Salt Lake City, Utah
June 23, 2018
June 23, 2018
July 27, 2018
This paper presents our three years’ experience in adapting and integrating big data concepts across the computer science undergraduate mathematics and statistics curriculum. Undergraduate computer science degree courses in mathematics and statistics teaches students the traditional logical and problem-solving skills; but that is only a small part of what is required of graduates when they enter the data science or big data analytics work force. Industry demands a much broader perspective: that of being equipped with technical skills in useful information retrieval from very large, complex, and unstructured data. Universities are waking up to the need for having dedicated big data analytics programs. But, most of the courses are on the graduate level. However, after careful analysis, it is identified that there is a lag in these courses providing channelized big data concepts training to the students, particularly to undergraduate computer science students. This paper serves to address this gap by providing an experience in infusing, teaching, and assessing big data modules in computer science undergraduate mathematics and statistics courses.
Pettis, C., & Swamidurai, R., & Abebe, A., & Shannon, D. (2018, June), Infusion of Big Data Concepts Across the Undergraduate Computer Science Mathematics and Statistics Curriculum Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. https://peer.asee.org/30662
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