Page 78 - Special Topic Session (STS) - Volume 3

STS515 Jeremiah D. D. et al.
debated in daily conversations, and in scientific discourses. Dhar (2013)
pointed out that, as a multi-disciplinary subject, Data Science (DS) is distinct
from statistics, referring to the growing needs of processing data that are
increasingly heterogeneous and unstructured. There is an emerging
consensus to see DS as an interdiscplinary field that incorporates
mathematics/statistics, computer science and information science, and
domain knowledge (WSU, 2016). Consequently, we consider it an interesting
question to ask: is there an ideal design of DS undergraduate curricula?

2. Methodology
Profiling DS Programmes
In this paper, we will examine a few typical DS curricula as adopted by some
institutions in the US, China, and New Zealand. The diversity of these DS
programmes is demonstrated in the different types of insitutes (liberal-arts
colleges, business schools, and universities) and different degress (BA, BSc,
and BEng). To profile the curriculum designs from these programmes, we use
a hybrid quantification approach to score their requirements on four
dimensions, and we use visualization to indicate the differences: mathematics,
statistics, programming, and computing. We concentrate on the prerequisite
and core levels and leave the electives out to gain some understanding of the
core structure of these programmes.
Using University of Columbia as an example for illustration, we take a look of
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its undergraduate DS programme design as shown in Table 1 :

Table 1. Programme structure of the DS major at Columbia

Prerequisites: 15 points
• Calculus I – III
• Linear Algebra (Math or Applied Math)
• STAT 1201 (Calculus-Based Introduction to Statistics)
Core: 8 courses (STAT and COMS)
STAT (12 points):
1) STAT 4203 (Probability Theory)
2) STAT 4204 (Statistical Inference)
3) STAT 4205 (Linear Regression Models)
4) STAT 4241 (Statistical Machine Learning) or COMS 4771 (Machine
Learning) COM (12 points)
1) Introduction to Computer Science: COMS 1004, COMS 1005, ENGI 1006,
or COMS 1007

Columbia University, URL https://mice.cs.columbia.edu/c/d.php?d=245, August 8, 2018.
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Retrieved April 29, 2019
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