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 makes it easy for everyone—not just the “data people”—to get clear, accurate, fast answers to any business question. Our cloud-native data catalog maps your siloed, distributed data to familiar and consistent business concepts, creating a unified body of knowledge anyone can find, understand, and use. data.world is an Austin-based Certified B Corporation and public benefit corporation and home to the world’s largest collaborative open data community.
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
AI For Good
Technology companies are the beneficiaries of AI technology and should shoulder the social responsibility for technology applications, and allow AI to be more reliable. Since initiating research in the field of AI, Microsoft has always paid attention to the ethical issues of AI development and application. When facing the challenges posed by AI, we are committed to helping everyone prepare for adequate responses, including helping students to cope with future career challenges, helping workers deal with changing industry conditions, and building a system to match workers with employment opportunities. Realizing “AI for good” requires cooperation between all industries, including the technology sector. Microsoft looks forward to working with practitioners from all fields to develop and share best practices. “AI for good” can be achieved by popularizing computational thinking education, promoting digital transformation, strengthening government participation, and achieving self-discipline in technology companies. AI benefiting mankind is not just a vision, but a future we are all headed towards. In this session, we talk about how AI is already changing some non-technical industries and helping achieve more on societal good.
Anusua Trivedi is a Data Scientist at Microsoft’s AI for Good Research Lab. She is focused on Applied Research and works on developing advanced Deep Learning models for advancing Societal Good. Anusua has also held positions with UT Austin and University of Utah. Anusua is a frequent speaker at machine learning and AI conferences all across the world.
Sr. Data Scientist
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 – 3:35 PM
Dir. Product Management
3:35 PM – 4:05 PM
4:05 PM – 4:25 PM
State of the Art Natural Language Processing in Healthcare
The ability of software to reason, answer questions and intelligently converse about clinical notes, patient stories or biomedical papers has risen dramatically in the past few years. This talk covers state of the art natural language processing, deep learning, and machine learning libraries in this space. We’ll share benchmarks from industry & research projects on use cases such as clinical data abstraction, patient risk prediction, named entity recognition & resolution, de-identification, and OCR.
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
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
5:05 PM – 5:50 PM
Data Literacy Panel Discussion
Patrick McGarry, data.world, Head of Strategic Partnerships
Ben Jones, Data Literacy, CEO
Gisselle Yang Xie, Lyft, Research Science Manager
5:50 PM – 6:50 PM
March 26, 2020
12:30pm – 6:00pm
3709 157th Ave NE
Redmond, WA 98052