Infusing Big Data Concepts in Undergraduate CS Mathematics Courses Through Active Learning

Industry demands workers that can retrieve useful information from very complex, unstructured data. While undergraduate computer science degree courses in mathematics and statistics extensively focus on the traditional logical and problem-solving skills, they fall short of providing adequate training to students in big data concepts that integrate theory and computation. More universities are becoming aware of the need to have dedicated big data analytics programs. Most of the courses are offered at the graduate level though. After careful analysis, it is has become evident that there is a gap in the curriculum as it relates to training for big data concepts in foundational courses for students. To be more specific, there is relatively little focus on the infusion of big data concepts in mathematics and statistics courses and its impact on classroom practices. This is definitely the case in many undergraduate computer science curriculums. This paper serves to address this gap by providing an experience in infusing, teaching, and assessing big data modules in various computer science undergraduate mathematics and statistics courses that immerse students in real-world big data practices through active learning.