Graduate
Graduate Program
The M.S. degree prepares students to enter a Ph.D. program, to teach at some colleges, and for many positions in business and industry. We offer a non-thesis and thesis option for our M.S. program, both of which can be completed in two years.
A Ph.D. in mathematics gives you the opportunity to teach mathematics at the university level (with the opportunity for original research).
The objective of the M.S. program in Statistical Science is to provide sound training in the fundamental principles and techniques of statistics. Graduates will be equipped for a variety of statistical careers in industry, business, agriculture, government and biomedical fields or to engage in further study in the doctoral level. This degree is also available online.
Three tracks provide flexibility in meeting our students academic and career goals:
- thesis option
- non-thesis internship option
- non-thesis consulting option
The Master of Science in bioinformatics and computational biology from the University of Idaho provides a unique interdisciplinary graduate educational experience. Our program has a stronger focus on statistical analysis than is typical in a bioinformatics program, but we emphasize computation and genomics more than is typical for a biostatistics program. Thus our students get the appropriate training that leads to successful careers in government, biotechnology, agriculture, biomedicine and academia.
Students have access to state of the art core facilities, including Research Computing and Data Services (RCDS) and Genomics and Bioinformatics Resources Core (GBRC) facility.
This program has a stronger focus on statistical analysis than is typical in a bioinformatics program, but we emphasize computation and genomics more than is typical for a biostatistics program. This enables the graduate to advance the state of the art, not merely to keep up with it. Thus our students get the appropriate training that lead to successful careers in government, biotechnology, agriculture, biomedicine and academia.
Students have access to state of the art core facilities, including Research Computing and Data Services (RCDS) and the Genomics and Bioinformatics Resources Core (GBRC) facility.
This program prepares you to contribute to the field of computer science in new and novel ways. Gain in-depth understanding of limitations and opportunities of computers in problem solving and explore high-level concepts in computational biology, network security and more.
As a graduate student in this field, you will gain an in-depth understanding of the limitations and opportunities in the use of computers to solve problems. Work alongside faculty on leading research and explore high-level concepts in computational biology and more to prepare for your career in the field or in academia.
Applying to a Certificate Program
- Students admitted to the Graduate School at the University of Idaho must submit the Academic Certificate Declaration on page 2 of the Change of Curriculum form to the certificate coordinator for department chair approval.
- To apply for the program as a stand-alone certificate please apply online through Graduate Admissions. You will be required to submit transcripts showing a minimum of a 3.0 GPA for your undergraduate degree.
- Graduate Academic Certificate requirements can be viewed in Section O-10-b of the U of I Catalog.
- Certificate programs follow the policies and procedures for a Graduate Academic Certificate.
Certificates for Graduate Students
A graduate certificate that trains professionals how to think about, organize, analyze, and visualize data, and communicate data-driven insights to specialist and lay audiences.
A graduate certificate suitable for graduate students with experience in statistical software and programming.
We have many workshops and bootcamp opportunities.
Practical Methods in Analyzing Science Experiments
| Course Number | Course Name | Credits |
|---|---|---|
| AVS 531 | Practical Methods in Analyzing Animal Science Experiments | 3 |
AVS 531: Upon completion of this class students will be able to manage and analyze data obtained from animal experimentations. This is a “hands-on” type of training, specifically designed for AVS graduate students and intends to provide our graduate students with a better understanding of designs commonly used in animal science experiments, advantages and potential pitfalls associated with each design, data processing and analysis, data tabulation, and graphic illustration, and data interpretation. Prereqs: 400-level statistics course
| Course Number | Course Name | Credits |
|---|---|---|
| BE 521 | Image Processing and Computer Vision | 3 |
| BE 541 | Instrumentation and Measurements | 3 |
BE 521: Fundamentals of digital image processing, analysis, feature recognition, and computer vision applied to areas of Biological Engineering including agricultural, environmental and biomedical applications. This course covers camera model, digital image processing and image analysis techniques for computer vision. Additional project components required for graduate credit.
Prereqs: (BE 242 and MATH 275) or permission
| Course Number | Course Name | Credits |
|---|---|---|
| BIOL 526 | Systems Biology | 3 |
| BIOL 545 | Phylogenetics | 3 |
| BIOL 549 | Computer Skills for Biologists | 3 |
| BIOL 563 | Mathematical Genetics | 3 |
BIOL 526 Systems Biology (3 credits)
Joint-listed with BIOL 426
Systems Biology will use quantitative approaches including theory and computation to understand the complex function that emerges from physiological systems. Topics will include transcriptional networks and their common motifs, robustness in chemotaxis and development, noise and variability, evolution of modularity, and optimality in metabolism. Two lectures per week. Cooperative: open to WSU degree-seeking students. (Fall only, alt/years).
Prereqs: BIOL 115, BIOL 115L and MATH 170 or permission of instructor
BIOL 545 Phylogenetics (3 credits)
The inference of evolutionary trees (phylogeny) and the processes that generate biodiversity from analyses of morphological, molecular, and behavioral data; uses of phylogenies in testing evolutionary and other hypotheses at both inter and intraspecific levels. Two hours of lecture and one 3-hour lab per week. Cooperative: open to WSU degree-seeking students. (Spring, Alt/years)
Prereqs: PLSC 205 or BIOL 213 and BIOL 310
BIOL 549 Computer Skills for Biologists (3 credits)
Joint-listed with BIOL 456
Exploration and analysis of biological datasets such as those in molecular evolution, systematics, and genomics. Demonstrations, exercises, and student projects to teach Unix skills, git version control, and computer programming for data exploration and analysis. Graduate credit requires a project and presentation. Cooperative: open to WSU degree-seeking students. (Fall, alt/even years)
Prereqs: BIOL 310 and STAT 251 or STAT 301; or Permission
BIOL 563 Mathematical Genetics (3 credits)
Cross-listed with MATH 563
Investigation of aspects of evolutionary biology with an emphasis on stochastic models and statistical methods; topics include: diffusion methods in molecular evolution, gene genealogies and the coalescent, inferring coalescent times from DNA sequences, population subdivision and F statistics, likelihood methods for phylogenic inference, statistical hypothesis testing, the parametric bootstrap. Cooperative: open to WSU degree-seeking students.
Prereqs: MATH 160 or MATH 170 and STAT 251 or STAT 301
| Course Number | Course Name | Credits |
|---|---|---|
| CE 526 | Aquatic Habitat Modeling | 3 |
| CE 579 | Simulation of Transportation Systems | 3 |
CE 526 Aquatic Habitat Modeling (3 credits, max 6)
The course objective is to learn the underlying principles of all components required for aquatic habitat modeling, to be able to perform such projects in riverine ecosystems including project design, data collection, data analysis and interpretation of the results and to learn the use of computational aquatic habitat models. Students will be working on their own modeling projects using the simulation model CASiMiR.
Prereqs: CE 322 and CE 325 or BE 355; or Permission. A minimum grade of ‘C’ or better is required for all pre/corequisites.
CE 579 Simulation of Transportation Systems (3 credits)
This course introduces students to the simulation of transportation systems, including the algorithms that constitute most traffic simulation models and how the models are applied to the study of real transportation problems. The course considers the fundamental issues that the transportation engineer must consider when developing and applying simulation models, the core algorithms that constitute transportation simulation models, how to build and test a simulation network, the process for validating and calibrating a simulation model, how model results should be analyzed and presented, and the process for using and the value of hardware-in-the-loop simulation.
Prereqs: Permission
| Course Number | Course Name | Credits |
|---|---|---|
| CS 511 | Parallel Programming | 3 |
| CS 570 | Artificial Intelligence | 3 |
| CS 572 | Machine Learning | 3 |
| CS 574 | Deep Learning | 3 |
| CS 575 | Evolutionary Computation | 3 |
| CS 577 | Python for Machine Learning | 3 |
| CS 578 | Neural Network Design | 3 |
| CS 579 | Data Science | 3 |
| CS 589 | Semantic Web and Open Data | 3 |
CS 511 Parallel Programming (3 credits)
Joint-listed with CS 411
Analysis, mapping, and the application of parallel programming software to high-performance systems; the principles of spatial- and temporal-locality of data memory hierarchies in performance tuning; architectural considerations in the design and implementation of a parallel program; the tradeoff between threaded (shared memory) and message-passing (distributed memory) programming styles and performance. Additional projects/assignments required for graduate credit. Recommended Preparation: Proficiency in programming using a modern language such as C or C++.
Prereqs: CS 395
CS 570 Artificial Intelligence (3 credits)
Joint-listed with CS 470
Concepts and techniques involved in artificial intelligence, Lisp, goal-directed searching, history trees, inductive and deductive reasoning, natural language processing, and learning. Extra term paper required for graduate credit.
Prereqs: CS 210
CS 572 Evolutionary Computation (3 credits)
Joint-listed with CS 472
Solving computation problems by “growing” solutions; simulates natural evolution using analogues of mutation, crossover, and other generic transformations on representations of potential solutions; standard EC techniques such as genetic algorithms and evolutionary programming, mathematical explanations of why they work, and a survey of some applications; the focus is on solving real-world problems using projects. Graduate-level research and possible paper or presentation required for graduate credit.
Prereqs: CS 210
CS 574 Deep Learning (3 credits)
Joint-listed with CS 474
Deep Learning is enabling many rapid technological advances across multiple science disciplines, from automated speech recognition through medical image analysis and to autonomous robots and vehicles. This course will cover Deep Learning topics on gradient decent (GD), cross-validation, regularization, deep feedforward neural networks (NNs), convolutional NNs (CNNs), recurrent NNs (RNNs), deep architectures, transfer learning, and multitask learning. In this course students will learn to: understand and describe concepts and implementations of: deep forward networks, regularization, CNNs, RNNs, and transfer learning; apply CNNs and RNNs for modeling, analyzing, and solving real-world problems; select and apply adequate or best-fit toolboxes to train, tune, and test a deep neural network. Students will also gain an ability to successfully communicate, collaborate, and lead within a project group setting. Additional work required for graduate credit.
Prereqs: (CS 121 or MATH 330) and STAT 301
CS 575 Machine Learning (3 credits)
Joint-listed with CS 475
Analysis and implementation of classic machine learning algorithms including neural networks, deep learning networks, principle component analysis, decision trees, support vector machines, clustering, reinforcement learning, ensemble learning, K-means, self-organizing maps and probabilistic learning such as Markov Chain Monte Carlo and Expectation Maximization algorithms. Techniques of preprocessing data, training, testing, and validating will be discussed along with statistical measures commonly used and pitfalls commonly encountered. Additional work required for graduate credit.
Prereqs: CS 210
CS 577 Python for Machine Learning (3 credits)
Joint-listed with CS 477
Python is widely used for Machine Learning and Data Science. This course introduces students to current approaches and techniques for finding solutions to Data Science problems using Machine Learning with Python. Topics include: classification, regression, clustering, ensemble learning, and deep learning. The course offers hands-on experiences with Machine Learning techniques using Python-based libraries and also modern tools used by computer and data scientists such as Jupyter Notebook. In this course students will learn: an ability to understand and describe the fundamental concepts and techniques of Machine Learning and their Python-based implementations; an ability to design, implement, and evaluate Python-based Machine Learning solutions for problems such as data classification and clustering. Students will also develop leadership and teamwork abilities through group discussions and projects. Additional work required for graduate credit.
Prereqs: (CS 121 or MATH 330) and STAT 301
CS 578 Neural Network Design (3 credits)
Introduction to neural networks and problems that can be solved by their application; introduction of basic neural network architectures; learning rules are developed for training these architectures to perform useful functions; various training techniques employing the learning rules discussed and applied; neural networks used to solve pattern recognition and control system problems.
Prereqs: Permission
CS 579 Data Science (3 credits)
Joint-listed with CS 479
Data science is advancing the conduct of science in individual and collaborative works. Data science combines aspects of data management, library science, computer science, and physical science using supporting cyber-infrastructure and information technology. Key methodologies in application areas based on real research experience are taught to build a skill-set that enables students to handle each stage in a data life cycle, from data collection, analysis, archiving, to data discovery, access and reuse. Additional work required for graduate credit.
Prereqs: MATH 330 or Permission
CS 589 Semantic Web and Open Data (3 credits)
Joint-listed with CS 489
The Semantic Web extends the core principles of the World Wide Web to make the meaning of data machine-readable. This course covers the technological framework and associated functionalities enabled by the Semantic Web and Linked Open Data that provide a space for large scale data integration, reasoning and analysis. In this course students will learn: an ability to understand and describe the fundamental concepts in Semantic Web, such as ontology, RDF, OWL, logic reasoning, ontology engineering, knowledge graph, Linked Data, SPARQL, Open Data, as well as the inter-relationships among those concepts; an ability to design and implement domain-specific solutions for Big Data problems using concepts such as ontology engineering, data querying, analysis, and transformation, and output generation; an ability to describe and apply ethical concepts such as privacy, intellectual property, and responsibility as they relate to data analysis and the Semantic Web. Students will also develop leadership and teamwork abilities through group projects. Additional work required for graduate credit.
Prereqs: CS 360 or CS 479 or CS 579
| Course Number | Course Name | Credits |
|---|---|---|
| CTE 519 | Database Applications and Information Management | 3 |
CTE 519 Database Applications and Information Management (3 credits)
Joint-listed with CTE 419
Teaching and training strategies for database applications. Includes database management principles and methods of information retrieval, processing, storage and distribution. Advanced project required for graduate credit.
| Course Number | Course Name | Credits |
|---|---|---|
| CYB 520 | Digital Forensics | 3 |
CYB 520 Digital Forensics (3 credits)
Cross-listed with CS 547
Joint-listed with CS 447, CYB 420
This course covers modern procedures, techniques, and best practices for digital forensic data acquisition, analysis, and case building. Covered topics and knowledge areas include (a) Applicable laws, policies, rules, procedures and best practices, and selected digital forensics techniques and tools (DFS); (b) Processes, techniques, tools, and best practices for static digital forensic data acquisition, analysis, and reporting from different host systems (HOF) and raw media (MEF). At the end of this course, students should have the knowledge, skills, and abilities to be able to appropriately prepare, perform, and record digital forensic investigation tasks on a selected set of media and hosts, of varied types. This including knowledge, skills, and abilities to: (1) Identify and describe applicable laws, policies, procedures, and static acquisition and analysis techniques and best practices for digital forensic investigations; (2) Identify the appropriate tools for a given forensic task on a given type of media, host, or image; and (3) Select and successfully use a variety of digital forensic tools for acquiring, analyzing, and recording case information. Hands-on and/or laboratory work is an essential component in this course. Significant additional work and performance required for graduate-level credit. Typically Offered: Fall.
ED 571 Introduction to Quantitative Research (3 credits)
Overview of research techniques, emphasizing experimental, quasi-experimental, descriptive, analytical, single subject designs. Special emphasis on interpreting and critically evaluating research articles; planning, analyzing, and writing quantitative research studies.
Prereqs: Graduate standing
ED 584 Univariate Quantitative Research in Education (3 credits)
The overall goal of the course is to prepare students to apply quantitative research methodology in education. Topics include understanding applied experimental, quasi-experimental and behavioral designs, survey design, measurement and instrumentation, sampling, item analysis, reliability analysis, and validity assessment.
Prereqs: ED 571
ED 587 Multivariate Quantitative Analysis in Education (3 credits)
Analysis and application of multivariate quantitative research methods in education and social sciences. The goal of the course is to expose students to multivariate statistics and quantitative research approaches. Topics include multiple correlation/regression, discriminate analysis, exploratory and confirmatory factor analysis, multivariate analysis of variance (MANOVA), multivariate analysis of covariance (MANCOVA), canonical correlation analysis, cluster analysis, log linear model, path analysis and structural equation modeling.
Prereqs: ED 584 or Permission
ED 589 Theoretical Applications and Designs of Qualitative Research (3 credits)
This course builds and expands on ED 574 Survey of Qualitative Research and examines qualitative research designs and the use of theory in qualitative research. The course will introduce ethnography, phenomenology, case study, narrative, historical and action research designs. Each design will be explored through four overarching theoretical lenses (organizational, economic, critical, and learning), allowing students to understand the role of theory in guiding and informing research design and methods. The aim of the course is to give students the tools to conceptualize their thesis or dissertation work.
Prereqs: ED 574 or Permission
ED 590 Data Analysis and Interpretation of Qualitative Research (3 credits)
This course builds and expands on ED 589 Theoretical Applications and Design of Qualitative Research and is designed for graduate students who intend to conduct qualitative research. This course is an advanced seminar to assist in developing skills in data analysis and the presentation of qualitative research findings. It will focus on contemporary discourse among qualitative researchers concerning the analysis of qualitative data. Theoretical foundations learned in ED 574 and ED 589 will be revisited as participants examine the ways in which theory informs and guides analysis and interpretation. Assignments are designed to facilitate the interaction between data, analysis, writing, and the literature.
Prereqs: ED 574 and ED 589
ED 591 Indigenous and Decolonizing Research Methods (3 credits)
In this course, students will explore the historic and current discourse in Indigenous and Decolonizing Research. From an interdisciplinary perspective, students will analyze knowledge production through histories of Indigenous persistence and resistance to colonial power. Course content will expose students to methodologies grounded in the lived experiences and histories of individuals and communities marginalized by the colonial legacy, and will seek to engage students in research which invigorates connections, struggles, and knowledges to reflect reciprocal benefit to communities beyond the academy.
ED 592 Decolonizing, Indigenous, and Action-Based Research Methods (3 credits)
Decolonizing, Indigenous, and Action-based Research Methods are forms of social justice inquiry used to engage deeply in questions of educational equity. Through study of research, methodology, and theory, this course interrogates and contributes to current thinking on social justice issues and social justice education practices. Goals of this course include: understanding the theoretical foundations of critical and action-based theories in research, the role of reflexivity, and approaches to research as social change; examining the impact of colonization on social science and educational research; exploring the impacts of Indigenous, minoritized, and community-based epistemologies on research methodologies; developing areas of inquiry, approaches to data collection, analysis and interpretation of data, and an action plan for change.
ED 595 Survey Design for Social Science Research (3 credits)
This course focuses on the design and development of the survey instrument. Topics include how to word questions, validation, development of appropriate scales, traditional and alternative modes of survey administration; impacts of non-response; the effect of question structure, wording and context of instrument items; and post-survey follow-up and data processing. Recommended Preparation: Foundations of Research course at graduate level.
| Course Number | Course Name | Credits |
|---|---|---|
| EDAD 570 | Methods of Educational Research | 3 |
EDAD 570 Methods of Educational Research (3 credits)
This course examines philosophical and theoretical frameworks, methodologies, strategies, techniques, and designs of educational research. The primary themes: (1) the integration of research with educational practices, (2) the role of diversity in the social/political context of research, and (3) the design and use of research in education.
| Course Number | Course Name | Credits |
|---|---|---|
| ENT 504 | Applied Bioinformatics | 3 |
ENT 504 (s) Special Topics (1-16 credits)
Credit arranged
| Course Number | Course Name | Credits |
|---|---|---|
| ENVS 511 | Data Wizardry in Environmental Sciences | 3 |
| ENVS 551 | Research Methods in the Environmental Social Sciences | 3 |
ENVS 511 Data Wizardry in Environmental Sciences (3 credits)
Joint-listed with ENVS 411
Data science skills are in demand across the full spectrum of careers in the environmental sciences. This course teaches programming and data science skills in the R programming language in the context of the interdisciplinary environmental sciences. Specific topics include planning for environmental data collection and analysis, basic introduction to environmental data analysis in R, environmental data exploration using graphs in R, environmental data exploration using basic statistical approaches in R, R programming, introduction to spatial data analysis in R, environmental data visualization via interactive web applications, and management of large environmental datasets in R. This course focuses on the development of practical skills and the application of skills through project-based learning. Additional work required for graduate credit. Typically Offered: Fall.
Prereqs: STAT 251
ENVS 551 Research Methods in the Environmental Social Sciences (3 credits)
Qualitative and quantitative social science data collection and analysis methods in the specific context of environmental research topics. Methods include interviews, focus groups and surveys, qualitative coding and statistical analysis, research co-production, and using spatial data.
Prereqs: One course or experience in basic statistics or Instructor Permission
| Course Number | Course Name | Credits |
|---|---|---|
| FOR 514 | Forest Biometrics | 3 |
| FOR 535 | Remote Sensing of Fire | 3 |
FOR 514 Forest Biometrics (3 credits)
This course provides a broad overview of forest biometrics, including forestry-specific sampling approaches, development of allometric relations, and use of remote sensing datasets.
Prereqs: STAT 431 or equivalent
FOR 535 Remote Sensing of Fire (3 credits)
Joint-listed with FOR 435
The course describes the state of the art algorithms and methods used for mapping and characterizing fire from satellite observations. The course will link the physical aspects of fire on the ground with the quantities that can be observed from remote sensing, and present an overview of the different aspects of environmental fire monitoring. The course will be accompanied by weekly lab sessions focused on the processing of satellite data from sensors used operationally for fire monitoring. This course assumes that you are familiar with the fundamental concepts of mathematics and physics, understand basic remote sensing techniques, and can use maps and GIS data layers. For graduate credit, additional literature review and a class project including evaluation of new, advanced technologies is required. (Spring)
Prereqs: FOR 375 or Permission
| Course Number | Course Name | Credits |
|---|---|---|
| GEOG 507 | Spatial Statistics and Modeling | 3 |
| GEOG 583 | Remote Sensing/GIS Integration | 3 |
GEOG 507 Spatial Analysis and Modeling (3 credits)
Joint-listed with GEOG 407
Introduces the basic theories and methods of spatial analysis used for statistical modeling and problem solving in human and physical geography. The special nature of spatial data (point, continuous, and lattice) in the social and physical sciences is emphasized. Topics include point pattern analysis, spatial autocorrelation analysis, spatial multivariate regression, local indicators of spatial association, and geographically weighted regression. Extra oral and/or written assignments required for graduate credit. Cooperative: open to WSU degree-seeking students.
Prereqs: STAT 431 or permission
GEOG 583 Remote Sensing IMAGE ANALYSIS/GIS Integration (3 credits)
Joint-listed with GEOG 483
Concepts and tools for the processing, analysis, and interpretation of digital images from satellite and aircraft-based sensors. The integration of remotely sensed date and the other spatial data types within Geographic Information Systems. Additional assignments and exams required for graduate credit. Two lectures and 2 hours of lab per week. Cooperative: open to WSU degree-seeking students. Typically Offered: Spring.
Coreqs: GEOG 385 or FOR 375 or equivalent
| Course Number | Course Name | Credits |
|---|---|---|
| MATH 538 | Stochastic Models | 3 |
MATH 538 Stochastic Models (3 credits)
Joint-listed with MATH 453 and STAT 453
Markov chains, stochastic processes, and other stochastic models; applications. Additional projects/assignments required for graduate credit. Cooperative: open to WSU degree-seeking students.
Prereqs: MATH 451 or Permission.
| Course Number | Course Name | Credits |
|---|---|---|
| MIS 555 | Data Management for Big Data | 3 |
MIS 555 Data Management for Big Data (3 credits)
Joint-listed with MIS 455
Introduction to big data and the various data models related to managing “Big Data” and very large datasets. Emphasis will be on developing NOSQL data management systems. Additional topics may include data access, data analytics, and data visualization. Additional projects/assignments required for graduate credit.
| Course Number | Course Name | Credits |
|---|---|---|
| NRS 578 | LIDAR and Optical Remote Sensing Analysis Using Open-Source Software | 3 |
NRS 578 LIDAR and Optical Remote Sensing Analysis (3 credits)
Joint-listed with NRS 478
LIDAR and optical remote sensing data play a key role in natural resource and environmental research and management. Students will use open-source software to efficiently and effectively work with optical and LIDAR remote sensing datasets. Topics include introduction to open-source software for LIDAR and optical remote sensing analysis, acquisition and pre-processing of optical and LIDAR remote sensing data, and remote sensing analysis approaches that allow conversion of remotely sensed data into management/research relevant information. This course focuses on development and application of practical skills through project-based learning. For graduate credit, primary literature review, discussion, and a class project including evaluation and writeup of unique and advanced datasets is also required.
Prereqs: STAT 251 and WLF 370; or STAT 427 and NRS 472 or FOR 472
| Course Number | Course Name | Credits |
|---|---|---|
| POLS 558 | Research Methods for Local Government and Community Administration | 3 |
POLS 558 Research Methods for Local Government and Community Administration (3 credits)
This course will provide research tools to students interested in local and community administration. Topics will include research design, inferential statistics, regression analysis, binary dependent variable modeling with application to policy analysis and performance measurement, and program evaluation.
Prereqs: STAT 251
| Course Number | Course Name | Credits |
|---|---|---|
| REM 507 | Landscape and Habitat Dynamics | 3 |
REM 507 Landscape and Habitat Dynamics (3 credits)
Students explore landscape change occurring a variety of spatial and temporal scales, including global change, succession, disturbance events, and change induced by humans. Via scientific readings, models and spatial analysis students will learn how to quantify landscape change and how a change in environmental conditions and disturbance regimes may affect the composition of landscapes, specifically plant and animal habitats. Recommended Preparation: courses in ecology, statistics, and GIS. (Fall, alt/years)
Prereqs: Permission
| Course Number | Course Name | Credits |
|---|---|---|
| STAT 431 | Statistical Analysis | 3 |
| STAT 514 | Nonparametric Statistics | 3 |
| STAT 516 | Applied Regression Modeling | 3 |
| STAT 517 | Statistical Learning and Predictive Modeling | 3 |
| STAT 519 | Multivariate Analysis | 3 |
| STAT 535 | Introduction to Bayesian Statistics | 3 |
| STAT 555 | Statistical Ecology | 3 |
| STAT 565 | Computer Intensive Methods | 3 |
STAT 431 Statistical Analysis (3 credits)
Concepts and methods of statistical research including multiple regression, contingency tables and chi-square, experimental design, analysis of variance, multiple comparisons, and analysis of covariance. Cooperative: open to WSU degree-seeking students.
Prereqs: STAT 251 or STAT 301
STAT 514 Nonparametric Statistics (3 credits)
Joint-listed with STAT 414
Conceptual development of nonparametric methods including one, two, and k-sample tests for location and scale, randomized complete blocks, rank correlation, and runs test. Permutation methods, nonparametric bootstrap methods, density estimation, curve smoothing, robust and rank-based methods for the general linear model, and comparison. Comparison to parametric methods. Additional coursework/project required for graduate credit. Typically Offered: Varies. Cooperative: open to WSU degree-seeking students.
Prereqs: STAT 431
STAT 516 Applied Regression Modeling (3 credits)
Joint-listed with STAT 436
Statistical modeling and analysis of scientific date using regression model including linear, nonlinear, and generalized linear regression models. Topics also include analysis of survival data, censored and truncated response variables, categorical response variables, and mixed models. Emphasis is on application of these methods through the analysis of real data sets with statistical packages. Additional coursework/projects required for graduate credit.
Prereqs: STAT 431
STAT 517 Statistical Learning and Predictive Modeling (3 credits)
Joint-listed with STAT 417
A comprehensive overview of statistical learning and predictive modeling techniques to analyze large data sets in science, social science, and other data-rich fields including, for example, biology, business, and engineering. Topics include regression, classification, resampling methods, model selection and regularization, tree-based methods, support vector machines, clustering, and text mining. The implementation of the methods will be in R and Python as needed. Basic experience with computer programming is assumed. Additional coursework/project required for graduate credit. Typically Offered: Fall.
Prereqs: STAT 431
STAT 519 Multivariate Analysis (3 credits)
Joint-listed with STAT 418
The multivariate normal, Hotelling’s T2, multivariate general linear model, discriminant analysis, covariance matrix tests, canonical correlation, and principle component analysis. Additional coursework/project required for graduate credit. Typically Offered: Spring. Cooperative: open to WSU degree-seeking students.
Prereqs: STAT 431
STAT 535 Introduction to Bayesian Statistics (3 credits)
Joint-listed with STAT 435
Exploring the basics of Bayesian thinking with a comparative approach to interpretations of probability. Statistical methods, Bayesian approach to statistical inference. Methods include point and interval estimation under the Normal model, and inference under hierarchical models with emphasis on statistical model building. Computational methods, applications of methods useful for sampling posterior distributions such as rejection sampling, importance sampling, and Markov Chain Monte Carlo. Additional coursework/project required for graduate credit. Typically Offered: Varies.
Prereqs: STAT 431 or equivalent
STAT 555 Statistical Ecology (3 credits)
Cross-listed with WLF 555
Stochastic models in ecological work; discrete and continuous statistical distributions, birth-death processes, diffusion processes; applications in population dynamics, population genetics, ecological sampling, spatial analysis, and conservation biology. Cooperative: open to WSU degree-seeking students. (Spring, alt/years)
Prereqs: MATH 451 or Permission
STAT 565 Computer Intensive Statistics (3 credits)
Numerical stability, matrix decompositions for linear models, methods for generating pseudo-random variates, interactive estimation procedures (Fisher scoring and EM algorithm), bootstrapping, scatterplot smoothers, Monte Carlo techniques including Monte Carlo integration and Markov chain Monte Carlo. Cooperative: open to WSU degree-seeking students. (Alt/years)
Prereqs: STAT 451, STAT 452, MATH 330, and computer programming experience or Permission
| Course Number | Course Name | Credits |
|---|---|---|
| WLF 552 | Ecological Modeling | 3 |
| WLF 555 | Statistical Ecology | 3 |
WLF 552 Ecological Modeling (3 credits)
Theory and practice of modeling individuals, populations, and communities in heterogenous environments. Construction of spatially-explicit and aspatial models of individual behavior, fitness, population regulation, metapopulation dynamics, and species interactions. Analysis of stability, population viability, harvest, and conservation interventions. Computer-intensive use of R and MATLAB for simulation and parameter estimation. In consultation with instructor, each student will independently develop a novel model of their research system. Typically Offered: Fall (Odd Years).
Prereqs: Statistics 431 and Math 160 or permission Cooperative: open to WSU degree-seeking students.
WLF 555 Statistical Ecology (3 credits)
Cross-listed with STAT 555
Stochastic models in ecological work; discrete and continuous statistical distributions, birth-death processes, diffusion processes; applications in population dynamics, population genetics, ecological sampling, spatial analysis, and conservation biology. Cooperative: open to WSU degree-seeking students. (Spring, alt/years)
Prereqs: MATH 451 or Permission
| Course Number | Course Name | Credits |
|---|---|---|
| WR 552 | Water Economics and Policy | 3 |
WR 552 Water Economics and Policy Analysis (3 credits)
Joint-listed with AGEC 452, AGEC 452
This course will provide students with an in-depth look at the role of economics in water resource planning. Topics will include an introduction to water law, common concepts in hydrology, and the tools necessary to evaluate irrigation and other water use decisions. The course will focus on economic theory and a practical background of water resource management, as such, significant time will be spent developing the tools most frequently utilized by water resource economists. This includes Linear Programming, Cost/Benefit Analysis, Residual Imputation methods, Regression Analysis, Input-Output Modeling, Survey Design and Implementation, and Cost of Avoidance Techniques. Additional work required for graduate credit. Typically Offered: Spring. Cooperative: open to WSU degree-seeking students.