Bridging the Gap Between Data and Insight using Open-Source Tools – Nicholas Arcolano ODSC Boston 2015 Bridging the Gap Between Data and Insight using Open-Source Tools – Nicholas Arcolano ODSC Boston 2015
Bridging the Gap Between Data and Insight using Open-Source Tools from odsc Despite the proliferation of open-source tools for analysis (such as Python and... Bridging the Gap Between Data and Insight using Open-Source Tools – Nicholas Arcolano ODSC Boston 2015

Bridging the Gap Between Data and Insight using Open-Source Tools from odsc

Despite the proliferation of open-source tools for analysis (such as Python and R) and those used for visualization
(such as Javascript / D3), there often exist significant gaps between these areas, and those of us trying to navigate the complete arc from data to insight can encounter many obstacles along the way. Fortunately, in recent years there have been many efforts to fill these needs, and today distilling a meaningful visualization from raw data is faster and easier than ever before.

In this talk we will use will use examples in geospatial analysis and visualization to illustrate how to open-source tools like Python, geopandas, and TileMill work together. Using examples from the RunKeeper mobile app we will show how we currently use these tools to understand better our customers and their data, and to communicate
with our colleagues, external partners, and the data community at large.

Presenter Bio
Nicholas Arcolano is a Senior Data Scientist at FitnessKeeper, makers of the RunKeeper and Breeze mobile and web apps for health and fitness tracking and guidance. Before joining FitnessKeeper he spent 10 years as a research staff member at MIT Lincoln Laboratory, working in the Cyber Security and Information Sciences and the Ballistic Missile Defense Divisions. While at Lincoln Laboratory he also received his Ph.D. in Applied Mathematics from Harvard University, where his research focused on numerical methods for the analysis of massive datasets. His areas of expertise and interest include statistical modeling, data mining, machine learning, data visualization, network science, and statistical signal processing.

Open Data Science

Open Data Science

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