Teaching Parallelism Without Programming: A Data Science Curriculum for Non-CS Students

被引：11
作者：
Gil, Yolanda [1 ]
机构：
[1] Univ Southern Calif, Inst Informat Sci, 4676 Admiralty Way, Marina Del Rey, CA 90292 USA
来源：
2014 WORKSHOP ON EDUCATION FOR HIGH PERFORMANCE COMPUTING (EDUHPC) | 2014年
关键词：
curriculum; teaching; data science; big data; workflows; semantic workflows; WINGS; parallelism;
D O I：
10.1109/EduHPC.2014.12
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
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.
引用
收藏
页码：42 / 48
页数：7
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