Computational and Applied Mathematics and Statistics, Mathematical Biology Track (BS in Computational and Applied Mathematics and Statistics)
CAMS Program
The CAMS (Computational and Applied Mathematics and Statistics) program is by nature inter-disciplinary. In CAMS, applications are the primary driver of the research agenda for scholarly activity. The CAMS program supports a collaborative, multi-disciplinary, and integrative approach to teaching and research in applied and computational mathematics, operations research, mathematical biology and statistics. Course work and research experiences in CAMS provide a strong base in both the knowledge and practical skills necessary to make important contributions to mathematics, industry and the sciences. There are two tracks within the CAMS program: Mathematical Biology and Applied Statistics.
Mathematical Biology aims at modeling natural, biological processes using mathematical techniques and tools. It has both practical and theoretical applications in biological research. Applying mathematics to biology has a long history, but only relatively recently has there been an explosion of interest in the field. Some reasons for this include: the explosion of data-rich information sets, due to the genomics revolution, which are difficult to understand without the use of analytical tools; recent development of mathematical tools such as chaos theory to help understand complex, nonlinear mechanisms in biology; an increase in computing power which enables calculations and simulations to be performed that were not previously possible; and an increasing interest in computer experimentation due to the complications involved in human and animal research.
The Applied Statistics Track provides a major option for undergraduates with an interest in statistics, “big data”, and actuarial science. As humans have developed cheaper and smaller sensors, web cameras and other data collection devices, the amount of data available to be analyzed and understood has exploded. Statistics is the mathematical science that pertains to the collection, analysis, interpretation, explanation, and presentation of data. Because of its empirical roots and its focus on applications, statistics is typically considered a distinct mathematical science rather than a branch of mathematics.
The Applied Statistics major gives students a rigorous training in the highly-interdisciplinary modern practice of statistics. The major draws upon a foundation of topics in mathematics and computer science to build a core specialization in applied and theoretical statistics and enriches this statistical knowledge with cross-disciplinary electives. Possible areas of focus within the major include: econometrics, data science, general applied statistics, and actuarial science.
The CAMS Mathematical Biology major trains students in the modeling and analysis of biological processes using tools from mathematics, statistics, data science, and computer science. These techniques have both practical and theoretical applications in biological research. Applications range across all scales of biology, including simulations of cellular processes, designs for synthetic biology, analyses of genomic data, and models of ecosystems.
Course Requirements
Major Writing Requirement
The upper-division writing requirement is satisfied by one of the following ways:
- Completion of BIOL 325 Introduction to Quantitative Biology or MATH 345 Introduction to Mathematical Biology with a grade of C- or better, or
- Completion of CAMS 495 Honors and CAMS 496 Honors , which requires the writing of an Honors thesis.
Major Requirements
No course can be counted in more than one category.
| Code | Title | Hours |
|---|---|---|
| MATH 111 | Calculus I | 4 |
| or MATH 131 | Calculus I for Life Sciences | |
| MATH 112 | Calculus II | 4 |
| or MATH 132 | Calculus II for Life Sciences | |
| BIOL 203 | Introduction to Molecules, Cells, Development | 3 |
| BIOL 204 | Introduction to Organisms, Ecology, Evolution | 3 |
| Major Computing Requirement | ||
| CSCI 141 | Modern Programming Fundamentals | 4 |
| CSCI 241 | Data Structures | 3 |
| Major Mathematics Requirement 1 | ||
| MATH 109 | Linear Algebra | 3 |
| MATH 109L | Computational Linear Algebra Lab | 1 |
| and | ||
| MATH 212 | Introduction to Multivariable Calculus | 3-4 |
| or MATH 213 | Multivariable Calculus for Science and Mathematics | |
| Mathematical Modeling Requirement | ||
| BIOL 325 | Introduction to Quantitative Biology (only one of these may be used to count toward the minor.) | 3-4 |
| or MATH 345 | Introduction to Mathematical Biology | |
| NSCI 351 | Cellular Biophysics and Modeling | 3 |
| or MATH 356 | Random Walks in Biology | |
| Statistics and Data Analysis Electives | ||
| Select two of the following: | 6-7 | |
| Intro to Biostatistics | ||
| Probability and Statistics for Scientists 2 | ||
| Statistical Data Analysis | ||
| Probability | ||
| Mathematical Statistics | ||
| Computational Elective | ||
| Select one of the following: | 3 | |
| Software Development | ||
| Algorithms | ||
| Simulation | ||
| Elementary Topics | ||
| Databases | ||
| Advanced Applications of AI | ||
| Neural Networks & Deep Learning | ||
| Practical Computing for Scientists and Engineers | ||
| Biology Electives | ||
| Select two of the following: | 6 | |
Any two 300-level or above courses with a BIOL prefix or BIOL attribute, of at least 3 credits each. | ||
| Applications and Models Electives | ||
| Select two of the following: | 6 | |
| Introduction to Laser Biomedicine | ||
| Matroids: The Value of Abstraction | ||
| Bioengineering and Synthetic Biology | ||
| Supercomputing for Science | ||
| Biomedical Materials and Devices | ||
| Biofabrication in Tissue Engineering | ||
| Computational Neurosci | ||
| Special Topics in Biology (Introduction to Bioinformatics) | ||
| GIS for Biologists | ||
| Physical and Analytical Chemistry for Life Sciences | ||
| Biochemistry | ||
| Fundamentals of Artificial Intelligence/Machine Learning | ||
| Applied Machine Learning | ||
| Spatial Data Discovery | ||
| Introduction to Geographic Information Systems and Spatial Analysis | ||
| Ordinary Differential Equations | ||
| Topics in Mathematics | ||
| Introduction to Numerical Analysis I | ||
| Introduction to Numerical Analysis II | ||
| Nonlinear Dynamics | ||
| Partial Differential Equations | ||
| Fluid Mechanics | ||
| Statistical Mechanics and Thermodynamics | ||
| Total Hours | 55-58 | |
- 1
This is normally done by taking and passing CSCI 141 Modern Programming Fundamentals and CSCI 241 Data Structures.
- 2
Note: MATH 351 Probability and Statistics for Scientists cannot be taken for credit if credit for MATH 451 Probability has already been given.
Note
Students may petition the Track Director or CAMS Director to substitute a single elective course with a research credit, independent study course, or honors thesis course. In total, at most one elective may be substituted in this manner. At the discretion of the directors, permission may be granted if the research content is considered equivalent to the elective requirement.