Psychiatrist and Data Scientist Uses ‘Big Data’ in Service of Minority Populations

2022 ◽  
Vol 57 (01) ◽  
Author(s):  
Mark Moran
Author(s):  
Dominik Krimpmann ◽  
Anna Stühmeier

Big Data and Analytics have become key concepts within the corporate world, both commercially and from an information technology (IT) perspective. This paper presents the results of a global quantitative analysis of 400 IT leaders from different industries, which examined their attitudes toward dedicated roles for an Information Architect and a Data Scientist. The results illustrate the importance of these roles at the intersection of business and technology. They also show that to build sustainable and quantifiable business results and define an organization's competitive positioning, both roles need to be dedicated, rather than shared across different people. The research also showed that those dedicated roles contribute actively to a sustainable competitive positioning mainly driven by visualization of complex matters.


2021 ◽  
Vol 9 ◽  
Author(s):  
Andrea Rau

Data collected in very large quantities are called big data, and big data has changed the way we think about and answer questions in many different fields, like weather forecasting and biology. With all this information available, we need computers to help us store, process, analyze, and understand it. Data science combines tools from fields like statistics, mathematics, and computer science to find interesting patterns in big data. Data scientists write step-by-step instructions called algorithms to teach computers how to learn from data. To help computers understand these instructions, algorithms must be translated from the original question asked by a data scientist into a programming language—and the results must be translated back, so that humans can understand them. That means that data scientists are data detectives, programmers, and translators all in one!


2019 ◽  
Vol 26 (5) ◽  
pp. 383-391 ◽  
Author(s):  
Melanie A Meyer

Abstract Objective Growth in big data and its potential impact on the healthcare industry have driven the need for more data scientists. In health care, big data can be used to improve care quality, increase efficiency, lower costs, and drive innovation. Given the importance of data scientists to U.S. healthcare organizations, I examine the qualifications and skills these organizations require for data scientist positions and the specific focus of their work. Materials and Methods A content analysis of U.S. healthcare data scientist job postings was conducted using an inductive approach to capture and categorize core information about each posting and a deductive approach to evaluate skills required. Profiles were generated for 4 job focus areas. Results There is a spectrum of healthcare data scientist positions that varies based on hiring organization type, job level, and job focus area. The focus of these positions ranged from performance improvement to innovation and product development with some positions more broadly defined to address organizational-specific needs. Based on the job posting sample, the primary skills these organizations required were statistics, R, machine learning, storytelling, and Python. Conclusions These results may be useful to organizations as they deepen our understanding of the qualifications and skills required for data scientist positions and may aid organizations in identifying skills and knowledge areas that have been overlooked in position postings.


Big Data ◽  
2016 ◽  
pp. 1-29 ◽  
Author(s):  
Yushi Shen ◽  
Yale Li ◽  
Ling Wu ◽  
Shaofeng Liu ◽  
Qian Wen

This chapter provides an overview of big data and its environment and opportunities. It starts with a definition of big data and describes the unique characteristics, structure, and value of big data, and the business drivers for big data analytics. It defines the role of the data scientist and describes the new ecosystem for big data processing and analysis.


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