Master of Bioinformatics

Bioinformatics is the science of collecting and analyzing complex biological data. It is an interdisciplinary field that develops and applies methods and software tools for understanding biological data. 

4 CreditsN 

A graduate-level introduction to Computer Science Fundamentals through a focus on the Python language. Students will complete the data science boot camp (a weekend in-person intensive or online equivalent) at the start of this online course.

4 Credits

A comprehensive approach to the study of cells, with emphasis on molecular techniques and understanding the primary literature. Analysis of the cell at the molecular level emphasizes a unity in the principles by which cells function. 

4 CreditsPrerequisite: BS degree in molecular biology, biochemistry or the permission of the instructor

Overview of basic statistical techniques including descriptive statistics, hypothesis testing, and regression.

3 Credits

A graduate level introduction to data science through a focus on the language R. Support tools and libraries such as Rstudio and the tidyverse will be emphasized. Students will complete the data science boot camp (a weekend in person intensive or online equivalent) at the start of this online course.

4 Credits

Selected topics of discrete mathematics and linear algebra related to data science analysis techniques and algorithms.

3 Credits

A graduate-level introduction to retrieving, cleaning, and visualizing data from widely varied sources and formats. The student will use common data science languages and tools for extraction, transformation, loading and visualizing data sets. Project presentations will have an emphasis on communication skills. Tableau visualization tools and Python libraries are used.

3 Credits

Multivariate statistical techniques including multivariate regression, logistic regression, and dimension reduction techniques. Students will get hands-on experience applying the topics covered to real datasets using R, a powerful and popular open-source statistical computing language.

3.00 CreditsPrereqs: DS-516 and DS-520.

This course considers the use of machine learning (ML) and data mining (DM) algorithms for the data scientist to discover information embedded in wide-ranging datasets, from the simple tables to complex data sets and big data situations. Topics include ML and DM techniques such as classification, clustering, predictive and statistical modeling using tools such as R, Python, Matlab, Weka and others.

3.00 CreditsPrerequisite: DS-500, DS-510, or by permission

This course focuses on database design and relational structures, data warehousing and access through SQL. Students will use SQL to create and pull data from database systems. NoSQL and data warehousing are also covered to give students the necessary background in database systems. 

3 Credits Pre-Req: DS-510

This course considers the management and processing of large data sets, structured, semi-structured, and unstructured. The course focuses on modern, big data platforms such as Hadoop and NoSQL frameworks. Students will gain experience using a variety of programming tools and paradigms for manipulating big data sets on local servers and cloud platforms. 

3 Credits Prerequisite: DS-500 or DS-510 

This course is designed to introduce students to the principles and practices of effective grant writing. Through a blend of lectures, workshops, and peer-review sessions, students will learn how to develop compelling research proposals, effectively communicate their research ideas, and navigate the grant submission and review process.

3 Credits

This course offers a comprehensive introduction to the principles and mechanisms underlying heritability and genetic variation. Students will explore Mendelian genetics, DNA structure and replication, gene expression, and the role of genetics in evolution, medicine, and society. This will be done through a combination of lectures, discussion, and recitation.

4 Credits

Molecular ecology delves into the interface of molecular biology and ecology, focusing on the application of molecular techniques to address ecological questions. Students will explore the genetic processes that underlie ecological phenomena and will learn how molecular tools can inform our understanding of speciation, adaptation, behavior, and conservation.

3 Credits

Medical Genetics will focus upon the studies in Kish Valley over the past 10 years. Students will learn the methods by which experts in the field analyze whole exomes, Propionic Acidemia, and secondary variants that may affect heart failure & arrhythmia and poor neurological outcomes. We will evaluate how genetic tests have been used to define the common and rare disorders. Students will learn how to calculate Minor Allele Frequencies, Carrier and Disease rates. We will also cover technologiesincluding Newborn Screening, Targeted Gene Mutation Tests, InVitae, and Gene-Dx Gene Panels, Plain-Insight-Profiles, and Whole Exomes sequencing technologies that are used to screen for diseases in this population.

3 Credits

Genomics is an exciting and ever advancing field. Through a mixture of traditional lectures and novel research-based laboratories students will learn about both the theoretical and practical applications of genomics within biology and across other scientific fields.

4 Credits

This interactive course is tailored to provide students with the essential computational tools to analyze large biological datasets and extract meaningful results using a novel biological dataset. Students will also learn to write a scientific manuscript that will be prepared for publication by the end of the course.

3 Credits

This course provides an in-depth exploration of the principles, techniques, and applications of biotechnology. Students will explore the fundamental concepts of molecular biology and the real-world impact and ethical considerations of biotechnology in various fields, including healthcare, agriculture, and the environment. Students will interface with professionals in the field and work toward professional development for the biotech industry.

3 Credits

1-4 Credits 

This course will utilize Microbial Community Analysis leveraging high-throughput sequencing technology to identify the microbes present in naturally occurring our man-made ecosystems. Students will learn both molecular and bioinformatics skill sets, as well as microbial ecology principles throughout this course.

4 CreditsN 

This culminating experience provides graduate students with the opportunity to engage in an independent, hands-on research experience for an entire semester. The research experience can be with private industry, academia, or the government and must be approved first by Dr. Lamendella. The experience must be immersive in bioinformatics and/or biotechnology, must have a data analysis component and the research project will be disseminated via both a written manuscript and oral presentation.

4 Credits  

Data science practicum requiring completion of a large-scale analysis project of a given data set. Written and oral communication skills emphasized.

3 CreditsPrerequisites: DS-500, DS-510, DS-516, and DS-520, or instructor permission.