It’s Time to Talk Data—At the Upcoming Data Dialogs Conference 2015
If you’re dealing with data in any manner—or don’t, and need to be—then you’ll want to mark your calendar for the upcoming Data Dialogs conference, hosted by the UC Berkeley School of Information. This exciting one-day data science conference will be held November 13, 2015 and provide an intimate and informal setting at UC Berkeley—where data experts from industry, research and academia will build relationships to create new ideas together.
Who should attend?
Conference topics will be applicable to individuals from a variety of sectors, including:
- Non-profit organizations
- Academic institutions
- Private sector businesses
- Public sector entities
Who will be speaking about what?
The day-long event will include a range of topics from data industry experts from around the country who’ll make up the 16-member schedule of speakers. They include business leaders, academics, and recent graduates of the Master of Information and Data Science program at Berkeley. Topics covered will include:
- How organizations are integrating data science and data science teams into business strategies.
- The best approach for using data to drive evidence-based projects.
- An exploration of the legal, policy and ethical implications of using data.
- The cutting-edge technologies that are available to optimize data use.
Here’s a sampling of speakers and what they’ll be presenting:
- Keynote: A. Charles Thomas, Chief Data Officer, Wells Fargo.Thomas is a 16-year data and analytics professional and executive vice president of the Technology and Operations Group for Wells Fargo & Company. He’s responsible for developing Wells Fargo’s approach to managing data, including the development of Enterprise Data Governance, Foundational Data, and a common data architecture.
- John Akred, Founder & CTO, Silicon Valley Data Science. Akred has over 15 years of experience in machine learning, predictive modeling, and analytical system architecture. He’ll be presenting “Running Agile Data Science Teams”—discussing the best methods for pursuing data-driven projects with cross-functional teams comprised of various disciplines.
- Molly Jackman, Public Policy Research Manager, Facebook. At Facebook, Jackman is responsible for guiding the company's research agenda from a policy perspective. She’ll be presenting “Industry Research: What, Why and How”—with a focus on why Facebook does research, its areas of focus, and the review process they use to determine both.
- Lina Nilsson, Head of Market Development, Enlitic. At Enlitic, Nilsson oversees the integration of the company’s deep learning technology into medical diagnostics and healthcare decision making. She’ll be presenting “Deep Learning for Medical Diagnosis”—focusing on how deep learning using large data sets within the increasingly digital world of healthcare can help doctors make medical diagnoses faster with higher rates of accuracy and accessibility.
- Anna Lauren Hoffman, Post-Doctoral Scholar, UC Berkeley School of Information. Anna is a trans woman and scholar working at the intersections of information, technology, culture, and ethics.She’ll be jointly presenting “Privacy is Dead, The End of Theory, and Other Things People Shout On the Internet: A Conversation About Ethics, Power, and Data Science.”
We’re excited to welcome our recent MIDS graduates who will be presenting a panel discussion of “Machine Learning and Baseball: A MIDS Capstone Project”—focusing on the system they built to predict pitching dynamics and the lessons they learned in the process:
- Zach Beaver, Data Scientist, Elder Research, Inc. Zach Beaver is a data scientist at Elder Research, Inc. in Washington DC, where he assists government agencies in leveraging their data to support better decision-making. His recent work includes fraud detection, developing methods to assess the credibility of home appraisals, and entity resolution. He is a recent graduate of UC Berkeley's Master of Information and Data Science program and holds degrees in Biology and Computer Science from Wofford College.
- Josh Lu, Tax Manager, Ernst and Young, Quantitative Services Group. Josh is a recent graduate of the UC Berkeley Master of Information and Data Science program. He is a CPA and works as Tax Manager in Ernst and Young's Quantitative Services group, specializing in research and development tax credits. He holds a Bachelor's degree in Accounting from Santa Clara University.
- Alan Si, Data Scientist, Castlight Health. Alan is a recent graduate of the UC Berkeley Master of Information and Data Science program. He previously worked for Upworthy as a Machine Learning Fellow and is currently a Data Scientist at Castlight Health where he focuses on improving user engagement and patient outcomes. He is originally from Canada and holds Bachelor degrees in Mechanical Engineering as well as Business Administration.
- Jason Goodman, UC Berkeley School of Information. Jason is a recent graduate of the UC Berkeley Master of Information and Data Science (MIDS) program. Prior to MIDS, he worked as a management consultant at Oliver Wyman, solving strategy problems for clients in the retail, pharmaceutical and financial services sectors. He holds a B.A. degree in Economics from Dartmouth College.
We asked Goodman for a few perspectives about his educational experience and how he’ll be using what he learned in the program:
Jason, please tell us a little about your learning experience in the Master of Information and Data Science program at Berkeley.
“In college, I studied econometrics and computer science. Thus, I knew I liked to program, and that I liked answering questions with data. After college, I worked as a management consultant. The experience taught me what a difference data can make for a company when it's used properly. However, I felt limited. In consulting, you can only do analyses if your clients can understand them, and most clients weren't very technically sophisticated. I would read on the weekends about machine learning, randomized experiments and big data techniques. I knew how powerful these tools could be for companies and, more broadly, for society at large. I felt like I was crawling while these 'data scientists' were running.
So MIDS was all about learning to run. It drew on much of what I already knew - programming, statistics, and business strategy - and expanded it further. The classmates were uniformly smart, interesting people from incredibly diverse backgrounds. Many of the professors came from industry, so they knew what it was really like to use these skills. I was in the program's first graduating class, so there were definitely some bumps along the way, but in general I found the classes valuable. More than anything, the degree gave me a taste of a lot of different areas in data science. I've set up a Hadoop cluster, run a randomized experiment, competed in a Kaggle machine learning competition, mulled the ethical considerations in data work, crafted a visualization in D3.js and created a data product from scratch. I've got a good foundation for whatever comes next!”
What are your career plans for using the skillsets you've acquired in data science?
“In the short term, my plan is to work at a mid-to-large sized tech company in the Bay Area, one that appreciates the importance of using data to make decisions. MIDS has given me a great toolset and I can't wait to apply it in industry. Longer term, I hope to use my skills with organizations like DataKind and Bayes Impact to help make a positive impact in the world.”
In addition to your capstone presentation, are there specific things that you're looking forward to during the conference?
“Of course, I'm excited to present with my team! Our capstone project involved predicting baseball pitcher tendencies with machine learning. We're looking forward to not only explaining our project and its results, but the wider lessons we learned about making data products with machine learning. In addition, I'm looking forward to being an audience member at Data Dialogs! We met Uber's Kevin Novak on a MIDS field trip, and we all walked away amazed with what Uber's doing with data. I can't wait for his talk. There are two talks that focus on using deep learning in vital settings: healthcare and cybersecurity. Those should both be fascinating. Finally, Anna was my professor for the ethics and policy course in the MIDS degree. I learned something in every one of her classes, and I know I'll enjoy her discussion with Nathan.
And I’m honored to be invited to speak at Data Dialogs!”