Learn how to make everyone more productive with data, from advanced practitioners to business users.
If data is only useful to highly-skilled data practitioners, it’s not living up to its original promise of making your organization smarter, faster, and more effective. Let’s fix that, together.
Join other executives like you for an afternoon of discussions on best practices, use cases, and cutting-edge tools around data practices. Learn from leaders in data who have engineered organizational change or implemented data practices at scale in an afternoon of presentations, and engage face-to-face with your peers at an evening reception.
RSVP today as spaces are limited. We look forward to seeing you!
data.world is the modern catalog for data and analysis. Choose data.world to wake up the hidden data workforce within your enterprise, multiply your data’s value, and create a data-driven culture faster. Bring together employees of all roles, backgrounds, and skills to work collaboratively using the tools they already love. data.world uses a knowledge graph to keep data connected to everything people need to find, understand, and use it. As a result, your data, analysis, and expertise become more discoverable, trustworthy, and reusable. Visit data.world to learn more.
12:30 pm – 1:30 PM
1:30 pm – 2:00 PM
What Data Scientists Need to Know
Data scientists face many of the same challenges software engineers faced 40 years ago. I cover some of these common challenges and different types of “data scientist” roles. I also discuss skills you will need on the job, including the overall data science workflow, framing projects, moving and reshaping data, interpreting results, and maintaining a fast science loop with quality at scale.
Michael Brundage is currently a Data Scientist at Google working on developer insights, and recently elected Trustee for the National Institute of Statistical Sciences. Previously, Michael was a Principal Data Scientist at Microsoft where he helped create the Windows Experimentation Platform, Principal Engineer at Amazon where he helped create the Data Scientist role, Distinguished Engineer at Yahoo, and Computational Analyst at Caltech/JPL. https://linkedin.com/in/michaelb
Staff Data Scientist
2:05 pm – 2:25 PM
2:30 PM – 3:00 PM
The Role of Data Quality Management in Data Science Reproducibility
At Hulu, the Data Quality Management team works with our data engineering teams to produce high quality data products that can be confidently consumed by our analytics and data science teams for reporting and modeling. Here I will talk generally about the importance of Data Quality Management to drive trust in data-driven insights and strategic initiatives and more specifically about how we are working with the Data Science team on their Feature Pipeline to improve the speed of model creation, reproducibility of model results, and trust in the conclusions.
Wendy Grus is currently a senior data analyst on the Data Quality Management team at Hulu in Seattle. She helps data engineering teams make high quality data products and helps data analysts and data scientists investigate data quality issues. Before moving over to tech, she worked in biotech as a bioinformatics analyst/scientist. Her background is in comparative evolutionary genomics where she looked for differences in genetic data patterns and investigated their evolutionary or health implications. Now she looks at differences in data patterns and investigates their implications on data usability. She volunteers for organizations that empower women and girls in music and technology, such as Rain City Rock Camp and Seattle PyLadies.
Sr Data Analyst, Data Quality Management
3:05 PM – 3:25 PM
Lessons Learned Deploying AI in Healthcare
David Talby is a chief technology officer at Pacific AI, helping fast-growing companies apply big data and data science techniques to solve real-world problems in healthcare, life science, and related fields. David is a creator of the Spark NLP open-source library, the most widely used NLP library in the enterprise, and leads its evolution at John Snow Labs. Previously, he led business operations for Bing Shopping in the US and Europe with Microsoft’s Bing Group and built and ran distributed teams that helped scale Amazon’s financial systems with Amazon in both Seattle and the UK. David holds a PhD in computer science and master’s degrees in both computer science and business administration.
John Snow Labs
3:25 PM – 3:55 PM
3:55 PM – 4:25 PM
Data Science Lead
Data Scientist Lead
4:30 PM – 5:00 PM
7 Factors of Team-Based Data Literacy
Teams of all types and sizes in every industry are seeking to assess their level of data literacy, and to determine what they need to do in order to evolve to a more highly data literate state. This begs the question: what are the key characteristics of a highly data literate team? In this presentation, we’ll consider seven key factors that together comprise the team’s state, and we’ll consider how these factors can contribute to or detract from their level of data literacy.
Ben Jones is the founder and CEO of Data Literacy, LLC, a training and education company that’s on a mission to help people learn the language of data. Ben teaches data visualization at the University of Washington, he’s the author of Avoiding Data Pitfalls (Wiley, 2019) and Communicating Data With Tableau (O’Reilly 2014). Ben also writes about data topics at his blog DataRemixed.com, and to balance out the digital side of things, he loves hiking and backpacking on the beautiful trails of the Pacific Northwest. Ben holds a BS in Mechanical Engineering from UCLA (2000) and an MBA from California Lutheran University (2011).
CEO & Founder
4:15 PM – 5:00 PM
Data Literacy Panel Discussion
Patrick McGarry, data.world, Head of Strategic Partnerships
Ben Jones,CEO, Data Literacy
Sarah Nell-Rodriquez, Data Literacy Strategic Manager, Qlik
Gisselle Yang Xie, Research Science Manager, Lyft
5:00 PM – 6:00 PM
March 26, 2020
12:30pm – 6:00pm
3709 157th Ave NE
Redmond, WA 98052