Oct 6th, 2017
DS521: A Hands-on Dive into Making Sense of Real World Data
From a user’s past reviews on Yelp, how likely is she to assign a five-star review to a business she has not reviewed before? This workshop teaches you how to solve data science problems like this using Jupyter Notebooks. We will cover common statistical techniques and popular Python libraries for data analysis and machine learning (Pandas, matplotlib, and scikit-learn).
Xun Tang Machine Learning Engineer, Yelp Inc.
Xun Tang is a Software Engineer at Yelp focusing on Ads Creative. On a daily basis, she uses Jupyter Notebooks to analyze data and train models to optimize a wide range of metrics. Prior to Yelp, she has worked on search and data integration problems at Yahoo! and Electronic Arts. She holds a Ph.D. in Computer Science from University of California, Santa Barbara. She is devoted to applying machine learning skills to solving real-world data problems.
Jamie Whitacre Technical Project Manager, Project Jupyter
Jamie Whitacre is the Technical Project Manager for Project Jupyter. She works with the core Jupyter team on development strategy and in strengthening connections with the larger Jupyter community. She works from the Berkeley Institute for Data Science (BIDS) and is an affiliate of the Lawrence Berkeley National Laboratory. Before joining the project, Jamie worked with the Smithsonians National Museum of Natural Historys Informatics team designing and building scientific data systems for the museums genomics and biodiversity research initiatives.