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Applying Advanced Statistical Techniques to Real-World Problems: Two Cases

Student: Artem Volgin

Supervisor: Valentina Kuskova

Faculty: International laboratory for Applied Network Research

Educational Programme: Applied Statistics with Network Analysis (Master)

Year of Graduation: 2020

Data Science competitions are one of the popular ways for beginners to become familiar with statistical terminology, obtain practical coding skills, and acquire experience using modern machine learning frameworks. Kaggle – a subsidiary of Google – is the most popular organizer of such competitions with more than 1 million registered users from 194 countries. In this dissertation, we present our submissions for the two analytical Kaggle competitions. The first competition – “2019 Kaggle ML & DS Survey” – was held in November 2019; the task was to tell a data-story, exploiting a given survey dataset. We focused on Kaggle users with a Ph.D. degree as our research object and described this community, using social network analysis techniques. The second competition – “DS4G: Environmental Insights Explorer” – was held in February of 2020; the task was to create a novel methodology for the emission factor calculations for power plants, using the remote sensing data. With the help of the Spatial Panel Data Modeling, we were able to obtain the average and marginal emission factors for Puerto-Rico and suggested a fruitful avenue for further improvements of this methodology.

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