Data Science Programs Research Articles

Data science can be an attractive career for physicists

Boot camps can help researchers transition into nonacademic jobs that use big data.

Data science programs in U.S. higher education: An exploratory content analysis of program description, curriculum structure, and course focus

In this study, an exploratory content analysis of 30 randomly selected Data Science (DS) programs from eight disciplines revealed significant gaps in current DS education in the United States. The analysis centers on linguistic patterns of program descriptions, curriculum requirements, and DS cours e focus as pertaining to key skills and domain knowledge. The results show that a range of unique terms was used in individual program descriptions, with common terms being shared across disciplines. DS programs required varying numbers of credit hours, including practicum and capstone. Most DS courses covered the basic level of analytical skills, but upper-level skills were inadequately addressed. Programs in eight disciplines delivered information skills through their core courses, and four addressed communication skills. Six disciplines covered visualization skills through their core courses, yet just three in elective courses. The course offering on mathematics/statistics was rather weak in iSchools. While core courses in iSchools provided communication and visualization skills, their electives courses did not address such skills. These findings have implications for improving DS education in iSchools and across other disciplines.

데이터 사이언스 교과과정에 대한 연구

본 연구는 국내외 데이터 사이언티스트(Data Scientist) 양성을 위한 데이터 사이언스(Data Science) 프로그램의 교과과정을 분석하였다. 이를 위해 국내 7개 대학교와 미국의 10개 대학교를 분석하였다. 14개의 데이터 사이언스 과정이 대학원 중심으로 운영되고 있는 것으로 나타났다. Conway의 데이터 사이언스 3대 영역 중 수학 및 통계 지식 영역에 국내는 10% 그리고 미국은 26%가 치중되어 있는 것으로 분석되었다. 강의계획서 분석에서 수업내용 및 평가 방법은 국내외가 유사한 것으로 나타났다. 본 연구 결과는 국내 데이터 사이언스 교과과정 개발에 기초 자료로 활용될 수 있을 것이다. The purpose of this study is to compare seven data science programs in Korea and ten data science programs in the US. Results show that 14 data science programs are housed in graduate schools. 10% of data science courses in Korea and 26% in the US fall under the Math and Statistics Knowledge area, one of the three areas defined by Conway. The syllabus analysis does not show much differences in terms of class contents and grading. The results of this study can be used to design data science programs that are more effective and well-grounded.

A Reply to Hosack and Sagers “Applied Doctorates in IT: A Case for Designing Data Science Graduate Programs"

This article presents a response to the argument presented by Hosack and Sagers (2015) in regards to the need and value for applied doctoral programs in Data Science. This response analyzes this proposition critically from the viewpoints of scope, doctoral program model, and program delivery.

Data Science and its Relationship to Big Data and Data-Driven Decision Making.

Companies have realized they need to hire data scientists, academic institutions are scrambling to put together data-science programs, and publications are touting data science as a hot-even "sexy"-career choice. However, there is confusion about what exactly data science is, and this confusion could lead to disillusionment as the concept diffuses into meaningless buzz. In this article, we argue that there are good reasons why it has been hard to pin down exactly what is data science. One reason is that data science is intricately intertwined with other important concepts also of growing importance, such as big data and data-driven decision making. Another reason is the natural tendency to associate what a practitioner does with the definition of the practitioner's field; this can result in overlooking the fundamentals of the field. We believe that trying to define the boundaries of data science precisely is not of the utmost importance. We can debate the boundaries of the field in an academic setting, but in order for data science to serve business effectively, it is important (i) to understand its relationships to other important related concepts, and (ii) to begin to identify the fundamental principles underlying data science. Once we embrace (ii), we can much better understand and explain exactly what data science has to offer. Furthermore, only once we embrace (ii) should we be comfortable calling it data science. In this article, we present a perspective that addresses all these concepts. We close by offering, as examples, a partial list of fundamental principles underlying data science.

The Berkeley Infrared Spatial Interferometer: A Heterodyne Stellar Interferometer for the Mid‐Infrared

A detailed description is given of the Infrared Spatial Interferometer (ISI), developed at the Space Sciences Laboratory of the University of California at Berkeley, which is a high spatial resolution interferometer for mid-infrared wavelengths. The instrumentation, its capabilities and performance, data analysis, science program, and future plans are all discussed. The system's use of heterodyne detection, analogous to that of a modern radio interferometer, is also compared with the homodyne or direct methods more commonly encountered in the visible and infrared. The ISI has been operating productively on Mount Wilson for the past 10 years measuring materials immediately surrounding stars and their changes as well as some stellar diameters. The new spectral capabilities described here, a recent increase in baseline length, and the upcoming expansion to a closure-phase imaging array provide important additional types of measurements.