This course is meant to provide students in applied mathematics with the broad skill-set needed to optimize model parameters to relevant biological or epidemic data. The course will almost entirely be based on material posted on this website.
Upon completing this course:
Students will gain a basic understanding of applied statistics, and will be functional in both R and C++. Note that this course will not be using an integrated code development environment for C++ program development; all C++ programs will be edited and compiled from the Unix command line. Students will also learn the basics of the Unix operating system, and will know how to submit jobs to batch in the A2C2 and, if possible, XSEDE distributed computing resources.
Students will learn how to read in, manipulate, and export data in both R and C++, and will be able to create publication-quality plots in R. Students will become adept at combining information from a variety of sources in an analysis (for instance, climate and disease incidence data, or vector population and disease incidence data). Students will be familiar with several different parameter optimization methods, and will understand the underlying assumptions of each.
There will be regular homework projects assigned throughout the course, which will be worth 50% of the grade. Students are strongly encouraged to work together in groups to discuss issues related to the course and resolve problems. However, plagiarism of code will not be tolerated.
The culmination of the course will be a group term project (two to three students collaborating together, with the project worth 50% of the final grade) that requires the development of a C++ program to solve a system of ordinary differential equations that describes the dynamics of disease spread, interacting biological populations, etc. The students will then use the Saguaro or XSEDE super computing resources to optimize the parameters of their model to data that the student has identified as being appropriate to describe with their model. The students will write-up the results of their project in a format suitable for publication (submission for publication is not required, but encouraged if the data and model analysis is novel). Students are responsible for locating and obtaining sources of data, and developing an appropriate model, so this should be something students begin to think about very early in the course.
This course has no associated textbook, due to the unique nature of the course content. Instead the course content consists of the modules that appear on this website. A textbook that students may find useful is Statistical Data Analysis, by G. Cowan
Students are expected to bring their laptops to class. Before the course begins, students are expected to have downloaded the R programming language onto their laptop from http://www.r-project.org/ (R is open-source free software).
Students are expected to either have access to a Unix/Linux-like operating system on either their laptop, or by remote access to another computer via ssh. Students are also expected to have a C++ compiler available on their laptop, or accessible by remote access via ssh (cc, gcc, or g++).
For students with Windows laptops, ASU provides an SSH/SCP client. Go to apps.asu.edu and in the software search tab type SSH. Then download the SSH client for windows, and start it up and follow the instructions to install it.
Once installed, start the program. It can be used both for transferring files, and also for SSH’ing to another machine. To connect to the general.asu.edu machine (which is a Unix machine for which you all should have an account under your ASURITE ID), click on “quick connect” and follow the instructions.
general.asu.edu has a g++ compiler, and you should be able to compile and run all C++ programs related to this course.
In addition to the general.asu.edu Unix machine, students can and should also subscribe to the research2.asu.edu and medusa2.asu.edu Unix machines. Go to asu.edu/selfsub and follow the instructions to obtain accounts on these machines.