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.
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.
BIN-516 Molecular and Cellular Biology
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 CreditsPRE-REQ: BS degree in molecular biology, biochemistry or the permission of the instructor.
DS-520 Statistics Fundamentals
Overview of basic statistical techniques including descriptive statistics, hypothesis
testing, and regression.
DS-516 Mathematics Fundamentals
Selected topics of discrete mathematics and linear algebra related to data science
analysis techniques and algorithms.
DS-525 Data Acquisition & Visualization
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.
DS-530 Multivariate Techniques
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
3.00 CreditsPrereqs: DS-516 and DS-520.
DS-552 Data Mining
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 CreditsPre-Req: DS-510
DS-575 Big Data Techniques
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 CreditsPrerequisite: DS-500 or DS-510
BIN-560 Genetic Analysis
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 CreditsPrereqs: BI 105/BI 106 or BI 101/102 or one year of college Biology.
BIN-580 Advanced Research Methods
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.
BIN-600 Environmental Genomics
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.
BIN-581 Bioinformatics Capstone
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.
DS-580 Data Science Capstone
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.