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WHY an ODSC BOOTCAMP?
Start Early
With Prerequisites training, you can start learning the very fundamentals of data science. Learn how to code with Python and SQL, basic match skills and any other preques you need
3-day Immersive
Join the 3-day live Mini-Bootcamp onsite or virtually
Get Certified
Join Ai+ Training and complete your Machine Learning certification
Get Help
Career Support
Network with attendees from 1,600+ companies and meet over 40 companies hiring directly at ODSC, career days, webinars, resume reviews and more
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
Registration
10% off ends in:
Kick-start your career in data science, from beginner to advanced in just 3 days. Get the skills you need, taught by world-renowned experts
ODSC Virtual Mini-Bootcamp is the best way to gain in-demand data science skills in the shortest time with minimum investment. Starting in the summer, the Live and on-demand pre-Bootcamp Training will provide the right preparation and confidence to put your practice to work in your learning path.
It’s a truly immersive experience. Over almost a week, you will access an expanse of industry-focused data science topics on a scale not offered elsewhere. Uniquely, you will also get 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.
Machine Learning with Python
- Tutorial on Automated Machine Learning
- Automated Machine Learning with Python – from scikit-learn to auto-sklearn
- Machine Learning and Statistics: Don’t Mind the Gap
- Evolution of Efficient and Robust AutoML Systems
- Introduction to Data Science
- Hands-on Machine Learning Engineer with scikit-learn
More sessions coming soon
Machine Learning with R
- Introduction to Data Science
- Intro Data Analysis using Pandas
- Automatic and Explainable Machine Learning
- How to Build and Test a Trading Strategy Using ML
- Introduction to Data Science
More sessions coming soon
Deep Learning
- Teaching Machines to Talk: Modern Speech Synthesis with Deep Learning
- PyTorch 101: Building a Model Step-by-Step
- Adversarial Attacks and Defence in Computer Vision 101
- Introduction to Data Science
- Exploring the Кimworld of Sound Space Using Generative Adversarial Networks
- Meta-Learning: Learning to Learn
More sessions coming soon
Data Engineering / MLOps
- Hands-on Machine Learning Engineer with scikit-learn
- Model Governance: A Checklist for Getting AI Safely to Production
- Production Machine Learning Monitoring: Principles, Patterns and Techniques
- Introduction to Data Science
More sessions coming soon
NLP
- NLP Fundamentals
- NLP for Transformers
- Advanced NLP: From Essentials to Deep Transfer Learning
- Teaching Machines to Talk: Modern Speech Synthesis with Deep Learning
More sessions coming soon
Our kickstart Bootcamp is fully customizable, you can attend the sessions you want live and on-demand after our conference. Check all available training here – Training Page.
Europe 2022 Mini-Bootcamp Overview
Begin, this spring, with a pre-bootcamp on-demand and live training to build confidence on the core fundamentals of data science, and steadily progress through the bootcamp week with our hands-on training and expert-led sessions and projects. Refer to the timeline below.
Pre-Bootcamp On-demand Training (starts now)
Get started in your learning track with free access to introductory concepts with short, to-the-point and industry driven on-demand courses.
- Complete Python Fundamentals – by Mona Khalil (Duration: ~4 hrs)
- Modern Data Acquisition using Python – by Max Humber, General Assembly (Duration: ~2 hrs)
- Data Science 101: Machine Learning & Modeling in Theory & Practice – by Kirk Borne, PhD, Booz Allen Hamilton (Duration: ~4 hrs)
- SQL for Data Science – by Mona Khalil, Greenhouse Software (Duration: ~3 hrs)
- Recommendation Systems in Python – by Joshua Bernhard, NerdWallet (Duration: ~3 hrs)
Schedule: Self-paced
Pre-Bootcamp Live Training (FREE & PAID)
Build a foundation of essential hands-on concepts as part of your Bootcamp pass and get complimentary access to one Live training of your choice on the Ai+ Training.
Upcoming live training include:
Free Literacy Pre-Bootcamp Sessions
These 60-minute sessions will help you understand core concepts in Data, Ai, and Programming, to get you to reach for our Bootcamp Program. Register for FREE or as part of your Bootcamp Pass.
May 19th, 2021 – Bootcamp Warmup – Data Literacy
June 2nd, 2021 – Bootcamp Warmup – AI Literacy
Mini-Bootcamp Day 1 - Introductory sessions (June 15th)
Current sessions include:
- Introduction to Data Science
- NLP Fundamentals
- Bayesian Data Science: Probabilistic Programming
- Basic Python for Data Processing
- PyTorch 101: Building a Model Step-by-Step
- Hands-on RL in Finance: Playing Atari vs Playing Markets
- Machine Learning For Remote Sensing Based Landcover Change Detection
- Explainable Artificial Intelligence Explained
- Teaching Machines to Talk: Modern Speech Synthesis with Deep Learning
Mini-Bootcamp Day 2 - Introductory to Advanced Sessions (June 16th)
Current sessions include:
- Hands-on Machine Learning Engineer with scikit-learn
- Dataframes.jl: a Perfect Sidekick for Your Next Data Science Project
- Adversarial Attacks and Defence in Computer Vision 101
- Mastering Gradient Boosting with CatBoost
- Reproducible and Automated Report Generation
- Automated Machine Learning with Python – from scikit-learn to auto-sklearn
- Automatic and Explainable Machine Learning with H2O
- Federated Learning: Practice and Modern Algorithms
- MLOps Orchestration: Your Highway to Accelerating Deployment of AI
Mini-Bootcamp Day 3 - Introductory to Advanced Sessions (June 16th)
Current sessions include:
- Advanced NLP: From Essentials to Deep Transfer Learning
- Audio-Visual Speech Enhancement and Separation Based on Deep Learning
- How to Build and Test a Trading Strategy Using ML
- Introduction to Data Analysis Using Pandas
- AI Risk to Companies
- Responsible Data Science Using Bias-Dashboards
- Federated Learning from Scratch to Production with Scaleout
- Advances in Conversational AI and NLP through Large Scale Language Models such as GPT-3
Machine Learning Certification & More
- Obtain an ODSC Bootcamp Certification of Completion
- Join Ai+ Training
- Complete the additional courses and exam to become Machine Learning Certified
Choose your Pass
Tuesday | 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 (Wed&Thu)
Access to In-person Mini-Bootcamp Training Sessions
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:
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)
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)
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)
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)
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)
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)
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.
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.
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 Europe Instructors
Thomas Wiecki, PhD
Thomas Wiecki is the Chief Executive Officer at PyMC Labs (www.pymc-labs.io), the world’s first Bayesian consultancy. Prior to that Thomas was the VP of Data Science at Quantopian, where he used probabilistic programming and machine learning to help build the world’s first crowdsourced hedge fund. He is an author of the popular PyMC3 package — a probabilistic programming framework written in Python. He holds a PhD from Brown University.
Bayesian Modeling without the Math(Track Keynote)
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)
Julia Schulte-Cloos, PhD
Julia Schulte-Cloos is a Marie Skłodowska-Curie funded LMU Research Fellow at the Geschwister Scholl Institute of Political Science at LMU Munich. Her main research interests lie in comparative politics, political behavior, research methodology, and reproducibility. Julia Schulte-Cloos has earned her PhD from the European University Institute.
Daria Stepanova, PhD
Daria Stepanova is a research scientist at Bosch Center for Artificial Intelligence. Her research interests include Knowledge Representation and Reasoning with a special focus on the automatic acquisition of rules from structured knowledge. Previously Daria was a senior researcher at Max Plank Institute for Informatics (Germany), where she was heading a group on Semantic Data. Daria got her diploma degree in Applied Computer Science from the Department of Mathematics and Mechanics of St. Petersburg State University (Russia) in 2010 and a PhD in Computational Logic from Vienna University of Technology (Austria) in 2015. Before starting her PhD she worked as a visiting researcher at the School of Computing Science at Newcastle University (UK) in an industrially-oriented project.
Frank Hutter, PhD
Frank Hutter is a Full Professor for Machine Learning at the Computer Science Department of the University of Freiburg (Germany), as well as Chief Expert AutoML at the Bosch Center for Artificial Intelligence.
Frank holds a PhD from the University of British Columbia (UBC, 2009) and a Diplom (eq. MSc) from TU Darmstadt (2004). He received the 2010 CAIAC doctoral dissertation award for the best thesis in AI in Canada, and with his coauthors, several best paper awards and prizes in international competitions on machine learning, SAT solving, and AI planning. He is the recipient of a 2013 Emmy Noether Fellowship, a 2016 ERC Starting Grant, a 2018 Google Faculty Research Award, a 2020 ERC PoC Award, and he is a Fellow of ELLIS. Frank’s recent research focuses on automated machine learning (AutoML), where he co-organized the ICML workshop series on AutoML every year since its inception in 2014, co-authored the prominent AutoML tools Auto-WEKA, Auto-sklearn, and Auto-PyTorch, won the first two AutoML challenges with his team, co-authored the first book on AutoML, worked extensively on efficient hyperparameter optimization and neural architecture search, and gave a NeurIPS 2018 tutorial with over 3000 attendees.
Nisha Muktewar
Nisha Muktewar is a Research Engineer at Cloudera Fast Forward Labs, where she spends time researching latest ideas in machine learning, builds prototypes that showcase these capabilities when applied to real-world use cases, and advises clients in this space. Prior to joining Cloudera, she worked as a Manager in Deloitte’s Actuarial & Modeling practice leading teams in designing, building, and implementing predictive modeling solutions for pricing, consumer behavior, marketing mix, and customer segmentation use cases for insurance and retail/consumer businesses.
David Talby, PhD
David Talby is a chief technology officer at Pacific AI, helping fast-growing companies apply big data and data science techniques to solve real-world problems in healthcare, life science, and related fields. David has extensive experience in building and operating web-scale data science and business platforms, as well as building world-class, Agile, distributed teams. Previously, he was with Microsoft’s Bing Group, where he led business operations for Bing Shopping in the US and Europe and worked at Amazon both in Seattle and the UK, where he built and ran distributed teams that helped scale Amazon’s financial systems. David holds a Ph.D. in computer science and master’s degrees in both computer science and business administration.
Model Governance: A Checklist for Getting AI Safely to Production(Talk)
Olga Petrova, PhD
A former theoretical physicist turned machine learning engineer, Olga is now building a smart data annotation platform at Scaleway as a technical product manager. On the community side, she enjoys blogging about the latest advancements in AI both in and out of working hours. Some of her writing can be seen on medium.com/@olgapetrova_92798.
Introduction to Transformers for NLP: Where We Are and How We Got Here(Tutorial)
Daniel Voigt Godoy
Daniel is a data scientist, teacher, and author of “Deep Learning with PyTorch Step-by-Step: A Beginner’s Guide”.
He has been teaching machine learning and distributed computing technologies at Data Science Retreat, the longest-running Berlin-based bootcamp, since 2016, helping more than 150 students advance their careers. Daniel is also the main contributor of two Python packages: HandySpark and DeepReplay.
His professional background includes 20 years of experience working for companies in several industries: banking, government, fintech, retail, and mobility.
Alex Peattie
Alex Peattie is the co-founder and CTO of Peg, a technology platform helping multinational brands and agencies to find and work with top YouTubers. Peg is used by over 1500 organisations worldwide including Coca-Cola, L’Oreal and Google.
An experienced digital entrepreneur, Alex spent six years as a developer and consultant for the likes of Grubwithus, Huckberry, UNICEF and Nike, before joining coding bootcamp Makers Academy as senior coach, where he trained hundreds of junior developers. Alex was also a technical judge at this year’s TechCrunch Disrupt conference.
Teaching Machines to Talk: Modern Speech Synthesis with Deep Learning(Workshop)
Marta Markiewicz
Currently Senior (Big) Data Scientist at InPost and Lecturer at Wroclaw University of Economics and Business, previously Head of Data Science at Objectivity, with background in Mathematical Statistics. For almost 10 years, she has been discovering the potential of data in various business domains, from medical data, through retail, HR, finance, aviation, real estate, logistics, … She deeply believes in the power of data in every area of life. Articles’ writer, conference speaker and privately – passionate dancer and hand-made jewellery creator.
The Colours of Cleaning(Talk)
Stefan Jansen
Stefan is the founder and Lead Data Scientist at Applied AI. He advises Fortune 500 companies, investment firms and startups across industries on data & AI strategy, building data science teams, and developing machine learning solutions. Before his current venture, he was a partner and managing director at an international investment firm where he built the predictive analytics and investment research practice. He also was a senior executive at a global fintech company with operations in 15 markets.
Earlier, he advised Central Banks in emerging markets, worked for the World Bank, raised $35m from the Gates Foundation to cofound the Alliance for Financial Inclusion, and has worked in six languages across Asia, Africa, and Latin America. Stefan holds Master degrees in Computer Science from Georgia Tech and in Economics from Harvard and Free University Berlin and is a CFA Charterholder. He is the author for ‘Machine Learning for Algorithmic Trading’ and has been teaching data science at Datacamp and General Assembly.
Guglielmo Iozzia
Guglielmo is part of MSD (Merck & Co. in North America). He is currently busy unlocking business value through Computer Vision and other ML/DL/AI applications to the biotech manufacturing space. He has an extensive background in Software Engineering and Data Science across other big organizations including IBM, Optum and FAO of the UN in diverse contexts (such as Healthcare, DevOps, Cyber Security). Guglielmo has been recognized as DataOps Champion at the Streamsets DataOps Summit 2019 and awarded as one of the Top 50 Tech Visionaries at the 2019 Dubai Intercon Conference. Since 2018 he is also an international speaker (almost 30 international conferences so far, including Big Things 2019 and 2020, Spark+AI Summit 2019, Annual Cyber Security and AI Summit 2019 and 2020), author of a tech book on distributed Deep Learning with Apache Spark and planning about a second tech book which should be probably released at the end of 2021.
Adversarial Attacks and Defence in Computer Vision 101(Workshop)
Lucas Käldström
Lucas is a cloud native expert who’s serving the CNCF community in lead positions for 5 years. He’s awarded Top CNCF Ambassador 2017 with Sarah Novotny. Lucas was a co-lead for SIG Cluster Lifecycle, co-created kubeadm, Cluster API & Weave Ignite and ported Kubernetes to ARM. Lucas runs 3 meetups, and has spoken at 7 KubeCons. Most recently, Lucas co-created Racklet besides his university studies.
Exploring Modern and Secure Operations of Kubernetes Clusters on the Edge(Talk)
Sara Khalid
Sara is a Senior Research Associate in Biomedical Data Science and University Research Lecturer at the University of Oxford, where she is the Machine Learning Lead in the Centre for Statistics in Medicine. She has 12 years of experience in machine learning, signal processing, and intelligent remote monitoring research, with applications in biomedical and planetary health informatics. Sara has served on the NASA Frontier Development Lab Artificial Intelligence Panel and the NASA Climate Challenge Big Think. She is a National Geographic Society Explorer in Tracking Plastic Pollution with Remote Monitoring and Machine Learning. Sara is also a University of Oxford Ambassador for Women in Data Science.
Alex Honchar
Alex Honchar is a tech entrepreneur and educator. Currently, he is co-founder and ML director at Neurons Lab – a consulting firm specializing in healthcare, finance, and IoT. Also, he writes a popular blog on Medium about machine learning applications and leadership. Previously he worked as an independent consultant with SMBs and startups on rapid go-to-market ML solutions and taught machine learning courses at the University of Verona and Ukrainian Catholic University.
Lukáš Csóka
Lukas Csoka, working as Head Big Data Foundations in Swiss Re, one of the biggest global reinsurers, combines data science, consultative thinking and business partnering as the main skills used daily in his job to push design and develop new AI products. His MS in software engineering from Slovak University of Technology and MBA in global management from City University of Seattle are supporting him during this journey. His projects include innovative solutions for farmers using satellites, the application of predictive methods to manage costs inside Swiss Re or the analysis of thousands of client meetings from text and the provision of recommendations to Swiss Re’s traders and many others.
AI Risk to Companies(Workshop)
Learn More About Our Program
What You’ll Learn
Learn Fundamentals
Prepare for your immersive training by learning core fundamentals during our pre-bootcamp sessions in the summer, including:
Fundamentals of Python Programming
Data Analysis in Python
Mathematics for Data Science
Introduction to Statistics for Data Science
SQL for Data Science and 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
See schedule for many more…
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 with Tensorflow and Keras
Reinforcement Learning and Deep Reinforcement Learning
Machine Learning
Transfer Learning
Natural Language Processing
Text Analytics
Data Visualization and Storytelling
Data Modeling and Data Wrangaling
NLP BERT and Pre-trained models
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 virtual networking events, such as Meet the Speakers, Meet the Experts, Ignite session; and many more
Network with hundreds of data science attendees to learn what it’s like to be a data scientist
Registration
10% off ends in:
Choose your Pass
Access to ODSC Europe 2020 recordigs
Pre-Conference On Demand or Live Training
1 Year of AI+ Training Premium Subscription
Tuesday - Thursday | ODSC Workshops
Tuesday - Thursday | ODSC Training Sessions
Tuesday - Thursday | ODSC Talks - 30 Sessions
Wednesday & Thursday | Virtual AI Expo and Demo Talks
Tuesday | Virtual Career Expo
On-demand Access to All Conference recordings
