








a. 
One course from each of the following groups (8 or 10 credits): 


(1) 
CEM 
141 
General Chemistry 
4 


CEM 
151 
General and Descriptive Chemistry 
4 


CEM 
181H 
Honors Chemistry I 
4 


LB 
171 
Principles of Chemistry I 
4 

(2) 
CEM 
142 
General and Inorganic Chemistry 
3 


CEM 
152 
Principles of Chemistry 
3 


CEM 
182H 
Honors Chemistry II 
4 


LB 
172 
Principles of Chemistry II 
3 

(3) 
CEM 
161 
Chemistry Laboratory I 
1 


CEM 
185H 
Honors Chemistry Laboratory I 
2 


LB 
171L 
Introductory Chemistry Laboratory I 
1 
b. 
One course from each of the following groups (8 to 10 credits): 


(1) 
LB 
273 
Physics I 
4 


PHY 
173 
Studio Physics for Scientists and Engineers I 
5 


PHY 
183 
Physics for Scientists and Engineers I 
4 

(2) 
LB 
274 
Physics II 
4 


PHY 
174 
Studio Physics for Scientists and Engineers II 
5 


PHY 
184 
Physics for Scientists and Engineers II 
4 
c. 
One course from each of the following groups (14 or 15 credits): 


(1) 
LB 
118 
Calculus I 
4 


MTH 
132 
Calculus I 
3 


MTH 
152H 
Honors Calculus I 
3 

(2) 
LB 
119 
Calculus II 
4 


MTH 
133 
Calculus II 
4 


MTH 
153H 
Honors Calculus II 
4 

(3) 
LB 
220 
Calculus III 
4 


MTH 
234 
Multivariable Calculus 
4 


MTH 
254H 
Honors Multivariable Calculus 
4 

(4) 
MTH 
314 
Matrix Algebra with Computational Applications 
3 
d. 
One of the following groups (4 or 6 credits): 


(1) 
STT 
380 
Probability and Statistics for Data Science 
4 

(2) 
STT 
441 
Probability and Statistics I: Probability 
3 


STT 
442 
Probability and Statistics I: Statistics 
3 
e. 
All of the following courses (31 credits): 


CMSE 
201 
Introduction to Computational Modeling and Data Analysis 
4 

CMSE 
202 
Computational Modeling Tools and Techniques 
4 

CMSE 
381 
Fundamentals of Data Science Methods 
4 

CMSE 
382 
Optimization Methods in Data Science 
4 

CMSE 
495 
Experiential Learning in Data Science 
4 

CSE 
232 
Introduction to Programming II 
4 

CSE 
331 
Algorithms and Data Structures 
3 

STT 
180 
Introduction to Data Science 
4 
f. 
A minimum of 12 credits of approved 400level courses or above. The following courses are eligible to fulfill this requirement. Other may be substituted with advisor approval. 


CMSE 
401 
Methods for Parallel Computing 
4 

CMSE 
402 
Data Visualization Principles and Techniques 
3 

CMSE 
410 
Computational Biology and Bioinformatics 
3 

CMSE 
411 
Computational Medicine 
3 

CMSE 
492 
Special Topics in Data Science 
1 to 4 

CSE 
402 
Biometrics and Pattern Recognition 
3 

CSE 
404 
Introduction to Machine Learning 
3 

CSE 
440 
Introduction to Artificial Intelligence 
3 

CSE 
480 
Database Systems 
3 

CSE 
482 
Big Data Analysis 
3 

MTH 
468 
Predictive Analytics 
3 

STT 
464 
Statistics for Biologists 
3 

STT 
465 
Bayesian Statistical Methods 
3 

A maximum of 12 credits may count towards the degree for enrollments in CMSE 492 with advisor approval. 