November 1st - 3rd, 2022
Data Science Kick-start
Kick-start Your Data Science Career
Learn Essential Skills to Kick-start Your Career
Data Science skills are in ever-increasing demand. Whether you are a data science beginner, a software engineer looking to augment skills, or even a business professional looking beyond the hype, our Kick-start track is for you.
The Data Science Kick-start track offers many introductory level talks, tutorials, and workshops to get you started. Our expert speakers and presenters offer world-class instruction, and give you invaluable insights to help you learn what matters most. We cover the most important tools, topic, and techniques in use to ensure you hit the ground running.
Previous Speakers
Thomas J. Fan
Thomas J. Fan is a Staff Software Engineer at Quansight Labs and is a maintainer for scikit-learn, an open-source machine learning library for Python. Previously, Thomas worked at Columbia University to improve interoperability between scikit-learn and AutoML systems. He is a maintainer for skorch, a neural network library that wraps PyTorch. Thomas has a Masters in Mathematics from NYU and a Masters in Physics from Stony Brook University.
Introduction to scikit-learn: Machine Learning in Python (Training)
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)
Leonardo De Marchi
Leonardo De Marchi holds a Master in Artificial intelligence and has worked as a Data Scientist in the sports world, with clients such as the New York Knicks. He now works in Thomson Reuters as VP of Labs, and also provides consultancy and training for small and large companies. His previous experience includes being Head of Data Science and Analytics in Bumble, the largest dating site with over 500 million users, heading the team through acquisition and an IPO.
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 cyber security 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)
Daniel Gerlanc
Daniel Gerlanc has worked as a data scientist for more than decade and been writing software 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(Bootcamp)
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)
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)
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 at conferences 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. Jennifer enjoys working with clients and helping them achieve their goals.
Large Scale Deep Learning using the High-Performance Computing Library OpenMPI and DeepSpeed(Workshop)
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)
Lara Kattan
Lara is a Risk Management Specialist at 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.
Probabilistic Programming and Bayesian Inference with Python (Half-Day Training)
Clinton Brownley, PhD
Clinton Brownley, Ph.D., is a data scientist at Meta (formerly 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 data analysis and visualization techniques, Clinton teaches a graduate course in interactive data visualization for UC Berkeley’s MIDS program, taught a short-term graduate course in regression analysis and machine learning workshop for NYU’s A3SR program, leads an annual machine learning in Python workshop, and is the author of two books, “Foundations for Analytics with Python” and “Multi-objective Decision Analysis”.
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.
Machine Learning with Python: A Hands-On Introduction(Training)
Stefanie Molin
Stefanie Molin is a software engineer and data scientist at Bloomberg in New York City, where she tackles tough problems in information security, particularly those revolving around data wrangling/visualization, 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, as well as 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.
What You’ll Learn
Data science has many focus areas. The goal of this track is to accelerate your knowledge of data science by offering a series of introductory level training sessions, talks, and workshops on the most important tools and topics to help you quickly gain essential data science skills
Deep Learning
Machine Learning
Artificial Intelligence
Text Analytics
Data Wrangling
Predictive Analytics
Machine Vision
Voice AI
Python for Data Science
R & Julia
Apache Spark, MLlib
Scikit-learn, Azure ML, Amazon ML, H20.ai
Tensorflow, Caffe, CNTK, Torch
Data Visualization
Neo4J, D3.js, R-Shiny
Why Attend?
Accelerate and broaden your knowledge of key areas in data science, including deep learning, machine learning, and predictive analytics
With numerous introductory level workshops, you get hands-on experience to quickly build up your skills
Post-conference, get access to recorded talks online and learn from over 100+ high quality recording sessions that let you review content at your own pace
Take time out of your busy schedule to accelerate your knowledge of the latest advances in data science
Learn directly from world-class instructors who are the authors and contributors to many of the tools and languages used in data science today
Meet companies ranging from hot startups to Fortune 500s looking to hire professionals with data science skills at all levels
Network at our numerous lunches and events to meet data scientists, enthusiasts, and business professionals
Get access to other focus area content including ML / DL, Data Visualization, Quant finance, and Open Data Science
Who Should Attend
The Data Science Kick-start track is ideal for anyone looking to learn the languages, tools, and topics of data science. You will train in key areas of data science like deep learning and machine learning, and learn the tools and languages to implement modules such as TensorFlow, scikit-learn, Python, and R
Beginners interested in getting started in data science
Individuals seeking to better understand focus areas of data science such as deep learning, machine learning, text analytics etc.
Software engineers and software architects looking to employ machine learning and data science in their programming
Data wranglers and database specialists looking to leverage their existing data assets with data science tools and models
Business professionals interested in data science and looking to gain a deeper understanding
Experienced data scientists looking to enhance their data science skills
Anyone interested in learning data science languages such as Python, R, and Julia
Technologists looking to use the latest data science tools such as Apache Spark and TensorFlow to implement machine learning and deep learning
Students and academics looking for more practical applied training in data science tools and techniques
Industry experts looking to assess the impact of data science on their industry
ODSC Newsletter
Stay current with the latest news and updates in open source data science. In addition, we’ll inform you about our many upcoming Virtual and in person events in Boston, NYC, Sao Paulo, San Francisco, and London. And keep a lookout for special discount codes, only available to our newsletter subscribers!