College:
College of Natural Science
Relevant Excerpt(s) from Academic Programs:
Department:
Computational Math, Science and Engineering CNS
Excerpt from the official Academic Programs Catalog:
Listed below are the approved requirements for the program from the official Academic Programs Catalog.
Students must consult their advisors to learn which specific requirements apply to their degree programs.
College of Natural Science
Department of Computational Mathematics, Science and Engineering
Undergraduate Programs
Data Science
The Bachelor of Science degree in Data Science is designed to provide students with a strong background in data science using a broad range of computational techniques, practice in statistical thinking, as well as in-depth exposure to topics in data science.
Requirements for the Bachelor of Science Degree in Data Science
- The University requirements for bachelor's degrees as described in the Undergraduate Education section of this catalog; 120 credits, including general elective credits, are required for the Bachelor of Science degree in Data Science.
The University's Tier II writing requirement for the Data Science major is met by completing Computational Mathematics, Science and Engineering 495, referenced in item 3. below.
- The requirements of the College of Natural Science for the Bachelor of Science degree.
- The following requirements for the major:
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a. |
One course from each of the following groups (8 or 10 credits): |
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(1) |
CEM |
141 |
General Chemistry |
4 |
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CEM |
151 |
General and Descriptive Chemistry |
4 |
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CEM |
181H |
Honors Chemistry I |
4 |
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LB |
171 |
Principles of Chemistry I |
4 |
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(2) |
CEM |
142 |
General and Inorganic Chemistry |
3 |
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CEM |
152 |
Principles of Chemistry |
3 |
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CEM |
182H |
Honors Chemistry II |
4 |
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LB |
172 |
Principles of Chemistry II |
3 |
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(3) |
CEM |
161 |
Chemistry Laboratory I |
1 |
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CEM |
185H |
Honors Chemistry Laboratory I |
2 |
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LB |
171L |
Introductory Chemistry Laboratory I |
1 |
b. |
One course from each of the following groups (8 to 10 credits): |
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(1) |
LB |
273 |
Physics I |
4 |
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PHY |
173 |
Studio Physics for Scientists and Engineers I |
5 |
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PHY |
183 |
Physics for Scientists and Engineers I |
4 |
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(2) |
LB |
274 |
Physics II |
4 |
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PHY |
174 |
Studio Physics for Scientists and Engineers II |
5 |
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PHY |
184 |
Physics for Scientists and Engineers II |
4 |
c. |
One course from each of the following groups (14 or 15 credits): |
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(1) |
LB |
118 |
Calculus I |
4 |
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MTH |
132 |
Calculus I |
3 |
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MTH |
152H |
Honors Calculus I |
3 |
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(2) |
LB |
119 |
Calculus II |
4 |
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MTH |
133 |
Calculus II |
4 |
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MTH |
153H |
Honors Calculus II |
4 |
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(3) |
LB |
220 |
Calculus III |
4 |
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MTH |
234 |
Multivariable Calculus |
4 |
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MTH |
254H |
Honors Multivariable Calculus |
4 |
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(4) |
MTH |
314 |
Matrix Algebra with Computational Applications |
3 |
d. |
One of the following groups (4 or 6 credits): |
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(1) |
STT |
380 |
Probability and Statistics for Data Science |
4 |
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(2) |
STT |
441 |
Probability and Statistics I: Probability |
3 |
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STT |
442 |
Probability and Statistics I: Statistics |
3 |
e. |
All of the following courses (31 credits): |
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CMSE |
201 |
Introduction to Computational Modeling and Data Analysis |
4 |
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CMSE |
202 |
Computational Modeling Tools and Techniques |
4 |
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CMSE |
381 |
Fundamentals of Data Science Methods |
4 |
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CMSE |
382 |
Optimization Methods in Data Science |
4 |
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CMSE |
495 |
Experiential Learning in Data Science |
4 |
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CSE |
232 |
Introduction to Programming II |
4 |
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CSE |
331 |
Algorithms and Data Structures |
3 |
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STT |
180 |
Introduction to Data Science |
4 |
f. |
A minimum of 12 credits of approved 400-level courses or above. The following courses are eligible to fulfill this requirement. Other may be substituted with advisor approval. |
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CMSE |
401 |
Methods for Parallel Computing |
4 |
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CMSE |
402 |
Data Visualization Principles and Techniques |
3 |
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CMSE |
410 |
Computational Biology and Bioinformatics |
3 |
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CMSE |
411 |
Computational Medicine |
3 |
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CMSE |
492 |
Special Topics in Data Science |
1 to 4 |
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CSE |
402 |
Biometrics and Pattern Recognition |
3 |
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CSE |
404 |
Introduction to Machine Learning |
3 |
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CSE |
440 |
Introduction to Artificial Intelligence |
3 |
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CSE |
480 |
Database Systems |
3 |
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CSE |
482 |
Big Data Analysis |
3 |
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MTH |
468 |
Predictive Analytics |
3 |
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STT |
464 |
Statistics for Biologists |
3 |
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STT |
465 |
Bayesian Statistical Methods |
3 |
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A maximum of 12 credits may count towards the degree for enrollments in CMSE 492 with advisor approval. |