A Guide to Using Princeton’s Computing Clusters to Handle Big Data

As I mentioned in my last post, this summer, I assisted in research by Dr. Kalhor at the Princeton’s Center for Policy Research on Energy and the Environment (C-PREE) in examining the effect of anomalous weather on economic activity as part of an internship funded by the High Meadows Environmental Institute (HMEI) at Princeton University. While my previous post focused on my insights in preparing for internships to maximize your experience, in this post, I want to focus on one of the technical challenges that I faced during the internship: (a) handling big data; and (b) one of the powerful tools that we have as students at Princeton students to handle large amounts of data –  Princeton’s large computing clusters.

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Reflecting on my Summer Research Internship

As I mentioned in a previous post, this last summer, I assisted in the research by Dr. Elmira Kalhor at the Center for Policy Research on Energy and the Environment (C-PREE) in examining the effect of anomalous weather on economic activity, as part of an internship funded by the High Meadows Environmental Institute (HMEI) at Princeton University. Under Dr. Kalhor’s guidance, it became a fulfilling experience as I had the opportunity to formulate a model of how business activity is affected by extreme weather and thus apply many of the economic tools and theories that I learned to a practical space.

Our work focused on business effects of extreme weather, up to and including hurricanes like Harvey

Now that it has been about three weeks since I gave my final presentation of my work to HMEI, I wanted to take the opportunity to reflect on my time there. While the internship was very enjoyable, it also presented me with many challenges – technical ones related to the statistical analysis and the research itself and practical/logistical issues in terms of having a valuable and fulfilling internship. In this post, I hope to discuss some of the more practical issues and guidelines that you could use to help maximize the efficiency of your internship experience, especially if you are working on a research-intensive project.

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