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 data scientists, AI and ML experts, analysts, business users, and managers for an afternoon of discussions on best practices, use cases, and cutting-edge tools. Learn from leaders in data 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 Does it Mean to be Highly Data Literate?
The term “data literacy” has been surfacing lately, but it begs the question – what is “data literacy”, anyway, and what does it look like to have it? In this presentation, we’ll go beyond a limited focus on what tools you’ve mastered and we’ll consider 17 key traits of data literacy divided into categories of knowledge, skills, attitudes and behaviors. We’ll finish with a self-assessment of our own strengths and weaknesses.
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 Communicating Data With Tableau (O’Reilly 2014) and he also writes about data topics at his blog DataRemixed.com.
Ben is a part-time Human Centered Design Thinking coach and has helped non-profits and early-age startups develop clarity on their mission and recognize growth areas. He moved to the Bay Area in 2010 and somehow managed to acquire a Masters in Structural Engineering after spending two years actually learning how to think.
2:05 pm – 2:35 PM
Bias and Ethics in Machine Learning
Machine Learning (ML) is a transformational technology with tremendous potential to drive innovation in research and business. Businesses utilizes ML have a substantial advantage over their competitors who have yet to embrace it. But with the benefits, there are also risks. Increasing reliance on algorithmic decisions with limited or no human oversight has raised new challenges for companies navigating the business issue of ML ethical risks.This talk will cover the current landscape of ML bias and ethics, provide cautionary tales of recent ML ethical mishaps, and describe the tools and best practices you can adopt to avoid similar mishaps.
Lisa Green is a science geek with a strong affinity for computer nerds, and she is passionate about open data. Lisa earned her PhD in physical chemistry from the University of California Berkeley, where she got to play with lasers and pull apart single molecules of RNA. Lisa moved from the lab to the office working in bioinformatics at NextBio. She then joined Creative Commons, first as a Program Director and later as Chief of Staff. In late 2011, Lisa launched Common Crawl, which now boasts petabytes of open data being used for some very cool projects. Lisa was the founding CEO of a micro-philanthropy startup, Click2Care, and Head of Social Impact at Domino Data Lab. She currently serves as the Executive Director of Solve for Good, a collaboration platform that matches skilled volunteers with data-driven social good projects.
Data Science for Social Good
2:35 pm – 3:05 PM
3:05 Pm – 3:35 PM
Five Mindsets to Succeed as a Data Scientist
As the title of Data Scientist becomes more ubiquitous, the big question for practitioners and employers becomes: “What factors lead to the success of a Data Scientist?”. Along with access to rich datasets, powerful tools and the skills to utilize the tools, the mindsets the Data Scientist possesses, and the environment the employers of Data Scientists create for these mindsets to flourish, are major contributors to success as well. In this talk we will learn what these mindsets are and how these can be applied within an enterprise to increase the odds for Data Scientists to succeed.
During daytime, Pallav works as a Data Scientist and tries to extract meaningful signals from the noisy world we live in. As the moon rises and evening sets in all bets are off and one might find Pallav on his bike riding through the Berkeley hills in bright colored lycra or performing never-before-scenes of Dramedy with his Improv troupe.
Pallav is a part-time Human Centered Design Thinking coach and has helped non-profits and early-age startups develop clarity on their mission and recognize growth areas. He moved to the Bay Area in 2010 and somehow managed to acquire a Masters in Structural Engineering after spending two years actually learning how to think.
He is an avid follower of Seth Godin, Ken Robinson, and Nicholas Taleb, and is currently looking at ways to explain algorithms through cute, anthropomorphized animals.
Levi Strauss & Co.
3:40 pm – 4:10 PM
Managing & Leading a Machine Learning team: How is it different from being an individual contributor?
Have you ever thought of becoming a manager? Have you ever wondered what are the skills needed? What’s it like being a manager, as opposed to being a developer? These are questions that we all have at some point in our career. The skills required to code and the skills needed to manage people are very different. Being a good manager is also not the same as being a good leader. Moreover, being a good manager of developers requires being able to think like a developer. In this talk, I will share my experience from being an IC and a technical lead to a manager, illustrating some key differences and providing some lessons learned.
Gabriela de Queiroz is a Sr. Engineering & Data Science Manager/Sr. Developer Advocate/ at IBM where she works on AI building tools, launching new open source projects, and improving existing core open source frameworks. She is the founder of R-Ladies, a worldwide organization for promoting diversity in the R community with more than 135 chapters in 44+ countries. She likes to mentor and shares her knowledge through mentorship programs, tutorials and talks. She holds 2 Master’s: one in Epidemiology and one in Statistics.
Gabriela de Queiroz
Founder at R-ladies
4:15 PM – 5:00 PM
5:00 PM – 6:00 PM
April 2nd, 2019
1:30pm – 6:00pm
Galvanize 44 Tehama St,
San Francisco, CA 94105, USA