Scientists and Engineers Statistical Data System: Data Overview

2007 ◽  
Author(s):  
Paula E. Stephan
2021 ◽  
pp. 251512742110292
Author(s):  
Briana Sell Stenard

This research seeks to understand which skills entrepreneurs in the different STEAM disciplines are using in their careers and if these skills differ from those being used by workers who major in STEAM fields but do not become entrepreneurs. The empirical analysis uses a large sample of more than 99,000 people from the restricted use National Science Foundation’s (NSF) Scientists and Engineers Statistical Data System (SESTAT). This work investigates the skills actually being used by entrepreneurs with undergraduate degrees in the STEAM disciplines to better inform what is being taught in the undergraduate classroom and to make sure learning objectives are in line with student career goals. This paper argues the need for more interdisciplinary skills to be taught in STEAM entrepreneurship curriculums.


Abjadia ◽  
2019 ◽  
Vol 4 (2) ◽  
pp. 97
Author(s):  
Waluyo Satrio Adji ◽  
Abdul Bashith ◽  
Ali Nasith ◽  
Saiful Amin

<p>Social problems in Indonesia in particular are caused by social phenomena transmitted to users of social media, especially Twitter. Big data system provided by Drone Emprit Academic is able to find social phenomena. The ability of critical literacy to read and write supported by statistical data is very important in the 4.0 era. The aim of the research is to find out which Drone Emprit Academic works, analyzes, and displays data on social phenomena whose results can be used to support critical Literacy. This research uses a qualitative approach, literature study method. The analysis includes three stages, namely organize, synthesize, identify. The results of this study that Drone Emprit Academic is a big data system that carries out social network analysis of specific conversations on Twitter in semi-realtime and detail. The form displayed is in the form of a percentage of trends, retweet relationships, mentioning trend graphs, most retweet statuses, conversation trends. The data generated can help read information about social phenomena so that it can support critical literacy which has been partially published in online and offline media.</p>


1983 ◽  
Vol 62 (7) ◽  
pp. 2209-2237
Author(s):  
C. J. Byrne ◽  
D. J. Gagne ◽  
J. A. Grandle ◽  
G. H. Wedemeyer

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