Ph.D. Tracks

The traditional track is designed to provide maximum flexibility for students wishing to design a particular course of study that does not fit within the framework of the four specialized tracks. As such, it is very important that the student, in conjunction with his/her advisor and the advisory committee, plan carefully a plan of study that provides a coherent set of courses supporting his/her career goals and objectives. By deliberating carefully and choosing wisely the student will construct a program that will serve him/her well for their entire professional career. 

Goals

1. To give the graduates of this program a widely based knowledge of the methods and theory of statistical science, preparing them to enter an academic environment or an applications-based field of their choosing.
2. To train academic and nonacademic research statisticians who can not only manage and interpret data from a wide variety of sources, but who can develop new methodologies for novel situations.

Traditional Track Requirements

While meeting the general course requirements for the Ph.D., Ph.D. students in the Traditional Track are required to take STAT 6105 Measure and Probability as one of the three elective 6000-level courses.

Modern statistical techniques tend to be highly algorithmically and computationally oriented. Abilities in numerical methods, computer programming, and algorithm construction and utilization are essential for the contemporary statistician, especially helpful for dealing with the “Big Data” problem. The Computational Track for the Ph.D. allows the student to specialize in these abilities and to prepare for a research career in computational statistics.

Goals

1. To give graduates of this program the fundamentals of modern statistical computational theory, methods and techniques.
2. To train students capable of developing new theory, methods, and techniques in computational statistics.

Computational Track Requirements

While meeting the general course requirements for the Ph.D. in statistics, the student in the Computational Track will have taken all of the following computationally oriented courses:

• STAT 5304: Statistical Computing
• STAT 5444: Bayesian Statistics
• STAT 5525: Data Analytics I
• STAT 5526: Data Analytics II
• STAT 6124: Stochastic Modeling and Inference

The topic(s) of the dissertation must be related to the Computational Track and must be approved by the student’s dissertation committee.

Goals

1. To give the graduates of this program an appropriate combination of statistical and industrial systems backgrounds so that they may have successful technical careers in industry or successful academic careers doing research in industrial statistics.
2. To train academic industrial statisticians who can serve as better bridges between the academic and corporate worlds. 

Industrial Track Requirements

While meeting the general course requirements for the Ph.D. in statistics, the student in the Industrial Track will have taken all of the following courses:

• STAT 5354 Structured Process Improvement
• STAT 5474 Statistical Quality Control
• STAT 5574 Response Surface Methodology I
• STAT 6504 Experimental Design and Analysis II
• STAT 6574 Response Surface Methodology II 
• STAT 6494 Statistical Quality Control II

Additionally, students are required to take three 5000-level courses in the Department of Industrial & Systems Engineering from the following list of courses, taken with consent from their advisor:

• ISE 5015-5016 Management of Change, Innovation, and Performance in Organizational Systems
• ISE 5124 Management of Quality & Reliability
• ISE 5134 Management Information Systems
• ISE 5204 Manufacturing Systems Engineering
• ISE 5405-5406 Optimization
• ISE 5424 Simulation
• ISE 5434 Economic Evaluation of Industrial Projects

Three different, suitable ISE courses can be chosen with approval from the advisor.

Upon completion of the M.S., or possibly later depending on the year of entry into the program, students are strongly encouraged to enter an extended internship program with one of the department’s corporate partners.

The topic(s) of the dissertation must be in the area of industrial statistics and approved by the student’s committee.

Goals

• To give the graduates of this program an appropriate combination of statistical and environmental systems backgrounds so that they may have successful technical careers in environmental organizations and companies or successful academic careers doing research in environmental statistics.
• To train academic environmental statisticians who can serve as better bridges between the academic and corporate worlds. 

Environmental Track Requirements

While meeting the general course requirements for the Ph.D. in statistics, the student in the Environmental Track will have taken all of the following courses:

• STAT 5304: Statistical Computing
• STAT 5504: Multivariate Statistical Methods
• STAT 5444: Bayesian Statistics
• STAT 5544: Spatial Statistics
• STAT 5414: Time Series Analysis I

Students in the Environmental Track are required to take three 6000-level courses in statistics in addition to STAT 6114 Advanced Inference.  The remaining three courses can currently be chosen, with consent from the advisor, from the following courses: STAT 6424 Multivariate Statistical Analysis, STAT 6514 Advanced Topics in Regression, STAT 6494 Advanced Bayesian Statistics, STAT 6504 Experimental Design and Analysis II, STAT 6404 Advanced Topics in Nonparametric Statistics, STAT 6414 Time Series Analysis II.

Also, the student will be expected to complete advanced course work in appropriate areas of concentration, to be chosen by the student in conjunction with his advisory committee. Thus, as an additional requirement, students are required to take three 5000-level courses in the electives from partnering departments. 

Possible courses are listed below.

Graduate Courses in Environmental Studies from different departments

Courses that may be used to satisfy requirements

Biological Sciences

• 5024: Population & Community Ecology
• 5034: Ecosystem Dynamics
• 5044: Aquatic Ecotoxicology
• 5054: Hazard Evaluation Of Toxic Chemicals

Civil and Environmental Engineering

• 5104: Environmental Chemistry
• 5714: Surface Water Quality Modeling
• 5184: Techniques For Environmental Analysis
• 5194: Environmental Engineering Microbiology
• 5204: Gis Applications In Civil Engineering
• 5214: Analysis Of Imaging Systems
• 5224: Adv. Gis Applications In Civil & Environmental Engr.
• 5324: Advanced Hydrology
• 5334: Analysis Of Water Resources Systems
• 5344: Environmental Systems Optimization
• 5354 (Geol 5814): Numerical Modeling of Groundwater
• 5364: Water Law

Biochemistry

• 4204: Biochemical Toxicology
• 5124: Probability Models In Agricultural Engineering
• 5144 (Cee 5064): Knowledge-Based Expert Systems
• 5304: Nonpoint Source Poll
• 5354: Nonpoint Source Pollution Modeling
• 4144: Biological Systems Simulation
• 4304: Nonpoint Source Pollution Modeling & Management

Crop & Soil Environmental Sciences

• 5634: Soil Chemistry
• 5694 (Biol 5694): Soil Biochemistry
• 4134: Soil Genesis & Classification
• 4734 (Ensc 4734): Environmental Soil Chemistry

Entomology 

• 4354 (Biol 4354): Aquatic Entomology
• 6164: Insecticide Toxicology
• 6254: Population Modeling Of Insect Systems

Fish and Wildlife Conservation

• 5214: Wildlife Population & Habitat Analysis
• 5224: Wildlife Population Dynamics
• 5734: Fisheries & Wildlife Planning
• 5514: Fish Population Dynamics & Modeling
• 5624: Fish Health

Forest Resources and Environmental Conservation

• 5104 (Geog 5104): Seminar In Remote Sensing & Geographic Information Systems
• 5214: Advanced Forest Inventory
• 5224: Forest Biometry
• 5254: Remote Sensing Of Natural Resources

Geography

• 5034: Analysis Of Spatial Data
• 5104 (For 5104): Seminar In Remote Sensing & Geographic Information Systems
• 5314: Advanced Spatial Analysis In Geographic Information Systems

Goals

1. To give the graduates of this program an adequate knowledge of statistical, computational and experimental methods, techniques and tools for the design and analysis of biological ‘omics’ (genomics, proteomics, metabolomics, phenomics) experiments, so that they may have successful academic careers in research institutions and universities or successful technical careers in industry.
2. To train academic and industrial research statisticians who can manage and interpret biological ‘omics’ data and who can work with scientists from various disciplines related to genetics, bioinformatics and biology.

Bioinformatics Track Requirements

While meeting the general course requirements for the Ph.D. in statistics, the student in the Bioinformatics Track will have taken all of the following courses:

• STAT 5504: Multivariate Statistical Methods
• STAT 5444: Bayesian Statistics
• STAT 5564: Statistical Genetics
• GBCB 5314: Paradigms for Bioinformatics
• CSES 5844: Plant Genomics
• GBCB 5004: Seminar in Genetics, Bioinformatics and Computational Biology (1credit)

In the case where not all of these courses can be offered, the Director of Graduate Programs in Statistics can grant dispensation for substitute courses. In the first year, students in this track are required to take all the M.S. core courses in statistics plus two more bioinformatics core courses (these can be taken later for students coming into the program with a M.S. degree obtained elsewhere). 

Additionally, students will take two courses from the following list of courses, taken with consent from their advisor:

• CS 5114: Theory of Algorithms
• CS 5124: Algorithms in Bioinformatics
• CS/Math 5485: Numerical Analysis and Software
• CS/Math 5486: Numerical Analysis and Software
• CS 5614: Database Management Systems
• CS 5804: Introduction to Artificial Intelligence
• CS 6104: Algorithms in Structural Bioinformatics
• CS 6104: Systems Biology and Drug Discovery
• CS 6604: Data Mining
• Math 5515/16: Continuous / Discrete Mathematical Models

Two different, suitable courses can be chosen with approval from the advisor.

Lastly, students in the Bioinformatics Track are required to take three 6000-level courses in statistics in addition to STAT 6114 Advanced Inference. The remaining three courses can currently be chosen, with consent from the advisor, from the following courses: STAT 6424 Multivariate Statistical Analysis, STAT 6514 Advanced Topics in Regression, STAT 6494 Advanced Bayesian Statistics, STAT 6504 Experimental Design and Analysis, STAT 6404 Advanced Topics in Nonparametric Statistics, STAT 6414 Time Series Analysis II, STAT 6105 Measure and Probability.

Students in the Bioinformatics Track will be required to take only one section of STAT 5984 Special Topics in Statistics instead of two.

The topic(s) of the dissertation must be related to the Bioinformatics track and must be approved by the dissertation committee members. The committee should consist of five faculty members including at least one member from outside of the Department of Statistics with expertise in genetics, bioinformatics and computational biology.

Goals

1. To provide Graduate Students the opportunity to collaborate on sports analytics problems at the University and beyond.
2. To allow Graduate Students to develop metrics and analytical predictive models in order to forecast team and game performances.
3. To train Graduate Students on collecting and maintaining high dimensional sports data for future analyses.
    

Sports Analytics Track Requirements

A student in this concentration area must satisfy the general course requirements for the PhD in statistics. 

Suggested graduate courses in the Department of Statistics:

• STAT 5304: Statistical Computing
• STAT 5314: Monte Carlo Methods in Statistics
• STAT 5364: Hierarchical Modeling
• STAT 5444: Bayesian Statistics
• STAT 5544: Spatial Statistics
• STAT 5414: TIme Series Analysis I
• STAT 5504: Multivariate Statistical Methods
• STAT 5514: Topics in Regression
• STAT 5525: Data Analytics I
• STAT 5534: Analysis of Multivariate Catagorical Data
• STAT 6124: Stochastic Modeling and Inference
• STAT 6424: Multivariate Statistical Analysis
• STAT 6494: Advanced Bayesian Statistics
• STAT 6414: Time Series Analysis II
• STAT 6514: Advanced Topics in Regression

The topic(s) of the dissertation must be related to Sports Analytics and must be approved by the student's dissertation committee.