Practical problem solving with data involves more than just visualization or applying the latest machine learning techniques. Intuition, domain knowledge, and reasonable approximations can mean the difference between a successful model and a catastrophic failure. We’ll dive into some best practices I’ve extracted from solving real world problems like computing trending topics, finding related searches, cleaning election data, and ranking experts on social networks.
New analysts or engineers are often lost when textbook approaches fail on real world data. Drawing inspiration from problem solving techniques in mathematics and physics, we will walk through examples that illustrate how come up with creative solutions and solve problems with big data.
Pete Skomoroch is a Principal Data Scientist at LinkedIn focused on reputation systems, personalization, and creating data driven products like LinkedIn Skills. Before joining LinkedIn, he was the Director of Advanced Analytics at Juice Analytics and a Sr. Research Engineer at AOL Search. Prior to AOL, he implemented pattern detection algorithms for streaming sensor data at MIT Lincoln Laboratory and constructed predictive models for large retail datasets. Pete has a B.S. in Mathematics and Physics from Brandeis University and blogs at DataWrangling.com.
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