Turn ODSC into a mini virtual bootcamp with our 3 day or 4 day passes.

Avail free access to live and on-demand pre-bootcamp training and hackathon to win prizes.

Join Pre-Bootcamp Training & Hackathon 

Start learning in the summer with the very fundamentals of data science, followed by a live training and hackathon to build confidence, and win prizes.

Learn Applied Skills at the Mini- Bootcamp

Join the 3 or 4-day live mini-bootcamp and learn the languages, algorithms, and tools from some of the leading data scientists in the world

Launch Your Career

 

Network with attendees from 1,600+ companies and meet over 40 companies hiring directly at ODSC

Get Certified 

 

Get a certification of completion upon finishing 3 or 4 days of instruction

Registration

40% off ends in:

 Kick-start your career in data science, from beginner to advanced in just 4 days. Get the skills you need, taught by world-renowned experts

ODSC Mini Virtual 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 & the Learning Hackathon 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.

Available Specialization Tracks

Attend one of our six specialization tracks or customize it to your own goals

  • Machine Learning with Python

    • Introduction to Statistics for Data Science
    • Mathematics for Data Science
    • Programming with Data: Python and Pandas
    • Hands-on Machine Learning with scikit-learn
    • Bayesian Data Science: Probabilistic Programming
    • Introduction To Face Processing With Computer Vision
    • Solving the Data Scientist’s Cold-Start Problem with Machine Learning Examples
    • Missing Data in Supervised Machine Learning
    • Causal Inference for Data Science
    • Scalable Active Learning and Semi-supervised Learning
    • ML Inference on Edge with ONNX Runtime
    • Atypical Applications of Typical Machine Learning Algorithms
    • Interpreting and Explaining XGBoost Models

  • Machine Learning with R

    • Introduction to Statistics for Data Science
    • Mathematics for Data Science
    • R for Python Programmers
    • Machine Learning in R Part I: Penalized Regression and Boosted Trees
    • Machine Learning in R Part II: Using workflows to build an ML optimization pipeline
    • Machine Learning in R Part III: Forecasting Time Series Data
    • Missing Data in Supervised Machine Learning
    • Machine Learning in R Part IV: Putting R-Based Machine Learning into Production with Plumber and Docker
    • Causal Inference for Data Science
    • Missing Data in Supervised Machine Learning
    • Causal Inference for Data Science
    • Atypical Applications of Typical Machine Learning Algorithms

  • Deep Learning

    • Introduction to Statistics for Data Science
    • Mathematics for Data Science
    • Programming with Data: Python and Pandas
    • Hands-on Machine Learning with scikit-learn
    • Introduction To Face Processing With Computer Vision
    • Deep Learning Building Blocks
    • Variational Auto-Encoders for Customer Insight
    • Transformers for Computer Vision
    • State of the art AI methods with TensorFlow: Transfer Learning, RL and GANs

     

     

    More sessions coming soon

  • Data Engineering / MLOps

    • Introduction to Statistics for Data Science
    • Mathematics for Data Science
    • SQL for Data Science
    • Data Science Best Practices: Continuous Delivery for Machine Learning
    • Deploying ML pipeines with open-data
    • Kubeflow Security and Pipeline deployment in MLOps
    • ML Inference on Edge with ONNX Runtime

     

     

     

    More sessions coming soon

  • NLP

    • Introduction to Statistics for Data Science
    • Mathematics for Data Science
    • Programming with Data: Python and Pandas
    • Advanced NLP: From Essentials to Deep Transfer Learning
    • Spark NLP for Healthcare: Lessons Learned Building Real-World Healthcare AI Systems
    • Building an Industry Classifier With The Latest Scraping, NLP and Deployment Tools
    • State-of-the-art NLP Made Easy with AdaptNLP
    • State of the art AI methods with TensorFlow: Transfer Learning, RL and GANs

       

     

    More sessions coming soon

  • Data Visualization

    • Introduction to Statistics for Data Science
    • Mathematics for Data Science
    • Bringing Data to the Masses Through Visualization
    • Animating Data: From matplotlib plots to GIFs
    • From Numbers to Narrative: Turning Raw Data into Compelling Visual Stories with Impact

     

     

     

     

    More sessions coming soon

Our mini 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.

Mini Virtual Bootcamp Program Overview

Begin, this summer, with a pre-bootcamp on-demand and live training followed by a hackathon 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

Get started in your learning track with free access to introductory concepts with short, to-the-point and industry driven on-demand courses.

Schedule: Self-paced

Note: only accessible to 3-Day and 4-Day Bootcamp registrants.

Pre-Bootcamp Live Training

As part of your Bootcamp access, use your skills at hand and avail access to live-training on a session of your choice: Deep Learning, NLP and SQL. These sessions brings hands-on code run-throughs on Jupyter Notebooks and guided projects, and build a solid foundation for advanced training sessions ahead. Click below for live-training outline and requirements.

Live-Training Schedule:  July 29th, 12 PM (ET)

Live-Training Schedule:  August 18th, 12 PM (ET)

Live-Training Schedule:  September 30th, 12 PM (ET)

Note: only accessible to 4-Day Bootcamp registrants.

Learning Hackathon

A unique challenge to apply your ideas and data skills gained from Pre-Bootcamp on-demand and live training. This challenge will equip you to upskill on advanced level topics during the 3 or 4-Day Bootcamp.

  • Create and share your compelling notebook by yourself or as a team with an analysis on one of the various datasets provided on causes of pressing importance.
  • Submit your project with a screencast of a presentation explaining the impact and methodology of your analysis.
  • Submissions must be original work of you or your team.
  • Hackathon Schedule: early-mid August (To be announced)
  • Judging: mid-late August
  • Winners announcement: Early September
  • Winners recognition: ODSC Europe Conference Awards (Bootcamp Week)

Prizes & Winners

First two winning teams will receive an invitation to donate an amount (to be announced soon) to a non-profit or charity of your choice, plus full complimentary access to the entire bootcamp and on-demand content library of over 150 industry and academia focused session. 

Subsequent third and fourth winners will receive one full-day complimentary training pass to attend sessions of their choice.

Register Here

(Note: Hackathon is open to all)

Mini-Bootcamp Day 1 - Beginner Level SEssions

Check full half-day and full-day training program at the Training Page.

  • Mathematics for Data Science
  • Statistics for Data Science
  • Introduction to Python Programming with Pandas
  • Introduction to Natural Language Processing
  • SQL for Data Science
  • Introduction to Bayesian Modelling
  • Recommendation Systems Fundamentals
  • Introduction To Face Processing With Computer Vision

Mini-Bootcamp Day 2 & 3 - Beginner to Intermediate Level Sessions

Live sessions include but are not limited to ones below. Check full half-day and full-day training program at the Training Page.

  • Introductory to Intermediate Machine Learning with scikit-learn
  • Advanced Machine Learning with scikit-learn: Pipelines, Evaluation Metrics, Imbalanced Classification
  • Machine Learning in R: Penalized Regression, Boosted Trees and build ML pipelines
  • Machine Learning in R: Forecasting Time Series Data and ML in production with Plumber, Docker
  • Machine Learning for Trading
  • Interpreting and Explaining XGBoost Models
  • Bayesian Data Science: Probabilistic Programming
  • Deep Learning with TensorFlow and PyTorch

Mini-Bootcamp Day 4 - Intermediate to Advanced Level Sessions

Live sessions include but are not limited to ones below. Check full half-day and full-day training program at the Training Page.

  • Variational Auto-Encoders for Customer Insight
  • Learning and Mining Large-Scale Spatiotemporal Data
  • From Numbers to Narrative: Turning Raw Data into Compelling Stories with Impact
  • How To Build And Test A Trading Strategy Using ML
  • State-of-the-art NLP Made Easy with AdaptNLP
  • Building an Industry Classifier With The Latest Scraping, NLP and Deployment Tools
  • Sequence Modelling with Deep Learning
  • Image Detection as a Service: Use APIs and Deep Learning to Support Products

Certificate of Completion & More

  • Obtain an ODSC Certification of Completion
  • Get access to all ODSC Europe 2020 recorded sessions
  • Get access to over 100+ additional recorded sessions from prior ODSC events, including ODSC East and ODSC West

Some of the current Bootcamp Instructors

Check full line-up of speakers and instructors here, and workshop and training sessions here.

Research ScientistBosch Center for AI

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.

FounderFire Plus Algebra

Alan Rutter

Alan Rutter is the founder of consultancy Fire Plus Algebra and is a specialist in communicating complex subjects through data visualization, writing and design. He has worked as a journalist, product owner and trainer for brands and organizations including Guardian Masterclasses, WIRED, Time Out, the Home Office, the Biotechnology and Biological Sciences Research Council and Liverpool School of Tropical Medicine.

Data ScientistGreenhouse Software

Mona Khalil

Mona is a Data Scientist at Greenhouse Software in New York City, where they contribute to data-informed decision making across the company and machine learning solutions to improve the hiring process for Greenhouse customers. They’ve previously worked in government, creating analytics and machine learning solutions to improve the lives of New Yorkers, and continue to be involved in civic projects through a number of volunteer and non-profit organizations. They’ve also been a statistics and data science educator with DataCamp, Emeritus, and in university settings. They hold a graduate degree in Developmental Psychology and are passionate about contributing to the ethical use of data science methodology in the public and private sector.

SQL for Data Science(Training)

Lead Instructor, Data and AnalyticsGeneral Assembly

Nathaniel Tucker

Nathaniel earned his AB/SM in Computer Science from Harvard. He previously worked as a Quant and Trader at Jane Street and Goldman Sachs before transitioning into the pure tech industry. Nathaniel worked as a Data Scientist at Facebook, a Product Manager at Microsoft and a Software Engineer at Google before joining Vicarious. He is an avid reader and learner. He teaches part time at General Assembly and is developing open source teaching material for data science, machine learning, and web development.

Deep Learning Building Blocks(Training)

Head of AI & ResearchAira

Anirudh Koul

Anirudh Koul is a noted AI expert, ML Lead for NASA FDL, UN/TEDx speaker, author of O’Reilly’s Practical Deep Learning book and a former scientist at Microsoft AI & Research, where he founded Seeing AI, considered the most used technology among the blind community after the iPhone. With features shipped to a billion users, he brings over a decade of production-oriented applied research experience on petabyte-scale datasets. He also coaches a team for Roborace, the Formula One championship of autonomous driving @200mph. His work in the AI for Good field, which IEEE has called ‘life-changing’, has received awards from CES, FCC, MIT, Cannes Lions, American Council of the Blind, showcased at events by UN, World Economic Forum, White House, House of Lords, Netflix, National Geographic, and lauded by world leaders including Justin Trudeau and Theresa May.

FounderScalar Research

Gabriel Bianconi

Gabriel is the founder of Scalar Research, a full-service artificial intelligence & data science consulting firm. Scalar helps companies tackle complex business challenges with data-driven solutions leveraging cutting-edge machine learning and advanced analytics. Previously, Gabriel was a B.S. & M.S. student in computer science at Stanford, where he conducted research on computer vision, deep learning, and quantum computing. He’s also spent time at Google, Facebook, startups, and investment firms.

Introduction To Face Processing With Computer Vision(Tutorial)

Assistant Professor, Computer ScienceNortheastern University College

Rose Yu, PhD

Dr. Rose Yu is an Assistant Professor at Northeastern University Khoury College of Computer Sciences. Previously, she was a postdoctoral researcher in the Department of Computing and Mathematical Sciences at Caltech. She earned her Ph.D. in Computer Science at the University of Southern California and was a visiting researcher at Stanford University. Her research focuses on machine learning for large-scale spatiotemporal data and its applications, especially in the emerging field of computational sustainability. She has over a dozen publications in leading machine learning and data mining conference and several patents. She is the recipient of the USC Best Dissertation Award, “MIT Rising Stars in EECS”, and the Annenberg fellowship.

Learning and Mining Large-Scale Spatiotemporal Data(Tutorial)

Kirk borne
Principal Data ScientistBooz Allen Hamilton

Dr. Kirk Borne

Dr. Kirk Borne is the Principal Data Scientist and an Executive Advisor at global technology and consulting firm Booz Allen Hamilton. In those roles, he focuses on applications of data science, data management, machine learning, A.I., and modeling across a wide variety of disciplines. He also provides training and mentoring to executives and data scientists within numerous external organizations, industries, agencies, and partners in the use of large data repositories and machine learning for discovery, decision support, and innovation. Previously, he was Professor of Astrophysics and Computational Science at George Mason University for 12 years where he did research, taught, and advised students in data science. Prior to that, Kirk spent nearly 20 years supporting data systems activities on NASA space science programs, which included a period as NASA’s Data Archive Project Scientist for the Hubble Space Telescope. Dr. Borne has a B.S. degree in Physics from LSU, and a Ph.D. in Astronomy from Caltech. In 2016 he was elected Fellow of the International Astrostatistics Association for his lifelong contributions to big data research in astronomy. As a global speaker, he has given hundreds of invited talks worldwide, including conference keynote presentations at many dozens of data science, A.I. and big data analytics events globally. He is an active contributor on social media, where he has been named consistently among the top worldwide influencers in big data and data science since 2013. He was recently identified as the #1 digital influencer worldwide for 2018-2019. You can follow him on Twitter at @KirkDBorne.

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

Atypical Applications of Typical Machine Learning Algorithms(Training)

Machine Learning Research EngineerWelocalize

Dr. Natasha Latysheva

Dr. Natasha Latysheva is a machine learning engineer in the NLP group at Welocalize, a leading language services provider. Her work focuses on machine translation and natural language processing. She previously worked as a data scientist in a video game studio, and before that on a PhD in computational biology at Cambridge.

Sr. Data ScientistJohn Snow Labs

Veysel Kocaman, PhD

Veysel Kocaman is a Senior Data Scientist and ML Engineer at John Snow Labs and has a decade long industry experience. He is also pursuing his PhD in CS as well as giving lectures at Leiden University (NL) and holds an MS degree in Operations Research from Penn State University. He is affiliated with Google as a Developer Expert in Machine Learning.

Spark NLP for Healthcare: Lessons Learned Building Real-World Healthcare AI Systems(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

40% off ends in:


Choose your Pass

Pre-Bootcamp On-Demand Training

Pre-Bootcamp Live Training

Learning Hackathon

Wednesday  | Fundamentals | 6 Sessions

Thursday & Friday| Data Science Training  - 20 Sessions

Thursday & Friday  | Workshops & Talks - 30 Sessions


Thursday & Friday | Virtual AI Expo and Demo Talks

Saturday | Workshops & Talks - 15 Sessions

Saturday  | 

Data Science Training  - 10 Sessions

Saturday | Virtual Career Expo 

Virtual 3 Day Bootcamp

€829

3 Days

Wed - Friday

Purchase Separately

Purchase Separately

Purchase Separately

Register | Save 40% 

Virtual 4 Day Bootcamp 

€999


All 4 Days


Register | Save 40% 

Who Should Attend

Beginners

  • Beginners pursuing data science careers

  • Those trying to decide on the right path

  • Business Analysts and Data Analysts seeking new skills

Intermediates

  • Data Scientists

  • Data Engineers

  • ML Engineers

  • Software Engineers

Citizens

  • Citizen Data Scientists

  • Data Wranglers

  • Data Science 4 Good

  • Data Journalists 

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