Major in Data Science
To earn a Major or a Double Major in Data Science (DATA), a student must complete the following:
| Credit requirements for Double Major | Credit requirements for Major |
3-year option | 48 credits (44 required + 4 DATA or cross-listed electives) | 60 credits (44 required + 16 DATA or cross-listed electives) |
4-year option | Capstone option: 64 credits (44 required + 8 DATA or cross-listed electives +12 capstone) Non-Capstone option: 64 credits (44 required + 20 DATA or cross-listed electives) | Capstone option: 80 credits (44 required + 24 DATA or cross-listed electives + 12 capstone) Non-Capstone option: 80 credits (44 required + 36 DATA or cross-listed electives) |
Note: that the Capstone option is available only with a four-year degree.
Minor in Data Science
A student wanting to minor in data science should gain 32 credits from pursuing Data Science courses and cross-listed electives. Out of the 32 credits, 28 credits should come from Data Science required courses (see below) and the remaining 4 credits should be gained from pursuing a DATA or cross-listed elective. For a 3-year exit option, a minor in data science requires 24 credits for graduation.
The following courses are required for a student to graduate with a Minor in Data Science:
| Required courses |
3- year option | Python for Data Science, Mathematical Foundations of Data Science, Data Structures and Algorithms, Applied Statistics, Introduction to Statistical Learning, and Machine Learning |
4-year option | Python for Data Science, Mathematical Foundations of Data Science, Introduction to Linear Algebra, Data Structures and Algorithms, Applied Statistics, Introduction to Statistical Learning, and Machine Learning |
Concentration in Data Science
A student wanting to pursue a Concentration in Data Science should gain 16 credits from pursuing Data Science courses listed below:
Python for Data Science, Mathematical Foundations of Data Science, Data Structures and Algorithms, Applied Statistics
Required Courses
The required courses in this program can be broadly categorised as Core: Basic, Foundations, and Over-arching
Core: Basic
Introduction to Data Science |
Python for Data Science |
Core: Foundations
Data Analytics Foundations |
Computational Foundations |
Mathematical Foundations |
Applied Statistics |
Data Structures and Algorithms |
Mathematical Foundations of Data Science |
Statistical Learning |
Data Management |
Introduction to Linear Algebra |
Machine Learning |
|
Introduction to Calculus |
Core: Over-arching
Elective Courses
Currently Available (This list will be expanded) |
Optimization for Data Science |
Natural Language Processing |
Big Data Analytics |
Computer Vision |
History of Data Science |
Sports Data Science |
Game Theory |
Econometrics |
Simulation and Modeling |
High Performance Computing |
Data Visualization |