B.S Curriculum and Degree Requirements
Computing Option (120 Credits)
Fall Semester (14 Credits)
CS 100 | Roadmap to Computing | 3 |
Math 111 | Calculus 1 | 4 |
PHYS 111 | Physics I | 3 |
PHYS 111 A | Physics I Lab | 1 |
Hum 101 | English Composition | 3 |
Freshman Seminar | Freshman Seminar | 0 |
Spring Semester (14 Credits)
CS 113 | Intro to Computer Science I | 3 |
Math 112 | Calculus II | 4 |
PHYS 121 | Physics II | 3 |
PHYS 121 A | Physics II Lab | 1 |
Hum 102 | English Composition | 3 |
Fall Semester (15 Credits)
CS 114 | Intro to Computer Science II | 3 |
Math 244 | Introduction to Probability | 3 |
Math 337 | Linear Algebra | 3 |
GER | Humanities/History (200 level) | 3 |
GER | Social Science Literacy GER | 3 |
Spring Semester (16 Credits)
CS 241 | Foundations of Computer Science I | 3 |
CS 280 | Programming Language Concepts | 3 |
IS 350 | Computers, Society, and Ethics | 3 |
Math 341 | Statistical Methods | 3 |
YWCC 207 | Computing & Effective Communication | 1 |
Data Science | Elective 1 | 3 |
Fall Semester (15 Credits)
CS 288 | Intensive Programming in Linux | 3 |
CS 301 | Introduction to Data Science | 3 |
CS 331 | Database System Design & Management | 3 |
CS 370 | Intro to Artificial Intelligence | 3 |
ENG 340 or ENG 352 | Oral Presentation or Technical Writing | 3 |
Spring Semester (16 Credits)
CS 435 | Advanced Data Structures and Algorithm Design | 3 |
Data Science | Elective 2 | 3 |
CS 482 | Data Mining | 3 |
CS 375 | Machine Learning | 3 |
GER | History and Humanities (300+ level) | 3 |
YWCC 307 | Professional Development in Computing | 1 |
Fall Semester (15 Credits)
CS 450 | Data Visualization | 3 |
CS 444 | Big Data Systems | 3 |
CS 492 | Data Science Capstone 1 | 3 |
MATH 478 | Stats Methods in Data Science | 3 |
Data Science | Elective 3 | 3 |
Spring Semester (15 Credits)
GER | Humanities/Social Science Senior Seminar | 3 |
Free Elective | Free Elective 1 | 3 |
CS 493 | Data Science Capstone 2 | 3 |
Math 344 | Regression Analysis | 3 |
Data Science | Elective 4 | 3 |
Statistics Option (120 Credits)
Fall Semester (14 Credits)
CS 100 | Roadmap to Computing | 3 |
Math 111 | Calculus 1 | 4 |
PHYS 111 | Physics I | 3 |
PHYS 111 A | Physics I Lab | 1 |
Hum 101 | English Composition | 3 |
Freshman Seminar | Freshman Seminar | 0 |
Spring Semester (14 Credits)
CS 113 | Intro to Computer Science I | 3 |
Math 112 | Calculus II | 4 |
PHYS 111 | Physics I | 3 |
PHYS 111 A | Physics I Lab | 1 |
Hum 102 | English Composition | 3 |
Fall Semester (15 Credits)
CS 114 | Intro to Computer Science II | 3 |
Math 244 | Introduction to Probability | 3 |
Math 337 | Linear Algebra | 3 |
GER | Humanities/History (200 level) | 3 |
GER | Social Science Literacy | 3 |
Spring Semester (16 Credits)
CS 241 | Foundations of Computer Science I | 3 |
CS 280 | Programming Language Concepts | 3 |
Math 213 | Calculus IIIB | 4 |
Math 341 | Statistical Methods | 3 |
Data Science | Elective 1 | 3 |
Data Science | Elective 1 | 3 |
Fall Semester (15 Credits)
Math 340 | Applied Numerical Methods | 3 |
Math 344 | Regression Analysis | 3 |
Math 391 | Numerical Linear Algebra | 3 |
CS 301 | Introduction to Data Science | 3 |
GER | History and Humanities (300 level) | 3 |
Spring Semester (15 Credits)
Math 345 | Multivariate Distributions | 3 |
Math 447 | Applied Time Series Analysis | 3 |
Data Science | Elective 2 | 3 |
Data Science | Elective 4 | 3 |
GER | History and Humanities (300 level) 2 | 4 |
Fall Semester (15 Credits)
Math 448 | Stochastic Simulation | 3 |
Math 461 | Statistical Computing with R | 3 |
Math 462 | Statistics and Statistical Learning Capstone 1 | 3 |
CS 450 | Data Visualization | 3 |
Data Science | Elective 3 | 3 |
Spring Semester (16 Credits)
GER | Humanities/Social Science Senior Seminar | 3 |
Math | Upper Level Elective (300+ level) | 3 |
Math 463 | Statistics and Statistical Learning Capstone 2 | 3 |
Data Science | Elective 4 | 3 |
Free Elective | Free Elective 2 | 4 |
Students considering switching to Computer Science or Mathematical Sciences should take PHYS 111/111A and 121/121A. Do not take PHYS 102/102A
Free electives should be chosen in consultation with the advisor. Some restrictions apply.
Choose from the following for the Data Science – Computing Option (courses used to satisfy another requirement of the degree may not be counted as a Data Science Elective.