UMN Course List

Here you will find a list of recommended courses curated by the PIs of the DCAN Labs. While this list provides a general overview of classes related to our field of research, we recognize the individual nature of a college education. Please feel free to reach out to a DCAN PI for one-on-one assistance in selecting courses from this list.

If any of these classes have been retired, or if you find other classes that are suitable for inclusion, please submit a GitHub issue with your suggested edits.

Mathematics and Computing

CSCI 1133 - Introduction to Computing and Programming Concepts

Fundamental programming concepts using Python language. Problem-solving skills, recursion, object-oriented programming. Algorithm development techniques. Use of abstractions/modularity. Data structures/abstract data types. Develop programs to solve real-world problems.

MATH 2241 - Mathematical Modeling of Biological Systems

Development, analysis and simulation of models for the dynamics of biological systems. Mathematical topics include discrete and continuous dynamical systems, linear algebra, and probability. Models from fields such as ecology, epidemiology, physiology, genetics, neuroscience, and biochemistry.

CSCI 3003 - Introduction to Computing in Biology

This course builds the computational skills needed to carry out basic data analysis tasks common in modern biology. Students will learn computing concepts (algorithm development, data structures, complexity analysis) along with practical programming skills in Python and R. No previous programming knowledge assumed.

STAT 3011 - Introduction to Statistical Analysis

Standard statistical reasoning. Simple statistical methods. Social/physical sciences. Mathematical reasoning behind facts in daily news. Basic computing environment.

BIOL 3272 - Applied Biostatistics

Conceptual basis of statistical analysis. Statistical analysis of biological data. Data visualization, descriptive statistics, significance tests, experimental design, linear model, simple/multiple regression, general linear model. Lectures, computer lab.

GCD 5005 - Computer Programming for Biology

Computer programming skills with applications in biology. Design/build new computer programs for applications in cell/developmental biology, including modeling of biological processes, advanced data analysis, automated image analysis.

PSY5018H - Mathematical Models of Human Behavior

Mathematical models of complex human behavior, including individual/group decision making, information processing, learning, perception, and overt action. Specific computational techniques drawn from decision theory, information theory, probability theory, machine learning, and elements of data analysis. prereq: Math 1271 or instr consent

Neuroscience/Neuroanatomy

NSCI 2101 – Human Neuroanatomy

This course will provide a broad introduction to the nervous system with an emphasis on the human nervous system. The course will introduce the structure and function of neurons, the major anatomical parts of the nervous system and the main functional systems. Functional systems will be approached through an understanding of the anatomical circuitry. The fundamental concepts of neurochemical communication studied in general terms in the first part of the course will be re-examined relative to specific functional systems later in the course. Although the major focus of the course will be on the normal nervous system, common diseases will be introduced for each main topic. Students will gain an understanding of the nature of many neurological diseases, which will provide further insight into how the normal nervous system functions. The neuronal substrates of learning/memory, addiction and drug actions will be examined. Through the lectures, laboratory exercises and other resources, students will be expected to gain an understanding of the neural circuitry and information processing responsible for the diverse range of human behaviors.

NSCI 3101 - Neurobiology I: Molecules, Cells, and Systems

This course discusses the basic principles of cellular and molecular neurobiology and nervous systems. The main topics include: Organization of simple networks, neural systems and behavior; how the brain develops and the physiology and communication of neurons and glia; the molecular and genetic basis of cell organization; ion channel structure and function; the molecular basis of synaptic receptors; transduction mechanisms and second messengers; intracellular regulation of calcium; neurotransmitter systems, including excitation and inhibition, neuromodulation, system regulation, and the cellular basis of learning, memory, and cognition. The course is intended for students majoring in neuroscience, but is open to all students with the required prerequisites.

NSCI 3102W - Neurobiology II: Perception and Behavior

This is the second of the introductory neurobiology courses. It introduces fundamental concepts in systems and behavioral neuroscience with emphasis on the neural circuits underlying perception and sensorimotor integration. Lectures will examine the neural basis of specific behaviors arising from the oculomotor, visual and auditory systems and notes are available on Canvas. Topics include: retinal processing, functional organization in the cerebral cortex, neural circuit development, language, reward, and addiction

NSC 5561 – Systems Neuroscience

Principles of organization of neural systems forming the basis for sensation/movement. Sensory-motor/neural-endocrine integration. Relationships between structure and function in nervous system. Team taught. Lecture, laboratory.

NSC 5661W - Behavioral Neuroscience

Neural coding/representation of movement parameters. Neural mechanisms underlying higher order processes such as memorization, memory scanning, and mental rotation. Emphasizes experimental psychological studies in human subjects, single cell recording experiments in subhuman primates, and artificial neural network modeling.

PSY 5038W - Introduction to Neural Networks

Parallel distributed processing models in neural/cognitive science. Linear models, Hebbian rules, self-organization, non-linear networks, optimization, representation of information. Applications to sensory processing, perception, learning, memory.

MATH 5447 - Theoretical Neuroscience

Nonlinear dynamical system models of neurons and neuronal networks. Computation by excitatory/inhibitory networks. Neural oscillations, adaptation, bursting, synchrony

Psychology

PSY3061 - Intro to Biological Psychology

Neurophysiology/neuroanatomy, neural mechanisms of motivation, emotion, sleep-wakefulness cycle, learning/memory in animals/humans. Neural basis of abnormal behavior, drug abuse.

PSY 5062 - Cognitive Neuropsychology

Consequences of different types of brain damage on human perception/cognition. Neural mechanisms of normal perceptual/cognitive functions. Vision/attention disorders, split brain, language deficits, memory disorders, central planning deficits. Emphasizes function/phenomenology

PSY 5064 - Brain & Emotion

Introduction to affective neuroscience. How brain promotes emotional/motivated behavior in animals/humans. Biological theories of emotion in historical/current theoretical contexts. Fundamental brain motivational systems, including fear, pleasure, attachment, stress, and regulation of motivated behavior. Implications for emotional development, vulnerability to psychiatric disorders.

PSY 5137 - Introduction to Behavioral Genetics

Genetic methods for studying human/animal behavior. Emphasizes nature/origin of individual differences in behavior. Twin and adoption methods. Cytogenetics, molecular genetics, linkage/association studies.

PSY 5862 - Psychological Measurement: Theory and Methods

Types of measurements (tests, scales, inventories) and their construction. Theory/measurement of reliability/validity. The focus is not on specific measuring instruments, but on methods and procedures that are used to develop various types of instruments. Procedures for evaluating instruments in terms of their reliability, validity, and other characteristics are emphasized. A basic knowledge of statistics and introductory calculus (simple derivatives and integrals) is assumed.

PSY 5865 - Advanced Psychological and Education Measurement

Topics in test theory. Classical reliability/validity theory/methods, generalizability theory. Linking, scaling, equating. Item response theory, methods for dichotomous/polytomous responses. Comparisons between classical, item response theory methods in instrument construction.

Neuroimaging

PSY 5065 - Functional Imaging: Hands-on Training

Basic neuroimaging techniques/functional magnetic resonance imaging (fMRI). First half of semester covers basic physical principles (e.g. K-space, spin echo, signal to noise ratio, resolution, scan time, trade-offs, Matlab simulations, pulse sequences). Second half students design/execute fMRI experiment on Siemens 3 Tesla scanner.

Data Analysis

STAT 5701 - Statistical Computing

Statistical programming, function writing, graphics using high-level statistical computing languages. Data management, parallel computing, version control, simulation studies, power calculations. Using optimization to fit statistical models. Monte Carlo methods, reproducible research.

PUBH 7430 - Statistical Methods for Correlated Data

Correlated data arise in many situations, particularly when observations are made over time and space or on individuals who share certain underlying characteristics. This course covers techniques for exploring and describing correlated data, along with statistical methods for estimating population parameters (mostly means) from these data. The focus will be primarily on generalized linear models (both with and without random effects) for normally and non-normally distributed data. Wherever possible, techniques will be illustrated using real-world examples. Computing will be done using R and SAS.

PSY 8814 - Analysis of Psychological Data

Data-analytic procedures used in psychological research. Types of variables used in psychological research. Data collection designs, their limitations. Procedures for analyzing experimental/non-experimental data, both univariate and multivariate. Emphasizes selection of data-analytic procedures. Procedures and their assumptions. Computation using R.

PSY 8815 - Analysis of Psychological Data

Data-analytic procedures used in psychological research. Types of variables used in psychological research. Data collection designs, their limitations. Procedures for analyzing experimental/non-experimental data, both univariate and multivariate. Emphasizes selection of data-analytic procedures. Procedures and their assumptions. Computation using R.

PSY 8960 – Rotating Statistics Class for Social Scientists

Multivariate statistics, non-parametric statistics, and multilevel modeling

EPSY 8264 - Advanced Multiple Regression Analysis

General linear model used as context for regression. Matrix algebra, multiple regression, path analysis, polynomial regression, standardized regression, stepwise solutions, analysis of variance, weighted least squares, logistic regression.

EPSY 8268 - Hierarchical Linear Modeling in Educational Research

Conceptual framework of hierarchical linear models for nested data, their application in educational research. Nature/effects of nested data, logic of hierarchical models, mixed-effects models. Estimation/hypothesis testing in these models, model-checking, nonlinear models.

STAT 8051 - Advanced Regression Techniques: linear, nonlinear and nonparametric methods

Linear/generalized linear models, modern regression methods including nonparametric regression, generalized additive models, splines/basis function methods, regularization, bootstrap/other resampling-based inference.

STAT 8052 - Applied Statistical Methods 2: Design of Experiments and Mixed - Effects Modeling

STAT 8053 - Applied Statistical Methods 3: Multivariate Analysis and Advanced Regression

Standard multivariate analysis. Multivariate linear model, classification, clustering, principal components, factor analysis, canonical correlation.

STAT 8054 - Statistical Methods 4: Advanced Statistical Computing