Preparing for the Utilization of Data Science / Data Analytics in Earth Science Research

This is a Preprint and has not been peer reviewed.

This Preprint has no visible version.

Download Preprint
Downloads

Download Preprint

Supplementary Files
Authors

Steven Kempler, Lindsay Barbieri, Tiffany Trapasso

Abstract

Data analytics is the process of examining large amounts of varying data types to uncover hidden patterns, unknown correlations and other useful information. The Earth Science Information Partners (ESIP) Earth Science Data Analytics (ESDA) Cluster was created to facilitate the co-analysis of Earth science data and information. In addition to pioneering the definition of ESDA, the cluster has analyzed and compared data science/data analytics (DS/DA) university curricula with DS/DA techniques and skills necessary to fill the growing demand for DS/DA professionals. Here we identify the techniques utilized and skills needed to perform Earth science DS/DA, and survey University level DS/DA curricula currently available to prepare students for careers in Earth science DS/DA. Earth science DS/DA techniques and skills were identified through community discussions and presentations (including ESIP ESDA Cluster, literature search, discussions with scientists, and perspectives from students). The curricula of 267 DS/DA degree programs from 167 universities were analyzed to determine the DS/DA courses most often taught. Once compiled, this information was then used to identify, analyze, and report on gaps between the professional needs and the academic offerings. This study provides guidelines for both prospective students of Earth science DS/DA in pursuit of their professional career and teachers of Earth science DS/DA. It also serves as a starting point to examine this rapidly changing academic landscape and extrapolate from for future studies. Ultimately, we hope that the results of this study will enable a more holistic consideration of the development of Earth science data science and analytics curricula.

DOI

https://doi.org/10.31223/osf.io/c973s

Subjects

Computational Engineering, Computer Sciences, Databases and Information Systems, Education, Engineering, Mathematics, Numerical Analysis and Scientific Computing, Physical Sciences and Mathematics, Science and Mathematics Education, Statistics and Probability

Keywords

earth science, statistics, Knowledge, Computer Science, data science, education, analytics, information, information technology, mathematics

Dates

Published: 2018-06-21 20:01

Older Versions
License

CC BY Attribution 4.0 International

Add a Comment

You must log in to post a comment.


Comments

There are no comments or no comments have been made public for this article.