Applied Mathematics (BS): Mathematical Foundations of Data Science Concentration
The Bachelor of Science in Applied Mathematics provides a strong foundation in mathematical theory, problem-solving, modeling, and computational methods, preparing students for careers in fields like engineering, finance, biomathematics, and data science. The program emphasizes practical applications and offers flexibility through "Applied Electives," allowing students to tailor their studies to interests in areas such as computer science, economics, physics, or operations research. Many students complement their degree with minors or second majors in related fields to enhance their career opportunities.
For more information about this program visit our website.
Department of Mathematics
North Carolina State University
Campus Box 8205
Raleigh, NC 27695
Dr. Alina Duca
Teaching Professor and Director of Undergraduate Programs in Mathematics
SAS Hall 2108B
919.515.1875
anduca@ncsu.edu
Plan Requirements
| Code | Title | Hours |
|---|---|---|
| Orientation (verify requirement) 1 | 0 | |
| COS 100 | Science of Change | 0 |
| or E 115 | Introduction to Computing Environments | |
| Communication and Advanced Writing | ||
| ENG 101 | Academic Writing and Research | 4 |
| Select one of the following Communications courses (Verify Requirement): 1 | 0 | |
| Public Speaking | ||
| Interpersonal Communication | ||
| Argumentation and Advocacy | ||
| Language, Communication, and Culture | ||
| Select one of the following Advanced Writing courses: | 3 | |
| Communication for Engineering and Technology | ||
| Communication for Business and Management | ||
| Communication for Science and Research | ||
| Analysis of Scientific and Technical Writing | ||
| Basic Mathematics | ||
| MA 141 | Calculus I 1 | 4 |
| MA 241 | Calculus II 2 | 4 |
| MA 242 | Calculus III 5 | 4 |
| MA 225 | Foundations of Advanced Mathematics 5 | 3 |
| MA 341 | Applied Differential Equations I 5 | 3 |
| Basic Science 3 | ||
| Courses must be taken from two different disciplines | 8 | |
| Chemistry - A Molecular Science and General Chemistry Laboratory | ||
| General Chemistry I for Students in Chemical Sciences and General Chemistry Laboratory I for Students in Chemical Sciences | ||
| Physics for Engineers and Scientists I and Physics for Engineers and Scientists I Laboratory | ||
| University Physics I | ||
| Introductory Biology: Ecology, Evolution, and Biodiversity | ||
| Introductory Biology: Cellular and Molecular Biology | ||
| Additional GEP Natural Sciences | 4 | |
| Select one of the following three options for the Statistics requirement: 4 | 6 | |
| Introduction to Probability and Distribution Theory and Introduction to Statistical Inference and Regression | ||
| Introduction to Probability and Introduction to Mathematical Statistics II | ||
| Introduction to Mathematical Statistics I and Introduction to Mathematical Statistics II | ||
| Select one of the following Introduction to Programming courses: | 3 | |
| Introduction to Computing: Python | ||
| Introduction to Scientific Computing | ||
| Statistical Programming | ||
| Advanced Mathematics 5, 6 | ||
| MA 405 | Introduction to Linear Algebra | 3 |
| MA 407 | Introduction to Modern Algebra for Mathematics Majors | 3 |
| MA 425 | Mathematical Analysis I | 3 |
| Data Science and Scientific Computing | 9 | |
| Mathematical Foundations of Data Science I | ||
| Mathematics of Scientific Computing | ||
| Mathematical Foundations of Data Science II | ||
| Math Electives 5 | 9 | |
| Data Science Electives | 6 | |
| Select two of the following courses: | ||
| Automated Learning and Data Analysis | ||
or CSC 522 | Automated Learning and Data Analysis | |
| Introduction to Data Science | ||
or CSC 442 | Introduction to Data Science | |
| Introduction to Statistical Computing and Data Management | ||
| Statistical Learning and Data Analytics | ||
| Advanced Computing for Statistical Reasoning | ||
| Convex Optimization Methods in Data Science | ||
| Concentration Requirements | ||
| PHI 227 | Data Ethics | 3 |
| DSA 202 | Introduction to Data Visualization | 1 |
| DSA 405 | Data Wrangling and Web Scraping | 1 |
| General Data Science Electives: Select 4 credits of DSA courses, at least 2 at the 400-level | 4 | |
| Advised Electives | 9 | |
| A personalized plan for the 9 credits of Advised Electives will be developed in collaboration with the student’s academic advisor. These courses offer the opportunity for the student to explore areas of interest in greater depth, particularly in subjects related to data science. Students are also encouraged to use these electives to pursue a minor or a second major. | ||
| GEP Courses | ||
| GEP Humanities | 3 | |
| GEP Social Sciences | 6 | |
| GEP Health and Exercise Studies | 2 | |
| GEP Interdisciplinary Perspectives | 5 | |
| GEP Requirement | 3 | |
| GEP Global Knowledge (verify requirement) | ||
| GEP Foundations of American Democracy (verify requirement) | ||
| World Language Proficiency (verify requirement) | ||
| Free Electives 7 | 4 | |
| Free Electives (4 Hr S/U Lmt) | ||
| Total Hours | 120 | |
- *
Minimum 2.0 math GPA required for graduation.
- 1
The Orientation and Communication verify requirements are satisfied by some General Education Program courses which could potentially double count.
- 2
MA 141 and MA 241 must be completed with a grade of C or better.
- 3
At most one grade below a C- is permitted in Basic Science Electives (CH, PY, BIO). The Statistics and programming requirements must be satisfied with a letter grade of C- or better.
- 4
(ST 370 with MA 421) or (ST 370 with ST 421) will be accepted for students transferring into the Mathematics major having already taken ST 370.
- 5
At most one letter grade of D +/- is permitted in Math Elective courses. No grades of D +/- are permitted in required math courses (MA141, MA241, MA242, MA225, MA341, MA 326, MA 402, MA404, MA405, MA407, MA425).
- 6
No more than 6 total credits each from undergraduate research (MA 491), independent study (MA 499), or credit by examination may be used to meet program requirements (credit from AP exams or transfer credits is not included under this restriction). Also, students must complete at least one-half of the required mathematics credit hours (21) at NC State University.
- 7
Students are encouraged to use Free Electives credits to pursue a minor or second major. Free electives courses cannot be CH 100, CH 111, CSC 226, ECE 220, MA 101, MA 107, MA 108, MA 111, MA 121, MA 131, MA 231, PY 131, PY 211, PY 212, ENG 100. 100-level World Language Courses (WL*) can be used if not satisfying the language proficiency requirement.
Math Electives
| Code | Title | Hours |
|---|---|---|
| MA 325 | Introduction to Applied Mathematics | 3 |
| MA 335 | Symbolic Logic | 3 |
| LOG 335 | Symbolic Logic | 3 |
| MA 351 | Introduction to Discrete Mathematical Models | 3 |
| MA 401 | Applied Differential Equations II | 3 |
| MA 408 | Foundations of Euclidean Geometry | 3 |
| MA 410 | Theory of Numbers | 3 |
| MA 412 | Long-Term Actuarial Models | 3 |
| ST 412 | Long-Term Actuarial Models | 3 |
| MA 413 | Short-Term Actuarial Models | 3 |
| ST 413 | Short-Term Actuarial Models | 3 |
| MA 414 | Foundations of Cryptography | 3 |
| CSC 414 | Foundations of Cryptography | 3 |
| MA 416 | Introduction to Combinatorics | 3 |
| CSC 416 | Introduction to Combinatorics | 3 |
| MA 421 | Introduction to Probability | 3 |
| MA 426 | Mathematical Analysis II | 3 |
| MA 427 | Introduction to Numerical Analysis I | 3 |
| CSC 427 | Introduction to Numerical Analysis I | 3 |
| MA 428 | Introduction to Numerical Analysis II | 3 |
| CSC 428 | Introduction to Numerical Analysis II | 3 |
| MA 432 | Mathematical Models in Life Sciences | 3 |
| MA 437 | Applications of Algebra | 3 |
| MA 450 | Methods of Applied Mathematics I | 3 |
| MA 451 | Methods of Applied Mathematics II | 3 |
| MA 491 | Reading in Honors Mathematics | 1-3 |
| MA 501 | Advanced Mathematics for Engineers and Scientists I | 3 |
| MA 504 | Introduction to Mathematical Programming | 3 |
| OR 504 | Introduction to Mathematical Programming | 3 |
| MA 505 | Linear Programming | 3 |
| ISE 505 | Linear Programming | 3 |
| OR 505 | Linear Programming | 3 |
| MA 513 | Introduction To Complex Variables | 3 |
| MA 514 | Foundations of Cryptography | 3 |
| CSC 514 | Foundations of Cryptography | 3 |
| MA 515 | Analysis I | 3 |
| MA 518 | Geometry of Curves and Surfaces | 3 |
| MA 520 | Linear Algebra | 3 |
| MA 521 | Abstract Algebra I | 3 |
| MA 522 | Computer Algebra | 3 |
| MA 523 | Linear Transformations and Matrix Theory | 3 |
| MA 524 | Combinatorics I | 3 |
| MA 526 | Mathematical Analysis II | 3 |
| MA 528 | Options and Derivatives Pricing | 3 |
| FIM 528 | Options and Derivatives Pricing | 3 |
| ECG 528 | Options and Derivatives Pricing | 3 |
| MBA 528 | Options and Derivatives Pricing | 3 |
| MA 531 | Dynamic Systems and Multivariable Control I | 3 |
| E 531 | Dynamic Systems and Multivariable Control I | 3 |
| OR 531 | Dynamic Systems and Multivariable Control I | 3 |
| MA 532 | Ordinary Differential Equations I | 3 |
| MA 534 | Introduction To Partial Differential Equations | 3 |
| MA 537 | Nonlinear Dynamics and Chaos | 3 |
| MA 540 | Uncertainty Quantification for Physical and Biological Models | 3 |
| MA 542 | Convex Optimization Methods in Data Science | 3 |
| MA 544 | Computer Experiments In Mathematical Probability | 3 |
| MA 546 | Probability and Stochastic Processes I | 3 |
| ST 546 | Probability and Stochastic Processes I | 3 |
| MA 547 | Stochastic Calculus for Finance | 3 |
| MA 548 | Monte Carlo Methods for Financial Math | 3 |
| FIM 548 | Monte Carlo Methods for Financial Math | 3 |
| MA 549 | Financial Risk Analysis | 3 |
| FIM 549 | Financial Risk Analysis | 3 |
| MA 551 | Introduction to Topology | 3 |
| MA 555 | Introduction to Manifold Theory | 3 |
| MA 561 | Set Theory and Foundations Of Mathematics | 3 |
| MA 565 | Graph Theory | 3 |
| CSC 565 | Graph Theory | 3 |
| OR 565 | Graph Theory | 3 |
| MA 573 | Mathematical Modeling of Physical and Biological Processes I | 3 |
| BMA 573 | Mathematical Modeling of Physical and Biological Processes I | 3 |
| MA 574 | Mathematical Modeling of Physical and Biological Processes II | 3 |
| BMA 574 | Mathematical Modeling of Physical and Biological Processes II | 3 |
| MA 580 | Numerical Analysis I | 3 |
| CSC 580 | Numerical Analysis I | 3 |
| MA 583 | Introduction to Parallel Computing | 3 |
| CSC 583 | Introduction to Parallel Computing | 3 |
| MA 584 | Numerical Solution of Partial Differential Equations--Finite Difference Methods | 3 |
| MA 587 | Numerical Solution of Partial Differential Equations--Finite Element Method | 3 |
| First Year | ||
|---|---|---|
| Fall Semester | Hours | |
| COS 100 | Science of Change 1 | 2 |
| MA 141 | Calculus I 2 | 4 |
| DSA 202 | Introduction to Data Visualization | 1 |
| GEP Requirement | 3 | |
| GEP Natural Sciences 3 | 4 | |
| GEP Health and Exercise Studies | 1 | |
| Hours | 15 | |
| Spring Semester | ||
| ENG 101 | Academic Writing and Research | 4 |
| MA 241 | Calculus II 2 | 4 |
| PY 205 | Physics for Engineers and Scientists I 3 | 3 |
| PY 206 | Physics for Engineers and Scientists I Laboratory 2 | 1 |
| COM 292 | Language, Communication, and Culture 1 | 3 |
| Hours | 15 | |
| Second Year | ||
| Fall Semester | ||
| MA 242 | Calculus III 5 | 4 |
| MA 225 | Foundations of Advanced Mathematics 5 | 3 |
| CSC 111 | Introduction to Computing: Python | 3 |
| BIO 181 | Introductory Biology: Ecology, Evolution, and Biodiversity 3 | 4 |
| GEP Health and Exercise Studies | 1 | |
| Hours | 15 | |
| Spring Semester | ||
| MA 341 | Applied Differential Equations I 5 | 3 |
| MA 405 | Introduction to Linear Algebra 5 | 3 |
| ST 371 | Introduction to Probability and Distribution Theory 3, 4 | 3 |
| PHI 227 | Data Ethics | 3 |
| Advised Electives | 3 | |
| Hours | 15 | |
| Third Year | ||
| Fall Semester | ||
| MA 407 | Introduction to Modern Algebra for Mathematics Majors 5 | 3 |
| MA 326 | Mathematical Foundations of Data Science I | 3 |
| ST 372 | Introduction to Statistical Inference and Regression 3, 4 | 3 |
| DSA 405 | Data Wrangling and Web Scraping | 1 |
| Advanced Writing/Speaking Elective | 3 | |
| Advised Electives | 3 | |
| Hours | 16 | |
| Spring Semester | ||
| MA 425 | Mathematical Analysis I | 3 |
| MA 404 | Mathematical Foundations of Data Science II | 3 |
| ST 442 | Introduction to Data Science | 3 |
| GEP Requirement | 3 | |
| General DSA Electives | 2 | |
| Hours | 14 | |
| Fourth Year | ||
| Fall Semester | ||
| MA 402 | Mathematics of Scientific Computing | 3 |
| Advanced Mathematics Elective 5 | 3 | |
| CSC 422 | Automated Learning and Data Analysis | 3 |
| Advised Electives | 3 | |
| GEP Requirement | 3 | |
| Hours | 15 | |
| Spring Semester | ||
| Advanced Mathematics Elective 5 | 6 | |
| General DSA Electives | 2 | |
| GEP Requirement | 3 | |
| Free Electives 7 | 4 | |
| Hours | 15 | |
| Total Hours | 120 | |
- *
Minimum 2.0 math GPA required for graduation.
- 1
The Orientation and Communication verify requirements are satisfied by some General Education Program courses which could potentially double count.
- 2
MA 141 and MA 241 must be completed with a grade of C or better.
- 3
At most one grade below a C- is permitted in Basic Science Electives (CH, PY, BIO). The Statistics and programming requirements must be satisfied with a letter grade of C- or better.
- 4
(ST 370 with MA 421) or (ST 370 with ST 421) will be accepted for students transferring into the Mathematics major having already taken ST 370.
- 5
At most one letter grade of D +/- is permitted in Math Elective courses. No grades of D +/- are permitted in required math courses (MA141, MA241, MA242, MA225, MA341, MA402, MA405, MA407, MA425).
- 6
No more than 6 total credits each from undergraduate research (MA 491), independent study (MA 499), or credit by examination may be used to meet program requirements (credit from AP exams or transfer credits is not included under this restriction). Also, students must complete at least one-half of the required mathematics credit hours (21) at NC State University.
- 7
Students are encouraged to use Free Electives credits to pursue a minor or second major. Free electives courses cannot be CH 100, CH 111, CSC 226, ECE 220, MA 101, MA 107, MA 108, MA 111, MA 121, MA 131, MA 231, PY 131, PY 211, PY 212, ENG 100. 100-level World Language Courses (WL*) can be used if not satisfying the language proficiency requirement.
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