Courses to take to develop skills for mathematical modeling and data analysis

Our lab is interested in developing and using mathematical models as formal theories of cognitive processes.  These models can be used to make specific predictions about how people will behave in laboratory tasks.  We can then see if human behavior aligns with one model’s predictions more than others.  These models are also useful for fMRI or mixed effects analyses.  Formal mathematical theories are more tractable and falsifiable than verbal theories.

I have had to teach myself much of the methods for how to do this type of work because the current psychology undergraduate degree does not properly prepare students in this domain.  There is no programming course required for the Psyc degree, and a student can get by taking only relatively easy math courses that are not as difficult as Calculus 1.  I think math and programming courses are invaluable skills for any college graduate to have.  Most importantly, these courses teach students how to think in a rationally sound and mechanistic way.  This type of thinking is needed to develop formal models of cognition.

I have put together a short list that a Psychology undergraduate student could try to incorporate into their degree plan.  This is a list I wish someone had shown me many years ago.  The skills and ways of thinking that are learned from taking these courses will help prepare students for research using mathematical modeling approaches.  These are also the type of skills used in data mining jobs at places like Google, Facebook, etc., and are an incredible supplement to the soft, but very important skills learned from the Psychology degree.

Math for university required courses:

Math 147 (Calculus 1 for Biological sciences)

Math 172 (Calculus)

Other Math courses:

Math 304 (Linear Algebra) and/or Math 323 (Linear Algebra) – more demanding

Math 411 (Mathematical Probability)

Math 442 (Mathematical Modeling)


CSCE 110 (Programming I) – 4 credits

CSCE 121 (Introduction to Program Design and Concepts) – 4 credits

CSCE 221 (Data Structures and Algorithms)  – 4 credits

CSCE 420 (Artificial Intelligence) – 3 credits

CSCE 442 (Scientific Programming) – 3 credit hours


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