The Bachelor of Science degree in Computational Data Science focuses on the computational foundations of data science, providing an in-depth understanding of the algorithms and data structures for storing, manipulating, visualizing, and learning from large data sets. Students in the program have unique access to a wide range of fundamental computer science courses in topics ranging from mobile application and web development to theory of computation and fundamental algorithms. Students can tailor their degree to their unique interests and requirements, with an emphasis on computational foundations.
Requirements for the Bachelor of Science Degree in Computational 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 Computational Data Science.
The University's Tier II writing requirement for the Computational Data Science major is met by completing Computational Mathematics, Science and Engineering 495, referenced in item 3. b. below.
Students who are enrolled in the College of Engineering may complete the alternative track to Integrative Studies in Biological and Physical Sciences that is described in item 1. under the heading Graduation Requirements for All Majors in the College statement.
- The requirements of the College of Engineering for the Bachelor of Science degree.
The credits earned in certain courses referenced in requirement 3. below may be counted toward College requirements as appropriate.
- The following requirements for the major:
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| a. |
Bioscience (4 to 6 credits) |
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(1) |
One of the following courses: |
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BS |
161 |
Cell and Molecular Biology |
3 |
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ENT |
205 |
Pests, Society and Environment |
3 |
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IBIO |
150 |
Integrating Biology: From DNA to Populations |
3 |
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MGI |
141 |
Introductory Human Genetics |
3 |
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MGI |
201 |
Fundamentals of Microbiology |
3 |
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PLB |
105 |
Plant Biology |
3 |
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PSL |
250 |
Introductory Physiology |
4 |
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(2) |
One of the following courses: |
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BS |
171 |
Cell and Molecular Biology Laboratory |
2 |
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CEM |
161 |
Chemistry Laboratory I |
1 |
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CEM |
162 |
Chemistry Laboratory II |
1 |
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PHY |
191 |
Physics Laboratory for Scientists, I |
1 |
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PHY |
192 |
Physics Laboratory for Scientists, II |
1 |
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PLB |
106 |
Plant Biology Laboratory |
1 |
| b. |
All of the following courses (45 credits): |
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CMSE |
201 |
Computational Modeling and Data Analysis I |
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 (W) |
4 |
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CSE |
232 |
Introduction to Programming II |
4 |
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CSE |
300 |
Social, Ethical, and Professional Issues in Computing |
1 |
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CSE |
331 |
Algorithms and Data Structures |
3 |
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CSE |
380 |
Data Management and the Cloud |
4 |
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CSE |
404 |
Introduction to Machine Learning |
3 |
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CSE |
482 |
Big Data Analysis |
3 |
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MTH |
314 |
Matrix Algebra with Computational Applications |
3 |
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STT |
180 |
Introduction to Data Science |
4 |
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STT |
380 |
Probability and Statistics for Data Science |
4 |
| c. |
Five courses selected from the following (15 to 17 credits): |
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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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CSE |
335 |
Software Engineering I |
4 |
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CSE |
402 |
Biometrics and Pattern Recognition |
3 |
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CSE |
415 |
Introduction to Parallel Computing |
3 |
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CSE |
431 |
Algorithm Engineering |
3 |
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CSE |
434 |
Autonomous Vehicles |
3 |
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CSE |
440 |
Artificial Intelligence |
3 |
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CSE |
446 |
AI Agents |
3 |
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CSE |
471 |
Media Processing and Multimedia Computing |
3 |
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CSE |
472 |
Computer Graphics |
3 |
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CSE |
475 |
Computational Human-Computer Interaction |
3 |
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CSE |
480 |
Database Systems |
3 |
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CSE |
492 |
Selected Topics in Data Science |
3 |
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MTH |
451 |
Numerical Analysis I |
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 |
Concentration in Software EngineeringThe department offers the following concentration to students wanting an area of specialization in their degree. The concentration is available to, but not required of, any student enrolled in the Bachelor of Science degree program in Computational Data Science. NOTE: Completing the Bachelor of Science degree in Computational Data Science with a concentration may require more than 120 credits. Upon completion of the required courses for a concentration, certification will appear on the student’s official transcript.
Software Engineering To complete a Bachelor of Science degree in Computational Data Science with a software engineering concentration, students must complete the requirements for the bachelor’s degree, including the following:
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| The following courses (8 credits): |
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| CSE |
335 |
Software Engineering I |
4 |
| CSE |
336 |
Software Engineering II |
4 |