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STS515 Jeremiah D. D. et al.
2) Data Structures: COMS 3134, COMS 3136, or COMS 3137
3) Discrete Math: COMS 3203
4) Analysis of Algorithms: CSOR
Electives: 5 Courses
STAT: 2 from the following
1) STAT 3106 (Applied Data Mining)
2) STAT 4206 (Statistical Computing and Introduction to Data Science)
3) STAT 4243 (Applied Data Science)
4) STAT 4224 (Bayesian Statistics)
5) STAT 4242 (Advanced Machine Learning)
COMS: 3 from the following
1) COMS 3261 (Computer Science Theory)
2) COMS 4111 (Introduction to Databases)
3) COMS 4130 (Principles and Practice of Parallel Programming)
4) COMS 4236 (Introduction to Computational Complexity)
5) COMS 4252 (Introduction to Computational Learning Theory)
6) Any COMS W47XX course except COMS 4771 (These are our AI/ML
oriented courses.)
By matching and evaluating the course outlines we gathered from the
Internet, we first score the subject intensities as represented in various DS
progarmmes with a range from 1 (only rudimentary) to 3 (very strong presence
and coverage). In Columbia’s case, the scores are: Mathematics (3: calculus,
algebra, discrete mathematics), Statistics (3: probability, inference, regression,
machine learning), Programming (2: data structure, algorithms), Computing (1:
introduction to CS). Clearly, the score on Computing could be improved if
other CS subjects are better covered such as database and cloud computing.
This shows that there might be some pedagogical or logistics factors that have
contributed to this particular curriculum design.
The DS profiles each with four scores are ready to be contrasted.
The following DS profiles are included for comparison in this paper:
- Columbia University, BA
- Michigan University, BEng
- Rochester University, BA/BSc
- Kansas State University, BA/BSc
- Maryville Colege, BA with BusAdmin minor
- “ChineseU”, a university in mainland China 3
- Auckland University, BSc
- Canterbury University, BEng
As the interim course information was obtained through private communications only, the
3
university hence is made anonymous.
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