Turn ODSC into a mini-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

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

Get Certified 

 

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

Registration

 Limited Offer | 10% discount 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 truly an 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
    • 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.

West Mini Virtual Bootcamp 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 (MULTIPLE DATES)

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, SQL and more. 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:  October 22nd, 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 (October 27th)

Live sessions include but are not limited to ones below. 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 (october 28th - 29th)

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 (october 30th)

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 West 2020 recorded sessions
  • Get access to over 100+ additional recorded sessions from prior ODSC events, including ODSC East and ODSC Europe

What You’ll Learn

 Learn Fundamentals

Prepare for your immersive training by learning core fundamentals with our pre-bootcamp sessions this summer, 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

  • 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

  • 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

Bootcamp Experience

Registration

 Limited Offer | 10% discount ends in:


Choose your Pass

Pre Bootcamp On-Demand Training

Pre-Bootcamp Live Training

Learning Hackaton

Tuesday  | Fundamentals | 6 Sessions

Wednesday & Thursday | Data Science Training  - 20 Sessions

Wednesday & Thursday  | Workshops & Talks - 30 Sessions


Wednesday & Thursday | Virtual AI Expo and Demo Talks

Friday | Workshops & Talks - 15 Sessions

Friday  |  Data Science Training  - 10 Sessions

Friday | Virtual Career Expo 

3 Month of Free AI+ Basic Subscription 

All Access

$869

3 Days

Wed - Fri 

Purchase Separately

Purchase Separately

Purchase Separately

Purchase Separately

Register | Save 10% 

Virtual 4 Day Bootcamp 

$999


All 4 Days


Register | Save 10% 

Current Instructors

Data Scientist II AI and MLAmazon

Wale Akinfaderin, PhD

Adewale (Wale) Akinfaderin is a Data Scientist at Amazon Web Services. His expertise is in machine learning, deep learning, statistical experimentation and general information theory. He has broad experience implementing and extending ML techniques to solve practical and business problems. In his spare time, he conducts research on Machine Learning for the Developing World.

Mathematics for Data Science and Machine Learning(Half-Day Training)

Data Science InstructorGeneral Assembly

Bethany Poulin

Bethany Poulin is a data scientist and educator with expertise in statistical analysis, data visualization, and complex algorithmic problem-solving. She has worked as a professional data scientist and educator for the last 4 years and loves sharing what she has learned with her students. Her unusual background in Fine Arts and experience teaching high school give her a unique perspective on both problem-solving and the learning-teaching process. Prior to teaching this part-time course, she was a lead instructor in our Data Science Immersive program on the Boston campus. She teaches simply because she loves students and enjoys being a part of their success. She holds a BFA in Professional Photography from Rochester Institute of Technology, did post bachelor’s studies and the University of Montana in Environmental Biology, where she was recognized nationally as a Morris K Udall Scholar, is one semester away from an MS in Data Science from the City University of New York. She is has presented at PyOhio 2018 and 2019 and gave a presentation at ODSC East in 2019. In her spare time, Bethany is an avid fly fisherman, potter, and maker.

Introduction to Shiny Application Development(Half-Day Training)

Software EngineerStride Consulting

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)

Risk Management SpecialistFederal Reserve Bank of Chicago

Lara Kattan

Lara is a risk specialist with the Federal Reserve Bank of Chicago and occasional adjunct at the University of Chicago’s Booth School of Business, teaching Python and R. Previously she’s taught a data science bootcamp and built risk models for large financial institutions at McKinsey & Co. She enjoys coffee, yoga and bikes.

Probabilistic Programming and Bayesian Inference with Python(Half-Day Training)

Staff Associate - Machine LearningColumbia University in the City of New York

Thomas Fan

Thomas J. Fan is a Staff Associate at the Data Science Institute at Columbia University. He is one of the core developers of scikit-learn, an open-source machine learning library is written in Python. Thomas holds a Masters in Mathematics from NYU and Masters in Physics from Stony Brook University. He also maintains skorch, a scikit-learn compatible neural network library that wraps PyTorch. He believes that developing open-source software is one of the best ways to maximize one’s impact.

Introduction to Scikit-learn: Machine Learning in Python(Half-Day Training)

Intermediate Machine Learning with Scikit-learn: Cross-validation, Parameter Tuning, Pandas Interoperability, and Missing Values(Half-Day Training)

Intermediate Machine Learning with Scikit-learn: Evaluation, Calibration, and Inspection(Half-Day Training)

Advanced Machine Learning with Scikit-learn: Text Data, Imbalanced Data, and Poisson Regression(Half-Day Training)

VP Data & Analytics, Cybersecurity Technology and ControlsJPMorgan Chase & Co

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)

Lead Strategy Planner for AI EcosystemIntel

Yashesh A. Shroff, PhD

Yashesh Shroff is a Lead Strategy Planner at Intel where he focuses on enabling the AI ecosystem on heterogeneous compute. Recently, as a product manager, he was responsible for the AI and media/game graphics software ecosystem showcasing Intel’s latest-gen graphics architecture (10nm). He has over 15 years of technical and enabling experience, spanning optical modeling, statistical analysis, and capital equipment supply chain at Intel. He has over 20 published papers and 4 patents. He has a Ph.D. in EECS from UC Berkeley and a joint MBA from UC Berkeley Haas & Columbia Graduate School of Business.

Natural Language Processing with PyTorch(Half-Day Training)

Lead Data ScientistSilicon Valley Startup in Stealth Mode

Ravi Ilango

Ravi Ilango is a Lead Data Scientist at a silicon valley startup in stealth mode. He is passionate in developing deployable deep learning solutions. Previously he was at StatesTitle and at Foghorn Systems as a Sr. Data Scientist and has over 10 years of experience at Apple as a data Scientist & at Applied Materials in Supply Chain Program Management. Ravi has a Graduate Certificate in Data Mining & Machine Learning from Stanford and completed a Masters Program in Aeronautics and Production Engineering from IIT Madras. He has a BS in Mechanical Engineering, Madras University.

Natural Language Processing with PyTorch(Half-Day Training)


Some of Our Previous Instructors

Dimitri Theoharatos
Dimitri Theoharatos

Instructor, Data Scientist | Scribd, Metis

Amy E. Hodler
Amy E. Hodler

Graph Analytics & AI Program Manager, Author | Neo4j

Nico Van de Bovenkamp
Nico Van de Bovenkamp

ETP Data Scientist, Instructor | Nielsen, General Assembly

Jake Dailey
Jake Dailey

Lead Data Scientist | Nielsen

Dr. Kirk Borne
Dr. Kirk Borne

Principal Data Scientist | Booz Allen Hamilton

Jared Lander
Jared Lander

Chief Data Scientist, Author of R for Everyone, Professor | Lander Analytics, Columbia Business School

Dr. Jon Krohn
Dr. Jon Krohn

Chief Data Scientist, Author of Deep Learning Illustrated | Untapt

Jennifer Reif
Jennifer Reif

Developer Relations Engineer | Neo4j

Eric Ma, PhD
Eric Ma, PhD

Author of nxviz Package

Innovation Women

Andreas Mueller, PhD
Andreas Mueller, PhD

Author, Research Scientist, Core Contributor of scikit-learn | Columbia Data Science Institute

Saishruthi Swaminathan
Saishruthi Swaminathan

Developer Advocate and Data Scientist | IBM CODAIT

Pieter Abbeel, PhD
Pieter Abbeel, PhD

Professor & Director of the Robot Learning Lab, Co-Founder, Advisor | UC Berkeley, OpenAI

Christian Hidber, PhD
Christian Hidber, PhD

Consultant | bSquare

Stacey Svetlichnaya
Stacey Svetlichnaya

Deep Learning Engineer | Weights & Biases

Click here for more training & career experts

Learn More About Our Program



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 

Companies Represented at ODSC Bootcamp

We’re Proud to Have the Best and Brightest in Attendance

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