Take the following courses: 

DS-500  Data Science Fundamentals

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

DS-510  Computer Science Fundamentals

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

DS-516  Mathematics Fundamentals

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

3 Credits

DS-520  Statistics Fundamentals

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

3 Credits

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.

3 Credits

DS-570   Database Systems

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

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.


Complete 9 elective credits from the following courses:

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 computing language.

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

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 Credits Prerequisite: DS-500 or DS-510 

MBA-511 Quantitative Analysis & Research Methods

Quantitative Analysis and Research Methods will examine some of the principle analytical tools for decision-making in business and investigation in the social sciences.

3 Credits

BIN-500  Bioinformatics Fundamentals

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 

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.

4 Credits  

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.

4 CreditsN 

Program Credit Total = 30-32

Any course exception must be approved by Dr. Kim Roth.

Kimberly Roth

Kimberly Roth  Biography →

  • Professor of Mathematics

Kimberly Roth  Biography →

  • Professor of Mathematics