Curriculum Requirements
Year One Courses
The listed courses below are core requirements meant to be taken within the first year of the program.
NEU 801: Molecular, Cellular, and Developmental Neuroscience I
3 credits, Fall Semester
Genetics, molecular and cellular biology of the developing and the adult nervous system.
NEU 802: Systems and Behavioral Neuroscience I
3 credits, Fall Semester
Nervous system specific gene transcription and translation. Maturation, degeneration,
plasticity, and repair in the nervous system
NEU 803: Molecular, Cellular, and Developmental Neuroscience II
3 credits, Spring Semester
Electrical and intra- and extracellular signaling mechanisms of neurons and glia in
health and disease in the developing and mature nervous system.
NEU 805: Systems and Behavioral Neuroscience II
3 credits, Spring Semester
Anatomy and physiology of multicellular olfactory, visual, auditory, motor, somatosensory
and autonomic nervous systems.
NEU 807: Strategies in Neuroscience Research
2 credits, Fall Semester
Methods and underlying principles of neuroscience research
PHM 830: Experimental Design and Data Analysis
3 credits, Summer Semester
Practical application of statistical principles to the design of experiments and analysis
of experimental data in pharmacology, toxicology, and related biomedical sciences.
Year Two Courses
During the second year, students have a choice between taking one (3 credits total) of the courses listed below:
ANS 824: Methods in Quantitative Genomics
3 credits, Fall Semester
Storage, processing and analysis of genotypic and phenotypic data using R. Basic R
programming and R tools for genomic analyses. Genome-wide association studies and
genomic prediction.
CMSE 830: Foundations of Data Science
3 credits, Fall Semester
Core mathematical principles that underlie the algorithms and methods used in data
science. Applications to problems in data analysis.
CSE 881: Data Mining
3 credits, Spring Semester
Techniques and algorithms for knowledge discovery in databases, from data preprocessing
and transformation to model validation and post-processing. Core concepts include
association analysis, sequential pattern discovery, anomaly detection, predictive
modeling, and cluster analysis. Application of data mining to various application
domains.
IBIO 830: Statistical Methods in Ecology and Evolution I
3 credits, Fall Semester
Fundamental elements of data analysis in ecology and evolution. Programming fundamentals
in the R computing language. Introduction to modeling biological data with modern
methods for estimation and inference.
STT 811: Applied Statistical Modeling for Data Scientists
3 credits, Spring Semester
Data Visualization. Linear regression. Analysis of variance. Logistic regression.
Generalized linear models. Variable selection. Categorical data analysis. Models for
design of experiments. Models for time series data.
STT 832: Data Visualization and Programming in R
3 credits, Fall Semester
Development of sports data predictive models. Extraction and management of sport data,
graphical and numerical summaries using visualization tools to model practical sports
scenarios. Compilation of written reports on test results and performance outputs.
Electives
Students need two electives. Students should take courses related to their research and
can work with their dissertation committee to choose these courses. Electives are
typically taken in Year Two.
Dissertation Research (999) Credits
NEU 999: Dissertation Research
A minimum of 24 credits total is required by MSU to earn a Ph.D. Typically, these credits are taken over multiple semesters after the comprehensive exam has been passed. No more than 36 research credits can be taken.
Other Information
- Other program requirements
- Comprehensive exam resources
- Dissertation completion information from previous students
- Format your dissertation right the first time