You’re In Good Company
The ODSC West Virtual Conference 2020 is one of the largest and ONLY applied data science training conferences in the world. Our instructors include some of the core contributors of many open source tools, libraries, and languages. Attend ODSC West 2020 virtually and learn the latest AI & data science topics, tools, and languages from some of the best and brightest minds in the field..
Focus Areas
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Mini-Bootcamp
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Deep Learning
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Machine Learning
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Predictive Analytics
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AI for Engineers
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Natural Language Processing
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Data Visualization
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Data Wrangling
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AI Research
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Data Science at Scale
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Data Science for Good
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Data Science Management
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Data Science Workflows
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AI Business Summit
Training Topics
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Recommendation Systems
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Transfer Learning
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Machine Vision
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Autonomous Machines
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Conversational AI
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Data Science Research
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Artificial Intelligence
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Speech Recognition
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Unsupervised Learning
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Image Classification
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Self-driving Vehicles
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Machine Translation
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200+ Speakers over 4 days
Open Source Tools
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Tensorflow, PyTorch, MXNet, Caffe 2, Chainer, CNTK
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Python, Jupyter Notebooks, XGBoost
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R programming, Julia, Scala, Stan,
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Apache Spark, MLlib, Streaming
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Scikit-learn, Theano, Shogun, Pylearn2
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Azure ML, Amazon ML,H20.ai, Cloud ML
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Kubeflow, Kubernetes, Seldon, Airflow,
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Neo4J, D3.js, R-Shiny
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Hadoop, Apache Storm, Apache Flink, Kafka, Druid
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SAS Viya
Participate at ODSC West 2020
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.
ODSC West Keynotes
Suchi Saria, PhD
Director of the Machine Learning and Healthcare Lab, John C. Malone Endowed Chair, Founder of Bayesian Health, MIT Technology Review’s 35 Innovators Under 35, and a World Economic Forum Young Global Leader
Johns Hopkins University
Jeannette M. Wing, PhD
Avanessians Director & Prof. of Computer Science, Former CVP of Microsoft Research, Former Asst. Dir. of the Computer and Information Science and Engineering Directorate at NSF, Former Dean & Head of the Department of CS at CMU
Columbia University
Ion Stoica, PhD
Professor of Computer Science, Head of RISELab. Co-founder of Anyscale, Databricks, and Conviva Networks, ACM Fellow, SIGOPS Hall of Fame Award (2015), SIGCOMM Test of Time Award (2011)
UC Berkeley
Raluca Ada Popa, PhD
Cybersecurity & Applied Cryptography Professor, MIT Technology Review's 35 Under 35, Recipient of Intel Early Career Faculty Honor award, George M. Sprowls Award for best MIT CS doctoral thesis, Co-founder of PreVeil
UC Berkeley
Zoubin Ghahramani, PhD
Chief Scientist, Founding Director of the AlanTuring Institute, Prof. of Information Engineering & Deputy Director of the Leverhulme Centre for the Future of Intelligence, Fellow of St John's College Cambridge and of the Royal Society
Uber | The University of Cambridge
Azalia Mirhoseini, PhD
Senior Research Scientist at Google Brain. Advisor at Cmorq. Co-founder Machine Learning for Systems Moonshot at Brain focusing on Deep RL. MIT Technology Review 35 Under 35 Award
Google Brain
ODSC West Featured Speakers
Tom M. Mitchell, PhD
Tom M. Mitchell is the Founders University Professor at Carnegie Mellon University, where he founded the world’s first Machine Learning Department, and authored the widely used textbook “Machine Learning.” Mitchell’s research over the years has focused on machine learning, artificial intelligence, cognitive neuroscience, and the impact of AI on society.
Mitchell currently co-directs the CMU–Squirrel AI Lab on Personalized Education at Scale, which is pursuing the use of artificial intelligence to transform personalized education over the coming years.
A former president of the Association for the Advancement of Artificial Intelligence (AAAI), Mitchell is a fellow of both the AAAI and the American Association for the Advancement of Science, and winner of the 2007 AAAI Distinguished Service Award. He was elected in 2010 to the U.S. National Academy of Engineering, and in 2016 to the American Academy of Arts and Sciences. His AI research has been featured in media ranging from the New York Times, to CBS’s 60 Minutes, to PBS’s “NOVA Science NOW,” to Chinese national television.
Gill Bejerano, PhD
Gill Bejerano is an Associate Professor of Developmental Biology and Computer Science at Stanford University, and a member of the Stanford Artificial Intelligence (AI) Laboratory and Bio-X program. Mr. Bejerano is a pioneer of Human Genome research. He is the discoverer of “Ultraconserved Elements”, human genomic regions that defy understanding of molecular evolution. He has also done influential work in applying Markovian models to biosequence analysis, and in showing that co-option of junk DNA into functional roles is an under-appreciated force shaping the evolution of the Human Genome. He serves Member of the Technical Advisory Board of Numenta, Inc. He holds a triple BSc in Mathematics, Physics and Computer Science (summa cum laude) and a PhD in Computer Science (Machine Learning applications in Bioinformatics) from the Hebrew University of Jerusalem.
Lisa Amini, PhD
Dr. Lisa Amini is the Director of IBM Research Cambridge, which is also home to the MIT-IBM Watson AI Lab, and of IBM’s AI Horizons Network. Lisa was previously Director of Knowledge & Reasoning Research in the Cognitive Computing group at IBM’s TJ Watson Research Center in New York, and she is also an IBM Distinguished Engineer. Lisa was the founding Director of IBM Research Ireland, and the first woman Lab Director for an IBM Research Global (i.e., non-US) Lab (2010-2013). In this role she developed the strategy and led researchers in advancing science and technology for intelligent urban and environmental systems (Smarter Cities), with a focus on creating analytics, optimizations, and systems for sustainable energy, constrained resources (e.g., urban water management), transportation, and the linked open data systems that assimilate and share data and models for these domains. She earned her PhD degree in Computer Science from Columbia University.
Jenn Wortman Vaughan, PhD
Jenn Wortman Vaughan is a Senior Principal Researcher at Microsoft Research, New York City. Her research background is in machine learning and algorithmic economics. She is especially interested in the interaction between people and AI, and has often studied this interaction in the context of prediction markets and other crowdsourcing systems. In recent years, she has turned her attention to fair and interpretable machine learning as part of MSR’s FATE group. Jenn came to MSR in 2012 from UCLA, where she was an assistant professor in the computer science department. She completed her Ph.D. at the University of Pennsylvania in 2009 and subsequently spent a year as a Computing Innovation Fellow at Harvard. She is the recipient of Penn’s 2009 Rubinoff dissertation award for innovative applications of computer technology, a National Science Foundation CAREER award, a Presidential Early Career Award for Scientists and Engineers (PECASE), and a handful of best paper awards. In her “spare” time, Jenn is involved in a variety of efforts to provide support for women in computer science; most notably, she co-founded the Annual Workshop for Women in Machine Learning, which has been held each year since 2006.
Intelligibility Throughout the Machine Learning Life Cycle(Talk)
Gaël Varoquaux, PhD
Gaël Varoquaux is an Inria faculty researcher working on data science and brain imaging. He has a joint position at Inria (French Computer Science National research) and in the Neurospin brain research institute. His research focuses on using data and machine learning for scientific inference, applying it to brain-imaging data to understand cognition, as well as developing tools that make it easier for non-specialists to use machine learning. Years before the NSA, he was hoping to make bleeding-edge data processing available across new fields, and he has been working on a mastermind plan building easy-to-use open-source software in Python. He is a core developer of scikit-learn, joblib, Mayavi and nilearn, a nominated member of the PSF, and often teaches scientific computing with Python using the scipy lecture notes.
Kurt Keutzer, PhD
Kurt received his Ph.D. degree in Computer Science from Indiana University in 1984 and then joined the research division of AT&T Bell Laboratories. In 1991 he joined Synopsys, Inc. where he ultimately became Chief Technical Officer and Senior Vice-President of Research. In 1998 Kurt became Professor of Electrical Engineering and Computer Science at the University of California at Berkeley. Kurt’s research now focuses on systems issues associated with the application of Deep Learning to computer vision, speech recognition, natural language processing, and finance.
Kurt has published six books, over 250 refereed articles, and is among the most highly cited authors in Hardware and Design Automation. Kurt was elected a Fellow of the IEEE in 1996. At the 50th Design Automation Conference Kurt received a number of awards reflecting achievements over the 50 year history of the conference. These included “Top Ten Cited Author” and “Top Ten Cited Paper.” He was also recognized as among one of only three people to have received four Best Paper Awards in the history of the conference. Kurt’s research on Deep Learning has also received Best Paper Awards at the Embedded Vision Workshop and at the International Conference on Parallel Processing.
As an entrepreneur Kurt has served as an angel investor and advisor to over twenty-five start-up companies including C-Cube Microsystems, Coverity, Simplex, and Tensilica. Kurt co-founded DeepScale with his PhD student Forrest Iandola. DeepScale was acquired by Tesla in 2019.
Tian Zheng, PhD
Tian Zheng is Professor and Department Chair of Statistics at Columbia University. She develops novel methods for exploring and understanding patterns in complex data from different application domains. Her current projects are in the fields of statistical machine learning, spatiotemporal modeling and social network analysis. Professor Zheng’s research has been recognized by the 2008 Outstanding Statistical Application Award from the American Statistical Association (ASA), the Mitchell Prize from ISBA and a Google research award. She became a Fellow of American Statistical Association in 2014. Professor Zheng is the recipient of the 2017 Columbia’s Presidential Award for Outstanding Teaching. From 2018-2020, she has been the chair-elect, chair and past-chair for ASA’s section on Statistical Learning and Data Science.
Topic-Adjusted Visibility Metric for Scientific Articles(Track Keynote)
Oren Etzioni, PhD
Dr. Oren Etzioni has served as the Chief Executive Officer of the Allen Institute for AI (AI2) since its inception in 2014. He has been a Professor at the University of Washington’s Computer Science department since 1991, and a Venture Partner at the Madrona Venture Group since 2000. He has garnered several awards including Seattle’s Geek of the Year (2013), the Robert Engelmore Memorial Award (2007), the IJCAI Distinguished Paper Award (2005), AAAI Fellow (2003), and a National Young Investigator Award (1993). He has been the founder or co-founder of several companies, including Farecast (sold to Microsoft in 2008) and Decide (sold to eBay in 2013). He has written commentary on AI for The New York Times, Nature, Wired, and the MIT Technology Review. He helped to pioneer meta-search (1994), online comparison shopping (1996), machine reading (2006), and Open Information Extraction (2007). He has authored over 100 technical papers that have garnered over 2,000 highly influential citations on Semantic Scholar. He received his Ph.D. from Carnegie Mellon in 1991 and his B.A. from Harvard in 1986.
Charles Givre
Charles Givre recently joined JP Morgan Chase works as a data scientist and technical product manager in the cybersecurity and technology controls group. Prior to joining JP Morgan, Mr. Givre worked as a lead data scientist for Deutsche Bank. Mr. Givre worked as a Senior Lead Data Scientist for Booz Allen Hamilton for seven years where he worked in the intersection of cybersecurity and data science. At Booz Allen, Mr. Givre worked on one of Booz Allen’s largest analytic programs where he led data science efforts and worked to expand the role of data science in the program. Mr. Givre is passionate about teaching others data science and analytic skills and has taught data science classes all over the world at conferences, universities and for clients. Mr. Givre taught data science classes at BlackHat, the O’Reilly Security Conference, the Center for Research in Applied Cryptography and Cyber Security at Bar Ilan University. He is a sought-after speaker and has delivered presentations at major industry conferences such as Strata-Hadoop World, Open Data Science Conference and others. One of Mr. Givre’s research interests is increasing the productivity of data science and analytic teams, and towards that end, he has been working extensively to promote the use of Apache Drill in security applications and is a committer and PMC Member for the Drill project. Mr. Givre teaches online classes for O’Reilly about Drill and Security Data Science and is a coauthor for the O’Reilly book Learning Apache Drill. Prior to joining Booz Allen, Mr. Givre, worked as a counterterrorism analyst at the Central Intelligence Agency for five years. Mr. Givre holds a Masters Degree in Middle Eastern Studies from Brandeis University, as well as a Bachelors of Science in Computer Science and a Bachelor’s of Music both from the University of Arizona. Mr. Givre blogs at thedataist.com and tweets @cgivre.
Rapid Data Exploration and Analysis with Apache Drill(Half-Day Training)
Tina Eliassi-Rad, PhD
Tina Eliassi-Rad is a Professor of Computer Science at Northeastern University. She is also a core faculty member at Northeastern University’s Network Science Institute. Prior to joining Northeastern, Tina was an Associate Professor of Computer Science at Rutgers University; and before that she was a Member of Technical Staff and Principal Investigator at Lawrence Livermore National Laboratory. Tina earned her Ph.D. in Computer Sciences at the University of Wisconsin-Madison. Her research is rooted in data mining and machine learning; and spans theory, algorithms, and applications of big data from networked representations of physical and social phenomena. She has over 100 peer-reviewed publications (including a few best paper and best paper runner-up awardees). Tina’s work has been applied to personalized search on the World-Wide Web, statistical indices of large-scale scientific simulation data, fraud detection, mobile ad targeting, cyber situational awareness, and ethics in machine learning. Her algorithms have been incorporated into systems used by the government and industry (e.g., IBM System G Graph Analytics) as well as open-source software (e.g., Stanford Network Analysis Project). In 2017, Tina served as the program co-chair for the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining and as the program co-chair for the International Conference on Network Science . In 2020, she is serving as the program co-chair for the International Conference on Computational Social Science. Tina received an Outstanding Mentor Award from the Office of Science at the US Department of Energy in 2010; and became a Fellow of the ISI Foundation in Turin Italy in 2019.
Julie A. Shah, PhD
Julie Shah is an Associate Professor in the Department of Aeronautics and Astronautics at MIT and leads the Interactive Robotics Group of the Computer Science and Artificial Intelligence Laboratory. Shah received her SB (2004) and SM (2006) from the Department of Aeronautics and Astronautics at MIT, and her PhD (2010) in Autonomous Systems from MIT. Before joining the faculty, she worked at Boeing Research and Technology on robotics applications for aerospace manufacturing. She has developed innovative methods for enabling fluid human-robot teamwork in time-critical, safety-critical domains, ranging from manufacturing to surgery to space exploration. Her group draws on expertise in artificial intelligence, human factors, and systems engineering to develop interactive robots that emulate the qualities of effective human team members to improve the efficiency of human-robot teamwork. In 2014, Shah was recognized with an NSF CAREER award for her work on “Human-aware Autonomy for Team-oriented Environments,” and by the MIT Technology Review TR35 list as one of the world’s top innovators under the age of 35. Her work on industrial human-robot collaboration was also recognized by the Technology Review as one of the 10 Breakthrough Technologies of 2013, and she has received international recognition in the form of best paper awards and nominations from the International Conference on Automated Planning and Scheduling, the American Institute of Aeronautics and Astronautics, the IEEE/ACM International Conference on Human-Robot Interaction, the International Symposium on Robotics, and the Human Factors and Ergonomics Society.
What to Expect When You Are Expecting Robots – The Future of Human-Robot Collaboration(Talk)
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.
Deep Learning (with TensorFlow 2)(Full-Day Training)
Rumi Chunara, PhD
Rumi Chunara is an Assistant Professor at NYU, jointly appointed at the Tandon School of Engineering (in Computer Science) and the School of Global Public Health (in Biostatistics/Epidemiology). Her PhD is from the Harvard-MIT Division of Health Sciences and Technology. Her research group focuses on developing computational and statistical approaches for acquiring, integrating and using data to improve population-level public health. Considering health from a comprehensive, multi-level perspective means that she develops methods to work with data including social media, mobile phone, satellite imagery and other digital data sources as well as electronic health record, telemedicine and other clinical data. She is an MIT Technology Review’s 35 Innovators Under 35, NSF Career and Max Planck Sabbatical award winner and her work has been funded by diverse sources including the Gates Foundation, National Science Foundation, National Institutes of Health, Facebook, and the International Growth Centre.
Andrew Gelman, PhD
Andrew Gelman is a professor of statistics and political science and director of the Applied Statistics Center at Columbia University. He has received the Outstanding Statistical Application award from the American Statistical Association, the award for best article published in the American Political Science Review, and the Council of Presidents of Statistical Societies award for outstanding contributions by a person under the age of 40. His books include Bayesian Data Analysis (with John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Don Rubin), Teaching Statistics: A Bag of Tricks (with Deb Nolan), Data Analysis Using Regression and Multilevel/Hierarchical Models (with Jennifer Hill), Red State, Blue State, Rich State, Poor State: Why Americans Vote the Way They Do (with David Park, Boris Shor, and Jeronimo Cortina), and A Quantitative Tour of the Social Sciences (co-edited with Jeronimo Cortina). Andrew has done research on a wide range of topics, including: why it is rational to vote; why campaign polls are so variable when elections are so predictable; why redistricting is good for democracy; reversals of death sentences; police stops in New York City, the statistical challenges of estimating small effects; the probability that your vote will be decisive; seats and votes in Congress; social network structure; arsenic in Bangladesh; radon in your basement; toxicology; medical imaging; and methods in surveys, experimental design, statistical inference, computation, and graphics.
Bayesian Workflow as Demonstrated with a Coronavirus Example(Talk)
Eric Xing, PhD
Eric P. Xing is a Professor of Computer Science at Carnegie Mellon University, and the Founder, CEO, and Chief Scientist of Petuum Inc., a 2018 World Economic Forum Technology Pioneer company that builds standardized artificial intelligence development platform and operating system for broad and general industrial AI applications. He completed his undergraduate study at Tsinghua University, and holds a PhD in Molecular Biology and Biochemistry from the State University of New Jersey, and a PhD in Computer Science from the University of California, Berkeley. His main research interests are the development of machine learning and statistical methodology, and large-scale computational system and architectures, for solving problems involving automated learning, reasoning, and decision-making in high-dimensional, multimodal, and dynamic possible worlds in artificial, biological, and social systems. Prof. Xing currently serves or has served the following roles: associate editor of the Journal of the American Statistical Association (JASA), Annals of Applied Statistics (AOAS), IEEE Journal of Pattern Analysis and Machine Intelligence (PAMI) and the PLoS Journal of Computational Biology; action editor of the Machine Learning Journal (MLJ) and Journal of Machine Learning Research (JMLR); member of the United States Department of Defense Advanced Research Projects Agency (DARPA) Information Science and Technology (ISAT) advisory group. He is a recipient of the Carnegie Science Award, National Science Foundation (NSF) Career Award, the Alfred P. Sloan Research Fellowship in Computer Science, the United States Air Force Office of Scientific Research Young Investigator Award, the IBM Open Collaborative Research Faculty Award, as well as several best paper awards. Prof Xing is a board member of the International Machine Learning Society; he has served as the Program Chair (2014) and General Chair (2019) of the International Conference of Machine Learning (ICML); he is also the Associate Department Head of the Machine Learning Department, founding director of the Center for Machine Learning and Health at Carnegie Mellon University; and he is a Fellow of the Association of Advancement of Artificial Intelligence (AAAI), and an IEEE Fellow.
8 Focus Areas. 4 Days. One Data Science Conference
The largest applied data science conference is now 4 days, including mini-bootcamp days, 3 full training days, and 2 talks/workshops days. You get even more talks, trainings, and workshops spread over 8 focus areas. Accelerate your data science knowledge, training, and network all in one event. Start learning now with our pre-bootcamp live and on-demand training and hackathon.
Conference Focus Areas
October 27th – 30th, 2020
AI for Social Good
Applying AI to help solve social, climate, and humanitarian challenges
EVENT SUMMARY
Pre-conference Training & Hackathon
– A real immersive learning experience –
Start learning now with live and on-demand pre-bootcamp sessions.
4 Days of Data Science Training
– Taught by World-Class Data Scientists –
Learn the latest data science concepts, tools, and techniques from the best.
2 Days of Keynotes, Talks, and Workshops
– Presented by the best in the field of data science –
West 2020 Registration
Save 30% for a Limited Time
Limited Offer now on Sale
Save 30% on Door Price
Save 30% on Door Price
Tickets available at a group discount rate
Registration & Discount Resources
Conference Pass guide
GENERAL PASS – $129
Access to breakout sessions, keynotes, and virtual events. Pre-conference training and conference training and workshops are not included.
FULL DAY PASSES
One Day Pass – $299
Price for one full day. Choose one of 4 days and get access to all sessions and recordings from the selected day.
Two Days Pass – $529
Price for TWO full conference days. Choose 2 of 4 days and get access to all sessions and recordings from the selected days
All Access – $799
Price for THREE full conference days. Choose 3 of 4 days and get access to all sessions and recordings from the selected days.
VIRTUAL BOOTCAMP PASS – $859
Choose your learning path from 50 session over 4 days. Start learning now with pre-bootcamp on-demand training.
More information here
FREE
Access to the Virtual AI Expo Hall and Solution Showcase. Access to Demo talks
CERTIFICATION of COMPLETION
A Certification of Completion with any half or full day training course
ODSC CERTIFICATION $1,000 or Free
Free with any training course. Choose from Machine Learning, Deep Learning, and Data Science. Junior, senior, and principal levels available.
Pay by invoice/purchase order
You are able to buy your ticket via Invoice/Purchase Order (PO).
Please submit your request to receive a Purchase Order HERE.
Diversity and Inclusion Scholarship
The data science and the tech communities should strive to be more inclusive and offer opportunities for everyone to learn and grow their career in data science. To help achieve this goal ODSC offers diversity and inclusion scholarship passes to minorities, underrepresented groups, graduate students, and others who express interest in the content ODSC offers. Find out more HERE
Discounts
We offer academic, government, non-profit and start-up discounts.
To find out more please click HERE
Group Discounts
If you have a group of 13 or more, please email us at group@odsc.com to enquire about additional discounts. Please mention the size of your group and the types of passes required.
How to Convince your manager to attend ODSC West 2020?
Let us help you convince your manager that your attendance will benefit your organization.
Please check HERE for more information.
Volunteer
In exchange for a free pass, ODSC is seeking volunteers to help with our program and event planning. See more details HERE.
Donate to our Fundraise
For this year’s event, ODSC West is donating to Girls Who Code and Black Girls Who Code. We invite you to join us and donate to our fundraiser. Both these organizations are dedicated to increasing the number of women in engineering and helping them become innovators in the STEM fields. Please donate what you can via our registration. No purchase is necessary to donate and 100% of funds raised are donated.
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ODSC Virtual Conference – How It Works
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Virtual conference experience includes networking lounge area, speaker auditorium, expo halls, and prizes
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Access multiple livestream tracks on Tuesday, Wednesday, Thursday, or Friday
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Switch between sessions or tracks as your interests dictate
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Multiple focus areas including deep learning, machine learning, NLP, research frontiers, AI X for business, and more
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Sessions you missed can be viewed on demand at your leisure
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Engage virtually with fellow attendees, speakers, and Expo partners
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Participate in Q&A sessions with your speaker over live chat
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Directly download slides and other session materials
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(Training only) Access training and workshops prerequisites, notebooks, and other materials prior to training session starting
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(Training only) Access hands-on training and workshops with instructor-led code labs and notebooks.
Where Business Meets AI
Ai X Virtual Business Summit
Co-located at ODSC West, Ai X brings together the leading practitioners, innovation experts, and business professionals that drive artificial intelligence across a range of industries
Innovation
Discover how techniques from Machine Learning, Deep Learning, and Predictive Analytics are driving AI Innovation
Expertise
Gain AI expertise and learn from leading experts how to work with the frameworks and tools employed in AI
Management
Manage and deploy AI in the real world. Hear from AI management and heads of data science teams on a range of topics
Networking
Network with the AI experts, innovators, business professionals, and data scientists building the future of AI
Past ODSC Speakers
Research Frontiers - Past Participating AI Labs
ODSC is delighted to host presentations from the following research labs including:
Partnering With ODSC
I can’t possibly explain how much I enjoyed the ODSC East Virtual conference.
Many of the talks and workshops exceeded my expectations.
Thank you very much and well done!
Batool A. | Research Fellow | Liverpool, UK
ODSC East 2020 is the 3rd ODSC event that I have attended. The staff and speakers did a tremendous job approaching the challenge of moving the conference online. The speakers were engaging, the staff attentive, and my team had a wonderful experience this week.
Well done! I look forward to attending my next event.
Leslie Walcott | Data Scientist | Chicago
It’s just wonderful to attend this virtual event with so many people over here.
Wonderful Initiative by ODSC.
Brilliant Content.
Harshit P. | Software Engineering | Accenture
I just want to say a huge thanks to the organizers! I was skeptical about the remote format at first. However, it is an introvert’s dream!
It is so much easier to ask questions in chat/slack than it is to raise my hand in a crowded lecture hall
Andras Z.| Lead Data Scientist | Brown University.
ODSC is the best community data science events on the planet. There are other events that cover special topics, or industries, etc., but ODSC is comprehensive and totally community-focused: it’s the conference to engage, to build, to develop, and to learn from the whole data science community.
Kirk Borne | Principal Data Scientist and Executive Advisor at Booz Allen Hamilton
