Determining the best organizational structure for data science teams is often debated. Should the data science team be an independent function, reporting into a head of Analytics or IT, providing support to various lines of business? Should data scientists be federated, reporting into business units and operating somewhat independently from each other? Or does a matrix organizational structure provide the best of both worlds? One approach that's becoming increasingly common is to establish a Data Science Center of Excellence -- a core team of data science experts that sets the course for a company's data science strategy, best practices and operating procedures, and technology ecosystem. They can offer guidance to citizen data scientists, data scientists reporting directly into business functions, and other stakeholders leveraging data science across the organization. But how do you go about establishing at Data Science Center of Excellence? During this workshop, we'll walk through organizational designs, a how-to guide for getting started, and will share best practices and lessons learned based on first-hand experience building a CoE in the field. We'll discuss what capabilities belong in a COE; whether / how to balance deep ML expertise vs. broad analytics capabilities. Attendees will leave with an actionable plan to get started on this path.
Audubon applies data science in its mission to understand how climate change will affect birds, which species will be most vulnerable, and what places will suffer the most climate-related threats. This past summer, Audubon released a report summarizing research from an unprecedented, model-driven assessment of the potential future impacts of climate change on 38 species of grassland birds. All of which was performed on Domino. In this session, Dr. Chad Wilsey, Audubon's interim chief scientist, will discuss: /- Why data science is important to Audubon's mission - Key findings from their recent research - Use cases & workflows - Organizational and technology challenges encountered - How Audubon's historical research processes have evolved as data science has matured as a function
A key driver to a successful data science platform is the ability to customize and extend beyond the default stock behavior. Domino allows this through its “open” approach that lets data scientists use their preferred languages, tools, and data sources. Additionally, a growing ask to accelerate data science is the ability to quickly obtain compute resources, especially with distributed and parallel processing. In this session, we will explore the data science platform's ability to quickly and dynamically spin up distributed Spark and Dask clusters. We will also explore integrations with other tools such as H2O, DataRobot, and others.