Computational and Applied Mathematics and Statistics-Mathematical Biology, Minor
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 minor helps students develop skills for the modeling and analysis of biological processes. Students use their training in mathematics, statistics, and computer science to study problems in biology. Applications range across all scales of biology, from cells to ecosystems.
Course Requirements
| 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 | |
| Core Requirements | ||
| Mathematical Modeling: | ||
| BIOL 325 | Introduction to Quantitative Biology | 3 |
| or MATH 345 | Introduction to Mathematical Biology | |
| NSCI 351 | Cellular Biophysics and Modeling | 3 |
| or MATH 356 | Random Walks in Biology | |
| Programming: | ||
| CSCI 141 | Modern Programming Fundamentals | 3-4 |
| or PHYS 256 | Practical Computing for Scientists and Engineers | |
| Statistics and Data Analysis: | ||
| Select one of the following: | 3-4 | |
| Intro to Biostatistics | ||
| Elementary Probability and Statistics | ||
| Probability and Statistics for Scientists | ||
| Statistical Data Analysis | ||
| Elementary Statistics | ||
| Electives | ||
| Select one of the remaining Mathematical Modeling courses above 1 | 3 | |
| At most one 300 level or above course with a BIOL prefix or BIOL attribute. | ||
| Select one of the following: | 3 | |
| Introduction to Laser Biomedicine | ||
| Matroids: The Value of Abstraction | ||
| Bioengineering and Synthetic Biology | ||
| Supercomputing for Science | ||
| Biofabrication in Tissue Engineering | ||
| Computational Neurosci | ||
| Special Topics in Biology (Introduction to Bioinformatics) | ||
| GIS for Biologists | ||
| Biochemistry | ||
| Physical and Analytical Chemistry for Life Sciences | ||
| Data Structures | ||
| Algorithms | ||
| Fundamentals of Artificial Intelligence/Machine Learning | ||
| Simulation | ||
| Databases | ||
| Applied Machine Learning | ||
| Special Topics in Data Application | ||
| Spatial Data Discovery | ||
| Neural Networks & Deep Learning | ||
| Introduction to Geographic Information Systems and Spatial Analysis | ||
| Ordinary Differential Equations | ||
| Statistical Data Analysis | ||
| Introduction to Numerical Analysis I | ||
| Introduction to Numerical Analysis II | ||
| Nonlinear Dynamics | ||
| Partial Differential Equations | ||
| Probability | ||
| Mathematical Statistics | ||
| Fluid Mechanics | ||
| Statistical Mechanics and Thermodynamics | ||
| Total Hours | 26-28 | |
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Note that only one of BIOL 325 Introduction to Quantitative Biology and MATH 345 Introduction to Mathematical Biology can count towards the minor.
Note: Students may petition the Track Director or CAMS Director to substitute a single elective course with a research credit or independent study course. At the discretion of the directors, permission may be granted if the research content is considered equivalent to the elective requirement.