Generative AI for Data Science 101: Coding Without Learning To Code

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   Stem-ming the Tide: Predicting STEM attrition using student transcript data

   Lovenoor Aulck, Rohan Aras, Lysia Li et al. · 2017 · Autres statistiques · Résumé IA disponible

   Résumé scientifique

   Science, technology, engineering, and math (STEM) fields play growing roles in national and international economies by driving innovation and generating high salary jobs. Yet, the US is lagging behind other highly industrialized nations in terms of STEM education and training. Furthermore, many economic forecasts predict a rising shortage of domestic STEM-trained professions in the US for years to come. One potential solution to this deficit is to decrease the rates at which students leave STEM-related fields in higher education, as currently over half of all students intending to graduate with a STEM degree eventually attrite. However, little quantitative research at scale has looked at cau…

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   How to survive the Squid Games using probability theory

   Elena Moltchanova, Miguel Moyers-González, Geertrui Van de Voorde et al. · 2024 · Autres statistiques · Pas encore résumé

   Résumé scientifique

   In this paper, we consider how probability theory can be used to determine the survival strategy in two of the ``Squid Game" and ``Squid Game: The Challenge" challenges: the Hopscotch and the Warships. We show how Hopscotch can be easily tackled with the knowledge of the binomial distribution, taught in introductory statistics courses, while Warships is a much more complex problem, which can be tackled at different levels.

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   Determining feature importance for actionable climate change mitigation policies

   Romit Maulik, Junghwa Choi, Wesley Wehde et al. · 2020 · Autres statistiques · Résumé IA disponible

   Résumé scientifique

   Given the importance of public support for policy change and implementation, public policymakers and researchers have attempted to understand the factors associated with this support for climate change mitigation policy. In this article, we compare the feasibility of using different supervised learning methods for regression using a novel socio-economic data set which measures public support for potential climate change mitigation policies. Following this model selection, we utilize gradient boosting regression, a well-known technique in the machine learning community, but relatively uncommon in public policy and public opinion research, and seek to understand what factors among the several …

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   Synthesis of High-Resolution Load Profiles with Minimal Data

   Thomas Schnake, David Bauer · 2019 · Autres statistiques · Résumé IA disponible

   Résumé scientifique

   For the estimation of a new energy supply system it is an important to have high-resolution energy load profile. Such a profile is in general either not present or very costly to obtain. We will therefore present a method which synthesizes load profiles from minimal given data, but with maximal resolution. The general initial data setting includes month integrals and load profiles a few days. The resulting time series features all important properties to represent a real energy profile.