Curriculum Overview for the Data Analytics and Insights in Animal Health Graduate Certificate

This Data Analytics and Insights in Animal Health Graduate Certificate is designed to meet a long-standing industry need in the animal health industry. Certificate course offerings and experiences are designed to equip students with the tools needed to approach consumer problems, navigate product challenges, and ideate new solutions in an interdisciplinary manner.

 

AAI 795 - Topics: Shared Concerns in Human and Animal Health-Related Data Analytics

2 Credit Hours

Faculty Member/Instructor: Gerald Wyckoff, Ph.D.

The wide array of data that is now available for both humans and companion and farm animals has not, on the whole, been organized to allow easy interoperability of such data. However, there is a clear set of problems that should be addressed with paired data from across species. While the interoperability problems within human health data have been well-studied, considering genomic, proteomic, and other data across human and animal data sets in combination with health data opens an entirely new set of problems for researchers. Recognizing the pitfalls of this data and understanding how to organize this data for successful analysis in hypothesis-driven frameworks, is the goal of this course.

 

AAI 852 - Vaccinology

3 Credit Hours

Faculty Member/Instructor: Paige Adams, Ph.D., D.V.M.

This multidisciplinary course is designed to provide the student with an understanding of vaccine development (including technology) from conceptualization through development, testing, and utilization. This course will specifically cover topics on the principles of pathogenesis of infectious diseases, immune protection, and eliciting protective immunity by vaccination; application of traditional and new technologies to vaccine development; principles of pre-clinical development, manufacturing, and clinical evaluation of a vaccine; importance of the regulatory process to vaccine development; and utilization of epidemiology to measure vaccine need and effectiveness.

 

AAI 860 - Introduction to Systems and Data in Animal Health

2 Credit Hours

Faculty Member/Instructor: Douglas Shane, Ph.D., D.V.M.

The field of Animal Health data analytics is comprised of the application of data systems, data computing, and data programming. This course is focused on data systems and data acquisition within animal health systems. This is an interdisciplinary course for students interested in data insights and digital technologies in animal health that explores life cycle and data collection systems encountered in Animal Health for both production and companion species, including the realities of data acquisition, data types, and analytics that may exist within those systems.

 

AAI 861 - Research Strategies to Drive Development of Animal Health Products

2 Credit Hours

Faculty Member/Instructor: Haley Larson, Ph.D.

Develop a working knowledge of the product development pipeline and continuous discovery techniques using both research and market insights as drivers for innovation in the Animal Health industry.

 

AAI 862 - Applied Data Analytics in Animal Health

3 Credit Hours

Faculty Member/Instructor: Douglas Shane, Ph.D., D.V.M.

Selection of appropriate analytic methods can vary between industries and projects — particularly when contrasting disciplines of mathematics and animal health. This course aims to enhance students proficiency to problem solve effective selection of analytical approaches for data types unique to the animal health industry. Students will develop and demonstrate the ability to generate a biological-based hypothesis or endpoint of interest relevant to an available data source, develop an analytic plan, execute an appropriate analysis, and report the findings. Students will apply existing knowledge of animal health and analytics to effectively work in interdisciplinary teams to execute an analytics project applied to animal health. Students will use programs such as Python.

 

AAI 863 - Special Topics: Data Analytics and Insights in Animal Health

1 Credit Hour

Faculty Member/Instructor: Douglas Shane, Ph.D., D.V.M.

This course is the final component of the Data Analytics and Insights in Animal Health Graduate Certificate program and is necessary for successful completion. Students will be required to demonstrate core certificate objectives: 1) Exhibit broad comprehension of animal health, 2) Demonstrate in depth understanding of programming and data management tools, 3) Apply appropriate mathematical methodologies to animal systems, and 4) Implement strategic use of data analytics for business applications, including animal health research and commercial insights. Demonstration of these knowledge and skills will occur through miniature assignments, culminating in a final report and presentation wherein the student will propose a new data analytic and insight project applied to the animal health industry. Students will describe the necessary components for project execution, requirements to develop the analytic tool or technology (e.g., studies, data, and methods), and how the analytic tool or technology will be used to enhance research, animal health and welfare, and/or drive business insights.

 

AAI 864 - Introduction to Computing for Animal Health

3 Credit Hours

Faculty Member/Instructor: Xuan Xu, Ph.D.

The field of Animal Health data analytics is comprised of applications of data systems, data computing, and data programming. The focus of this course is on data computing and programming as applied to animal health data. The purpose of this course is to introduce students to the application of essential data-driven approaches to tackle the challenges facing animal health. The use of computational approaches is considered to be more valuable in helping scientists study specific hypotheses in animal health. The Introduction to Computing for Animal Health course will focus on intuitive computational methods to address animal health questions with interdisciplinary philosophy. Students will understand the analysis workflows and computational methods that unravel and translate complicated data from animal health into a comprehensive demonstration.

 

AAI 865 - Assessing Effectiveness of Animal Therapeutics and Vaccines

1 Credit Hour

Faculty Member/Instructor: Paige Adams, Ph.D., D.V.M.

This interdisciplinary course is designed for students working with data derived from animal health systems to expand their knowledge and understanding of the pathophysiology of common animal diseases. The aim of this course is not only to familiarize students with animal disease processes but also to provide basic understanding of the principles behind the use of various clinical laboratory assays in the diagnosis of disease, including their use in assessing the efficacy of drugs and vaccines. Laboratory data from published animal studies will be used to apply principles discussed during the course.

 

AAI 867 - Zoonotic Pathogens in the Food Chain

2 Credit Hours

Faculty Member/Instructor: Haley Larson, Ph.D.

By examining the development, spread and transmission of zoonotic diseases through the food chain from animals through production processes to humans, this course will review ways modern food production systems contribute to the risk of zoonotic diseases, and where mitigation strategies need to be focused. Covering factors in the animal production process and attributes of microorganisms that allow potential contamination of food sources, this course will discuss pathogens that have recently emerged as important infections, and new trends in animal production, such as organic livestock farming and raw milk consumption. (Cross listed with DMP 880/FDSCI 961)

 

AAI 870 - Seminar: Applied Statistics for Animal Industry Seminar

1 Credit Hour

Faculty Member/Instructor: Haley Larson, Ph.D.

The course is designed to provide students an opportunity to actively discuss how foundational statistics concepts are used to analyze animal experiments, and how choices made when selecting a statistical model influences the biological conclusions that can be drawn. This course will provide students with a comprehension of analytical decisions that need to be considered when balancing statistical model integrity with practically significant conclusions on animal responses. Students will gain this insight through a series of interdisciplinary seminars as well as student led class discussions on statistical methodology of pre-selected animal experiment case studies.

 

ASI 650 - Identification and Data Management of Food Animals

2 Credit Hours

Faculty Member/Instructor: Dale Blasi, Ph.D.

Procedures and the conventions required to accomplish individual identification for farm animals. Principles of sound data collection and management. Principles of automatic information and data capture technologies for transforming data into information. Guest lectures from allied industry and livestock producers will illustrate various products, services and applications.

 

DMP 710 - Introduction to One Health

2 Credit Hours

Faculty Member/Instructor: Haley Larson, Ph.D.

One Health encompasses the complex interrelationships among humans, animals, and the environment. This online course provides a broad introduction to One Health, incorporating original videos of leading experts, case studies, and scientific readings. It addresses zoonotic diseases and environmental issues that impact human, animal, and ecosystem health.

 

DMP 754 - Introduction to Epidemiology

3 Credit Hours

Faculty Member/Instructor: Robert Larson/Douglas Shane, Ph.D., D.V.M.

The purpose of this course is to introduce students to the basic principles and methods of epidemiology in order to recognize and understand how disease affects populations (and the associated implications for individuals). This course will prepare students to use epidemiologic methods to solve current and future challenges to diagnose, treat, prevent, and control disease during their professional training and throughout their career. (Cross listed with MPH 754)

 

MATH 896 - Topics in Mathematics: Fundamentals of Biomathematical Methods

4 Credit Hours

Faculty Member/Instructor: Majid Jaberi-Douraki, Ph.D.

Problems in computational science and physical and biological sciences have increasingly been utilizing sophisticated mathematical techniques. As a result, the gap between the mathematical sciences and other disciplines has heavily been bridged with recent development in the interdisciplinary field of mathematical biology. Our principal goal for this course is to teach students from mathematical sciences, physiology, and health sciences how to model their problems using dynamical sciences. The modeling is mostly done with continuous system of ordinary and partial differential equations (ODEs and PDEs); however, they will be approximated using standard and nonstandard finite difference numerical techniques. The emphasis will be on fundamentals of mathematical modeling, i.e., model construction, continuous population models, models for interacting populations, and dynamics of infectious diseases.

 

MKTG 880 - Applied Marketing Analytics

3 Credit Hours

Faculty Member/Instructor: Jaebeom Suh, Ph.D.

Business Intelligence is a systematic approach to harnessing customer data and competitive to drive strategic business decision making. This course deals with how to collect and analyze business data to enhance the quality of decision making in modern enterprises. Unlike courses based on data mining (inductive approach), this course will be largely based on regression techniques (deductive approach). The course will be based on lectures, case analysis, and hands-on exercises to make students comfortable with power computing tools used for data analysis. The cases and exercises will be bundled with data which will be used to apply concepts learned in class to real business situations.

 

STAT 703 - Introduction to Statistical Methods for the Sciences

3 Credit Hours

Faculty Member/Instructor: TBA

Statistical concepts and methods applied to experimental and survey research in the sciences; test of hypotheses, parametric and rank tests; point estimation and confidence intervals; linear regression; correlation; one-way analysis of variance; contingency tables, chi-square tests.

 

STAT 705 - Regression and Analysis of Variance

3 Credit Hours

Faculty Member/Instructor: Christopher Vahl, Ph.D.

Simple and multiple linear regression, analysis of covariance, correlation analysis, one-, two- and three-way analysis of variance; multiple comparisons; applications including use of computers; blocking and random effects.