Data does not speak for itself. While successful data science is dependent on data and modeling, visualization is also essential for communicating the patterns in the data and the context around it. Although many solutions are available to create rich interactive graphs and charts, they are generally too separated from the analysis process itself. This creates inefficiencies at the boundary and makes rapid iteration of research more difficult. Minimizing these inefficiencies is the focus of this talk. By combining agile data analysis tools with web-based visualization libraries, we can optimize the data scientist’s tool chain for both exploratory research and the presentation of results.
A proper solution needs to take into account all of the components of data analysis that happens between raw data and the final visualization.
Chang She is a cofounder of Lambda Foundry. From 2011 to 2012, he served as Assistant Vice President at Barclays Capital researching quantitative FX strategies and building research infrastructure. From 2006-2011, he worked at AQR Capital Management in global equities research and algorithm execution. He graduated from MIT with an M.Eng in Computer Science and S.B. degrees in Computer Science and Political Science.
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