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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.



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.


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