Training Sessions

– Taught by World-Class Data Scientists –

Learn the latest data science concepts, tools and techniques from the best. Forge a connection with these rockstars from industry and academic, who are passionate about molding the next generation of data scientists.

Highly Experienced Instructors

Our instructors are highly regarded in data science, coming from both academia and notable companies.

Real World Applications

Gain the skills and knowledge to use data science in your career and business, without breaking the bank.

Cutting Edge Subject Matter

Find training sessions offered on a wide variety of data science topics from machine learning to data visualization.

ODSC Training Includes

Form a working relationship with some of the world’s top data scientists for follow up questions and advice.

Additionally, your ticket includes access to 50+ talks and workshops.

High quality recordings of each session, exclusively available to premium training attendees.

Equivalent training at other conferences costs much more.

Professionally prepared learning materials, custom tailored to each course.

Opportunities to connect with other ambitious like-minded data scientists.

10+ reasons people are attending ODSC East 2018

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A Few of Our 2018 Training Sessions

More training sessions to be added soon!

Title

General Training Session: Machine Learning R Part I with Jared Lander, Statistics Professor at Columbia University and Author of R for Everyone 

Bio

Jared Lander is the Chief Data Scientist of Lander Analytics a data science consultancy based in New York City, the Organizer of the New York Open Statistical Programming Meetup and the New York R Conference and an Adjunct Professor of Statistics at Columbia University. With a masters from Columbia University in statistics and a bachelors from Muhlenberg College in mathematics, he has experience in both academic research and industry. His work for both large and small organizations ranges from music and fund raising to finance and humanitarian relief efforts.

He specializes in data management, multilevel models, machine learning, generalized linear models, data management and statistical computing. He is the author of R for Everyone: Advanced Analytics and Graphics, a book about R Programming geared toward Data Scientists and Non-Statisticians alike and is creating a course on glmnet with DataCamp.

Title

General Training Session: Machine Learning in Part II with Jared Lander, Statistics Professor at Columbia University and Author of R for Everyone

Bio

Jared Lander is the Chief Data Scientist of Lander Analytics a data science consultancy based in New York City, the Organizer of the New York Open Statistical Programming Meetup and the New York R Conference and an Adjunct Professor of Statistics at Columbia University. With a masters from Columbia University in statistics and a bachelors from Muhlenberg College in mathematics, he has experience in both academic research and industry. His work for both large and small organizations ranges from music and fund raising to finance and humanitarian relief efforts.

He specializes in data management, multilevel models, machine learning, generalized linear models, data management and statistical computing. He is the author of R for Everyone: Advanced Analytics and Graphics, a book about R Programming geared toward Data Scientists and Non-Statisticians alike and is creating a course on glmnet with DataCamp.

Title

General Training Session: A Tour of Machine Learning Algorithms: The Usual Suspects in Some Unusual Applications with Dr. Kirk Borne, Principal Data Scientist and Executive Advisor at Booz Allen Hamilton

Bio

Dr. Kirk Borne is the Principal Data Scientist and an Executive Advisor at global technology and consulting firm Booz Allen Hamilton (since 2015). He previously spent 12 years as Professor of Astrophysics and Computational Science at George Mason University where he taught and advised students in the graduate and undergraduate Data Science degree programs. Before that, he worked 18 years on NASA projects in various roles, developing and managing large data systems for space science research, including program manager in NASA’s Space Science Data Operations Office and Data Archive Project Scientist for the Hubble Space Telescope. He has a PhD in Astronomy from Caltech and a BS in Physics from LSU. In 2014 he was named an IBM Big Data and Analytics Hero, and in 2016 he was elected Fellow of the International Astrostatistics Association. 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.

Title

General Training Session: Python Tutorial for Computational Finance with Fatena El-Masri, PhD, Senior Financial Analyst at FDIC 

Bio

Fatena El-Masri, PhD is a Senior Financial Analyst at FDIC, with many years of experience in financial engineering, applied statistics, and quantitative risk modeling and management (including market, credit and operational risk). For her Computational Science and Informatics PhD dissertation research at GMU, she used Python and agent-based models to study the stability of the banking network and to identify instability conditions for failing banks.

While Fatena was working for an assignment with the Royal Australian Navy, she managed a Proof of Concept (POC) Artificial Intelligence (AI) project for the Australian Maritime Warfare Center, using IBM Watson, Watson Explorer, and Blue Prism for Natural Language Processing, Robotic Process Automation (RPA), & Video Analytics. She coded Python & TensorFlow for video analytics, and provided oversight management of the RPA contract to automate processes.

Title

General Training Session: Turning a Data Science Brain Dump into Software with Katie Malone, PhD, Director of Data Science Research and Development at Civis Analytics

Bio

Katie Malone is the Director of Data Science Research and Development at Civis Analytics, a data science software and services company. At Civis she leads the Data Science Research and Development department, which tackles some of Civis’ most novel and challenging data science consulting engagements as well as writing the core data science code that powers the Civis Data Science Platform. A physicist by training, Katie spent her PhD searching for the Higgs boson at CERN and is also the instructor for Udacity’s Introduction to Machine Learning course. As a side project she hosts a weekly podcast about data science and machine learning, Linear Digressions.

Title

General Training Session: Introduction to Pyton for Data Science with Skipper Seabold, Director of Data Science at Civis Analytics

Bio

Skipper is a Director of Data Science at Civis Analytics, a data science technology and advisory firm. He leads a team of data scientists from all walks of life from physicists and engineers to statisticians and computer scientists. He is an economist by training, and has a decade of experience working in the Python data community. He started and led the statsmodels Python project, was formerly on the core pandas team, and has contributed to many projects in Python data stack.

Title

General Training Session: Machine Learning with scikit-learn for Beginners with Andreas Mueller, PhD, Core Contributor of scikit learn and Author of Introduction to Machine Learning with Python

Bio

Andreas is a lecturer at the Data Science Institute at Columbia University and author of the O’Reilly book “Introduction to machine learning with Python”, describing a practical approach to machine learning with python and scikit-learn. He is one of the core developers of the scikit-learn machine learning library, and has been co-maintaining it for several years. He is also a Software Carpentry instructor. In the past, Andreas worked at the NYU Center for Data Science on open source and open science, and as Machine Learning Scientist at Amazon. 

His mission is to create open tools to lower the barrier of entry for machine learning applications, promote reproducible science and democratize the access to high-quality machine learning algorithms.

Title

General Training Session: Intermediate Machine Learning with scikit-learn with Andreas Mueller, PhD, Core Contributor of scikit learn and Author of Introduction to Machine Learning with Python

Bio

Andreas is a lecturer at the Data Science Institute at Columbia University and author of the O’Reilly book “Introduction to machine learning with Python”, describing a practical approach to machine learning with python and scikit-learn. He is one of the core developers of the scikit-learn machine learning library, and has been co-maintaining it for several years. He is also a Software Carpentry instructor. In the past, Andreas worked at the NYU Center for Data Science on open source and open science, and as Machine Learning Scientist at Amazon. 

His mission is to create open tools to lower the barrier of entry for machine learning applications, promote reproducible science and democratize the access to high-quality machine learning algorithms.

Title

General Training Session: Advanced Machine Learning with scikit-learn Part I with Andreas Mueller, PhD, Core Contributor of scikit learn and Author of Introduction to Machine Learning with Python

Bio

Andreas is a lecturer at the Data Science Institute at Columbia University and author of the O’Reilly book “Introduction to machine learning with Python”, describing a practical approach to machine learning with python and scikit-learn. He is one of the core developers of the scikit-learn machine learning library, and has been co-maintaining it for several years. He is also a Software Carpentry instructor. In the past, Andreas worked at the NYU Center for Data Science on open source and open science, and as Machine Learning Scientist at Amazon. 

His mission is to create open tools to lower the barrier of entry for machine learning applications, promote reproducible science and democratize the access to high-quality machine learning algorithms.

Title

General Training Session: Advanced Machine Learning with scikit-learn Part II with Andreas Mueller, PhD, Core Contributor of scikit learn and Author of Introduction to Machine Learning with Python

Bio

Andreas is a lecturer at the Data Science Institute at Columbia University and author of the O’Reilly book “Introduction to machine learning with Python”, describing a practical approach to machine learning with python and scikit-learn. He is one of the core developers of the scikit-learn machine learning library, and has been co-maintaining it for several years. He is also a Software Carpentry instructor. In the past, Andreas worked at the NYU Center for Data Science on open source and open science, and as Machine Learning Scientist at Amazon. 

His mission is to create open tools to lower the barrier of entry for machine learning applications, promote reproducible science and democratize the access to high-quality machine learning algorithms.

Title

General Training Session: Introduction to TensorFlow Lattice for Interpretable Machine Learning with Maya Gupta, PhD, Glassbox ML R&D Team Lead at Google

Bio

Maya Gupta leads Google’s Glassbox Machine Learning R and D team, which focuses on designing and developing controllable and interpretative machine learning algorithms that solve Google product needs. Prior to Google, Gupta was an Associate Professor of Electrical Engineering at the University of Washington from 2003-2013. Her PhD is from Stanford, and she holds a BS EE and BA Econ from Rice. Gupta founded and runs the wooden jigsaw puzzle company, Artifact Puzzles.

Title

General Training Session: Synthesizing Data and User Visualization Experience with Mark Schindler, Co-founder and Managing Director of GroupVisual.io

Bio

Mark Schindler is co-founder and Managing Director of GroupVisual.io. For over 15 years, he has designed user-interfaces for analytic software products and mobile apps for clients ranging from Fortune 50 companies to early-stage startups. In addition to design services, Mark and his team mentor startup companies and conduct workshops on data visualization, analytics and user-experience design

Title

General Training Session: A Gentle Introduction to Predictive Analytics with R with Dr. Colin Gillespie, Author of Efficient R Programming, R Trainer and Consultant, and Senior Lecturer at Newcastle University

Bio

Dr. Colin Gillespie is Senior lecturer (Associate Professor) at Newcastle University, UK. His research interests are high performance statistical computing and Bayesian statistics. He is regularly employed as a consultant by Jumping Rivers and has been teaching R since 2005 at a variety of levels, ranging from beginners to advanced programming.

Title

General Training Session: Optimizing Hadoop Environments with William Dailey, Senior Hadoop Engineer and Educator at Hortonworks

Bio

Title

General Training Session: Pre-trained Models, Transfer Learning and Advanced Keras Features with Francesco Mosconi, PhD, Data Scientist, Consultant, and Trainer at CATALIT

Bio

Francesco Mosconi has a PhD in Physics and is a Data Scientist at Catalit LLC. Instructor at Udemy. Formerly co-founder and Chief Data Officer at Spire, a YC-backed company that invented the first consumer wearable device capable of continuously tracking respiration and physical activity. Machine Learning and python expert. Also served as Data Science lead instructor at General Assembly and The Data incubator.

Title

General Training Session: Introduction to Text Mining with R with Ted Kwartler, Data Scientist at Liberty Mutual

BIO

Ted Kwartler is a Data Scientist at Liberty Mutual. He advocates for and integrates customer innovation into everyday culture and work. He helps to define and organize all customer service functions and key performance indicators. Thus, he incorporates data-driven customer analytics decisions balanced with qualitative feedback to continuously innovate for the customer experience. Specialties: Statistical forecasting and data mining, IT service management, customer service process improvement and project management, business analytics.

Here’s Some Sample Training Sessions from our Previous Event.


  • Getting Started with TensorFlow with Senior Staff Developer Advocate for TensorFlow at Google, Magnus Hyttsten 
  • Modeling Big Data with R, sparklyr, and Apache Spark with Co-founder of Win Vector LLC and Author, Dr. John Mount 
  • Machine Learning in R Part 1 with Statistics Professor at Columbia University and Author of R for Everyone, Jared Lander
  • Machine Learning in R Part II with Statistics Professor at Columbia University and Author of R for Everyone, Jared Lander
  • scikit-image: Image Processing in Python with Founder of scikit-image and contributor to numpy, scipy, Stéfan van der Walt, PhD  
  • Cython and Multi-Threading to Accelerate Code with Core Developer of the Popular scikit-learn Machine Learning Package for Python and Pomegranate Writer, Jacob Schreiber 
  • Data Science 101 with Director of Data Science at Civis Analytics, Katie Malone, PhD
  • Introduction to Python for Data Science with Director of Data Science at Civis Analytics, Skipper Seabold 
  • Deep Learning with Apache MXNet with Data Scientist at Amazon AI and Contributing Editor at KDnuggets, Zachary Chase Lipton, PhD
  • Deep Learning with Apache MXNet with Data Scientist at Amazon AI and Contributing Editor at KDnuggets, Zachary Chase Lipton, PhD
  • Machine Learning with scikit-learn Part I with Core Contributor of scikit learn and Author of Introduction to Machine Learning with Python,  Andreas Mueller
  • Introduction to TensorFlow Lattice for Interpretable Machine Learning with Glassbox ML R&D Team Lead at Google, Maya Gupta
  • Introduction to Text Analytics with AVP of Innovation at Liberty Mutual, Ted Kwartler
  • Hands-on with the Google Assistant and Developing Your Own Assistant Apps with Dialogflow with Developer Advocate at Google, Ido Green
  • Python Chatbots Part I with Machine Learning with Software Engineer and Data Scientist at Microsoft, Micheleen Harris 
  • Machine Learning with Microsoft Azure with Siddharth Ramesh, Data Scientist at Microsoft
  • Solving Real-World Data Problems with Spark with David Drummond, PhD,   Director of Engineering at Insight Data Science
  • Solving Real-World Data Problems with Spark with Judit Lantos, Data Engineering Team Lead at Insight Data Science
  • Role of Machine Learning in Natural Languages Arpan Chakraborty, PhD , Artificial Intelligence and Machine Learning Instructor at Udacity
  • Take a Journey from Data Science into Cognitive Computing with CTO, Analytics and Machine Learning at IBM, Dr. Ali Arsanjani  
  • Pre-trained Models, Transfer Learning and Advanced Keras Features with Data Scientist and Instructor at CATALIT, Francesco Mosconi, PhD
  • Alexa Skills Workshop: Building Voice Experiences w/ Amazon Alexa with Paul Schindler, Community Manager at Coding Dojo
  • Introduction to Data Visualization Using D3.js with Nicholas Watts, Software Developer at DataRobot 

 

Any ticket grants access to even more training with many more additional workshops

Here’s a sample from our previous event.


  • Boosting Product Categorization with Machine Learning

  • Product Analytics: Transforming Social Data into Actionable Insights

  • Apache Spark: A Hands-On Workshop

  • Driver and Occupants Monitoring AI for Autonomous Vehicles

  • Deep Learning in Production with Deeplearning4j

  • Security in Machine Learning with CleverHans

  • Modern Time-Series with Prophet

  • Beyond Word2vec: Recent Developments in Document Embedding

  • Recommendation System Architecture and Algorithms

  • Introduction to Representational Learning

  • Scalable Data Science and Machine Learning using R4ML

  • Data Science for Executives Part I

  • Advice For New And Junior Data Scientists

  • Strategies for Practical Active Learning

  • The Power of Monotonicity to Make ML Make Sense

  • Building a Chatbot for a Large Company: 3 Reasons Why These Projects Fail and 5 Steps How To Fix It

  • R Tools for Data Science

  • Hyperparameter Tuning in Cloud Machine Learning Engine Using Bayesian Optimization

  • Training a Prosocial Chatbot

  • Using Open-Source Tools in Support of Neglected Diseases Drug Discovery

  • Concrete NLP Solutions for Organizing, Classifying, and Generating Text Data

  • In-Memory Computing Essentials for Data Scientists

  • Black Mirror: Training and Deploying a Chatbot to Talk Like You with Deep Learning

  • Data Science for Executives Part II

  • Deep Neural Networks with Keras

  • From Machine Learning to Native GPU Code Generation All in One Language

  • Deep Learning From Scratch Using Python

  • Machine Learning & Deep Learning on AWS

  • Industry Classification at Scale: Leveraging Web Data, Crowdsourcing and Distributed Computing for Model Generation

  • Building a Predictive Model in Python

  • Dynamic Risk Networks: Mapping Risk in the Financial System

  • What Do Neural Embedding Based Translation Algorithms Tell Us About Language Similarity?

  • Deep Learning in R vs. Python Using Keras

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