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 CreditsNPre-req: BI-105, BI-106, BI-121, BI-122, CH-142,CH-143, CH-242, CH-243 

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 Credits PRE-REQ: 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 

Topics covered will include basic and advanced topics in transmission, quantitative and population genetics, with emphasis on analysis. the methods that modern researchers use to discover gene function and molecular basis of adaptive or disease traits and how they are transmitted over generations in model and non-model species. 

4 Credits Prereqs: BI 105/BI 106 or BI 101/102 or one year of college Biology.

This class will provide training in advanced modern molecular wet lab, statistical and/or informatics tools. Bioinformatics skills will be related to assembly, annotation, variant characterization, and/or comparison of eukaryotic genomes and populations. Statistical analyses will be performed in R. Molecular tools may include DNA and RNA isolation, electrophoresis, restriction digests, DNA isolation from gels, PCR, sequencing, next generation sequencing and equipment maintenance. Core bioinformatics learning objectives will receive special attention. General skills include training students in the process and procedures of conducting meaningful and responsible research in Biology, including: deriving research objectives, experimental design, problem solving skills, responsible conduct.

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.