All Times EDT
Poster Q&A will be available during these designated hours as part of the virtual conference.
We will focus on how data science groups are organized within data science institutes in academia and within industry and government.
With the recent big data, data science, and deep learning revolution, companies across the world are hungry for data scientists and machine learning scientists to bring actionable insight from the vast amount of data collected. In the past couple of years, deep learning has gained traction in many application areas and become an essential tool in the data scientist’s toolbox.
In this course, participants will develop a clear understanding of the big data cloud platform and technical skills in data science and machine learning. They will use hands-on exercises to understand deep learning. We will also cover the “art” part of data science and machine learning so participants learn the typical data science project flow, general pitfalls in data science and machine learning, and soft skills to effectively communicate with business stakeholders.
The big data platform, data science, and deep learning overviews are specifically designed for an audience with a statistics education background. This course will prepare statisticians to be successful data scientists and machine learning scientists in various industries and business sectors with deep learning as a focus. Please have a laptop available for hands-on sessions. No software download or installation is needed.
Quantum computing isn’t science fiction anymore. IBM, D-Wave, and Rigetti all provide cloud access to their quantum processing units (QPUs). Have a laptop available! We will talk about the basics of quantum computing and how to implement an algorithm on actual quantum hardware. We will focus on Rigetti’s Forest SDK, a set of Python libraries designed to interact with QPU, and practical quantum computing, rather than theory. Participants will learn about the following:
• The notion of a quantum bit • Different quantum computing architectures • Various quantum logic operations and how to implement them in code • Rigetti’s Python API to interact with the quantum device • How to write and execute a quantum program
Winston Churchill observed that "we shape our buildings, and afterwards our buildings shape us." The same is true of our development environment, which shapes our development process. Ad hoc and unstructured environments lead to unstructured processes that are difficult to reproduce. This short course leverages the author's crant toolkit and shows how to use Docker, git, make, and other tools to create a development environment optimized for reproducible research. At the end of the course, you'll be able to create a re-usable environment that automates testing, packaging, report generation, and more. You'll also learn how to incorporate notebooks into your development process in a way that maintains reproducible research.
We all hear about data science technology these years. What is data science? How does data science change the things around us? This short course serves as an introduction to a combination of practical data science technologies with a focus on experimentation, recommendation systems, and reinforcement learning. We will talk about how these core technologies help build a great product. At the end of the course, audience is expected have a clear understanding of various data science technologies and applications. Both lectures and lab exercises will be offered.