineering, & Gen AI – available on-demand;
Beginner to Advanced Level Training
From the Leading Instructors in the Industry
Planned 2024 Topics Include
Large Language Models & Generative AI
Introduction to Large Language Models
Prompting with OpenAI and Prompting Safety Guardrails
Introduction to Prompt Engineering
Fine Tuning Large Language Models & Embedding Models
Fine Tuning Embedding Models
Building a Q&A Bot with LLMS, Vectors, and LangChain
LLMOps – Large Language Model Workflow
Advanced LLMs – LangChain Agents
Advance LLMs – Parameter Efficient Fine-Tuning
Advance LLMs – Retrieval-Augmented Generation (RAG)
AI-Assisted Code Generation Techniques
Machine Learning
MLOps and Machine Learning Pipelines
No-Code and Low-Code 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
Real-time Streaming Analytics
Causal Inference with Machine Learning
Auto Machine Learning (AutoML)
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
Distributed Machine Learning & Deep Learning
NLP
Introduction to NLP and Topic Modeling
Self Supervised learning; new techniques
Transfer Learning in NLP
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
Ai+ Training Tracks
Access included with select passes
Ai+ is the only hands-on training platform solely developed for AI practitioners. Keep training with the top names in the industry.
Participate at ODSC East 2024
As part of the global data science community we value inclusivity, diversity, and fairness in the pursuit of knowledge and learning. We seek to deliver a conference agenda, speaker program, and attendee participation that moves the global data science community forward with these shared goals. Learn more on our code of conduct, speaker submissions, or speaker committee pages.
Online registration ends in
Choose your Pass
Virtual Bootcamp Orientation Sessions for in-person and virtual attendees
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)
4 Prep-Bootcamp live tutorials on Data Literacy, AI Literacy, Programming, and SQL (Value $796)
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
Need More Reason To Sign Up?
ODSC Training Includes
Opportunities to form working relationships with some of the world’s top data scientists.
Access to 40+ training sessions and 70+ workshops.
Hands-on experience with the latest frameworks and breakthroughs in data science.
Affordable training–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.
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!