It’d be great if people brought a collection of junk from home (no, really). Old shoes, pencils, pens, scrap paper, cans (full or empty), popsicle sticks, duct tape, etc. All junk will be welcome. We are going to build stuff!
Being “data-driven” is about more than just storing lots of data and generating reports. As with many other types of projects, the most crucial part of any data-oriented project is choosing an appropriate problem or opportunity on which to focus in the first place. In this tutorial, you will learn how to apply design thinking to identify problems and opportunities where data can be used as part of a solution. We will go through a series of small-group exercises where we focus on defining problems, considering current solutions, creating new approaches, and building prototypes. Participants will leave armed with a new perspective on how to use data as a resource within their own organizations.
Mike Stringer is co-founder and managing partner of Datascope Analytics, a consulting and design firm, where he has lead or contributed to projects across a variety of industries for clients including Procter & Gamble, Thomson Reuters, and other leading companies. Mike is passionate about realizing the potential for data to be used as a resource to make a positive impact on business and society.
He also enjoys decidedly non data-oriented activities, including exploring the amazing food in Chicago, playing and listening to music, and generally making things from scratch. Mike received a BS in Engineering Physics from the University of Colorado and a PhD in physics from Northwestern University.
Dean Malmgren is co-founder and managing partner of Datascope Analytics. As an author of several peer-reviewed publications on big data analytics and visualization, Dean is excited about bringing cutting-edge techniques out of research and into practice. When not teasing himself or others, Dean can be found swimming, cycling, or running for silly long distances. Dean received a BS in math and chemical engineering from the University of Michigan and a PhD in chemical engineering from Northwestern University.
Laurie Skelly is a Data Scientist at Chicago-based data consulting firm Datascope, and a curriculum developer and instructor for the Data Science bootcamp at Metis, Kaplan’s school for new economy skills training. Connecting a real-world problem to its ideal technical solution is an art and a science. At Datascope, Laurie has led and contributed to projects for clients ranging from international Fortune 50 giants to regional nonprofits, representing a broad collection of business sectors and verticals. For her PhD in social neuroscience from the University of Chicago, she employed machine learning algorithms to model the neural circuitry of emotion in incarcerated psychopathic offenders.
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