This course may span 1 or 2 semesters and constitutes the major independent research project of the student's program. The project should involve 2-3 months of data collection summary, and analysis but will also require some background preparation.
Read more about "ANSC*6900 Major Paper in Animal and Poultry Science"
This course focuses on computational aspects of analytical techniques for biological data. Topics covered include, how to operate a computer efficiently when using biological data, types of biological data used in animal biosciences, how to prepare biological data for analysis, programming skills for bio-data organization, manipulation and problem solving, biodata visualization, and computational aspects of data modelling.
Major topics and methods in the systematic review of the scientific literature, meta-analysis and empirical modelling for animal sciences will be covered. Topics include strategies for systematically locating and extracting data from the literature, database development and management, model development and statistical analysis, model evaluation and scientific paper writing.
Read more about "ANSC*6550 Systematic Review & Meta-Analysis for Animal Biosciences"
The course will cover major topics in machine learning with applications to animal biosciences and related areas. This is a project-based course and it will have a computational component and a lab component focused on Python programming.
Read more about "ANSC*6100 Special Project – Machine Learning for Animal Biosciences"
Major topics and methods which fall within the scope of 'digital animal production' will be covered. Broad areas of focus include feed formulation, animal models and the application of machine learning within animal production systems.
Read more about "ANSC*4040 Digital Technologies for Animal Production Systems"
This course helps to foster graduate student career success in academia, government, or industry by enhancing student skills in scientific communication and knowledge dissemination and by providing opportunities for personal growth in a chosen area of professional development.
The course will cover major topics and methods in bioinformatics and computational biology for animal sciences. Topics include alignments, phylogenetics, genomics, data mining, databases, DNA, RNA and protein structures, DNA sequence analysis, data curation, pipeline construction and data visualization. This is a project-based course and will have a computational component and a lab component focused on bioinformatics programming.
Read more about "ANSC*6330 Topics in Computational Biology and Bioinformatics"
You will build a repertoire of modelling techniques from weekly assignments. Lectures will be used to discuss the results of the previous assignment(s) and introduce the methods for these subsequent piece of work. I will lead the discussion and lecture on modelling methodology. Each of you will obtain independence in model construction and analysis with a term project to develop, reproduce, test or use a model related to a research area of your interest.
This two-semester course focuses on statistical principles, experimental designs, and communication of findings to research peers within the agricultural field. Students apply statistical techniques and perform data analyses.
The MODNUT conference is a prominent event in the field of animal nutrition, focusing on the latest advancements and applications of mathematical models in nutrition research. It provides a platform for researchers, scientists, and industry professionals to share insights, methodologies, and innovations.
Read more about "Modelling Nutrient Digestion and Utilization in Farm Animals (MODNUT)"
