ODSC Webinar Calendar
ODSC East 2019 Warm-Up
Principal Data Scientist
Becoming The Complete Data Scientist with Data Literacy and Data Storytelling
I will review some of the key data literacy components that contribute to successful data science in real world applications. In discussing these concepts, I will give examples through the art of data storytelling, which aims to answer the core questions that your clients, colleagues, and stakeholders want to have answered: What? So what? Now what? By focusing your effort on addressing the user questions and user requirements, which then drive your project’s data and modeling activities, which then fuel your final data products and project deliverables, you will establish yourself as a key contributor to any analytics team. Your technical skills may bring you customers, but it’s not the technical stuff that you know (i.e., your successes) that brings your customers back. What brings customers back is your customers’ successes, which are nurtured and grown through clear explanations of the data, the modeling activities, and the results, which they can then share with others.
Kirk Borne is a data scientist and an astrophysicist who has used his talents at Booz Allen since 2015. He was professor of astrophysics and computational science at George Mason University (GMU) for 12 years. He served as undergraduate advisor for the GMU data science program and graduate advisor in the computational science and informatics Ph.D. program.
Kirk spent nearly 20 years supporting NASA projects, including NASA’s Hubble Space Telescope as data archive project scientist, NASA’s Astronomy Data Center, and NASA’s Space Science Data Operations Office. He has extensive experience in large scientific databases and information systems, including expertise in scientific data mining. He was a contributor to the design and development of the new Large Synoptic Survey Telescope, for which he contributed in the areas of science data management, informatics and statistical science research, galaxies research, and education and public outreach.
Ph.D., Author, Lecturer, Core Contributor of scikit-learn
Introduction to Machine Learning
Machine learning has become an indispensable tool across many areas of research and commercial applications. From text-to-speech for your phone to detecting the Higgs boson, machine learning excells at extracting knowledge from large amounts of data. This talk will give a general introduction to machine learning, as well as introduce practical tools for you to apply machine learning in your research. We will focus on one particularly important subfield of machine learning, supervised learning. The goal of supervised learning is to “learn” a function that maps inputs x to an output y, by using a collection of training data consisting of input-output pairs. We will walk through formalizing a problem as a supervised machine learning problem, creating the necessary training data and applying and evaluating a machine learning algorithm. The talk should give you all the necessary background to start using machine learning yourself.
Andreas Mueller received his MS degree in Mathematics (Dipl.-Math.) in 2008 from the Department of Mathematics at the University of Bonn. In 2013, he finalized his PhD thesis at the Institute for Computer Science at the University of Bonn. After working as a machine learning scientist at the Amazon Development Center Germany in Berlin for a year, he joined the Center for Data Science at the New York University in the end of 2014. In his current position as assistant research engineer at the Center for Data Science, he works on open source tools for machine learning and data science. He is one of the core contributors of scikit-learn, a machine learning toolkit widely used in industry and academia, for several years, and has authored and contributed to a number of open source projects related to machine learning.
Ph.D. in Physics and Data Scientist at Catalit LLC, Instructor at Udemy
Pre-trained models, Transfer Learning and Advanced Keras Features
You have been using keras for deep learning models and are ready to bring your skills to the next level. In this workshop we will explore the use of pre-trained networks for image classification, transfer learning to adapt a pre-trained network to your use case, multi gpu training, data augmentation, keras callbacks and support for different kernels.
Francesco Mosconi, Ph.D. in Physics and 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.
Senior Software Engineer at Comet.ML
Easy Visualizations for Deep Learning
Visualizations are important in order to debug and understand how a Deep Learning model is representing a problem. In this talk, I will introduce a layer of software (ConX) that was developed on top of Keras in Jupyter Notebooks for making useful (and beautiful) visualizations of activations of a neural network. We will develop a model from scratch, train it, test it, and explore various tools for visualizing learning over time in representational space.