Focusing on topics such as water safety and quality, wastewater management, and disease transmission, the course offers a comprehensive exploration of the importance of advanced data science in shaping public health strategies. Through hands-on experiences with microbiological and -Omics data, students will develop skills to interpret complex data sets effectively.
Athena Title
Wastewater Data Science
Prerequisite
Permission of department
Semester Course Offered
Offered spring
Grading System
A - F (Traditional)
Student learning Outcomes
Students will be able to describe and discuss some of the most important environmental and public health challenges we face in water and sanitation across the globe, including the commonly used methods to approach these issues such as experimental design and implementation, data analysis tools, computational modeling, and statistical assessment.
Students will be able to identify gaps in the application of bioinformatics to actionable approaches for real-world scenarios. This will also result in learning to search, review, and assess relevant cutting edge journal articles.
Students will be able to interpret metagenomics and metatranscriptomics data sets enabling them to make informed inferences about microbial community dynamics, potential pathogenicity, and overall environmental health impacts.
Students will be able to describe and appraise connections of quantitative microbial and -Omics data sets by applying techniques such as quantitative microbial risk assessment and machine learning for predictive analysis.
Students will be able to design and complete a final project with -Omics data of their own or using a given data set to demonstrate their understanding of innovative approaches to water and sanitation problems.
Topical Outline
Introduction to water and sanitation related issues across the globe
Commonly used methods for tackling these issues and related challenges:
o Experimental design and implementation
o Sample processing
o Data analysis- quantitative and qualitative
o Data application- risk assessments, modeling
Global public health ethics and responsibilities
Current strengths of bioinformatics applications in public health
o Broad-brush approach to environmental sampling
o Identification of unexpected targets
Current weakness of bioinformatics applications in public health and potential solutions
o Overcoming proportion data (relative abundance)
o Recovery accuracy, particularly for amplicon-based sequencing
o Reproducibility (lack of replication)
Hypothesis testing using omics data.
Determining viability and infectivity using omics data
Achieving absolute quantification using omics data
Integrating omics data for standard quantitative microbial risk assessment
Integrating omics data, quantitative data, and covariates using AI/machine learning for risk prediction
Discussion- is a catch-all using bioinformatics possible for quantitative application?
Final semester project: students will utilize the tools and knowledge gained throughout the semester to produce a quantitative analysis and actionable solutions from bioinformatics data sets.
