DocumentCode
233655
Title
Teaching Parallelism without Programming: A Data Science Curriculum for Non-CS Students
Author
Gil, Yolanda
Author_Institution
Inf. Sci. Inst., Univ. of Southern California, Marina del Rey, CA, USA
fYear
2014
fDate
16-16 Nov. 2014
Firstpage
42
Lastpage
48
Abstract
The goal of our work is to develop an open and modular course for data science and big data analytics that is accessible to non-programmers. The course is designed to cover major concepts that are useful to understand the benefits of parallel and distributed programming while not relying on a programming background. These key concepts focus more on algorithmic aspects rather than architecture and performance issues. A key aspect of our work is the use of workflows to illustrate key concepts and to allow the students to practice.
Keywords
Big Data; parallel programming; teaching; big data analytics; data science curriculum; distributed programming; nonCS students; parallel programming; Data analysis; Distributed databases; Parallel processing; Programming profession; Semantics; curriculum; teaching; data science; big data; workflows; semantic workflows; WINGS; parallelism;
fLanguage
English
Publisher
ieee
Conference_Titel
Education for High Performance Computing (EduHPC), 2014 Workshop on
Conference_Location
New Orleans, LA
Type
conf
DOI
10.1109/EduHPC.2014.12
Filename
7016357
Link To Document