Data Science

Graduate Certificate Program - Data Science Foundations

The Graduate Certificate in Data Science Foundations recognizes competency in data science techniques including: predictive modeling, data mining, information management and data analytics.

Course Work Summary

It is expected that students seeking enrollment in this program will have sufficient foundational skills and aptitude in computer programming, statistical analysis, information systems, and databases. The required foundational skills may have been obtained through formal academic qualifications, work experience, or a combination of the two.

Current Students

Required Course

 

UN 5550 - Introduction to Data Science

Introduces concepts and skills fundamental to Data Science including: getting data, data wrangling, exploratory data analysis, basic statistics, data visualization, data modeling, and learning. The course introduces data science from different perspectives: computer science, mathematics, business, engineering, and more.

  • Credits: 3.0
  • Lec-Rec-Lab: (2-0-3)
  • Semesters Offered: Fall, Spring
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate; Must be enrolled in one of the following Major(s): Data Science

Elective Courses

Two courses from the list of three courses must be selected:


CS 5631 - Data Visualization

Introduction to scientific and information visualization. Topics include methods for visualizing three-dimensional scalar and vector fields, visual data representations, tree and graph visualization, large-scale data analysis and visualization, and interface design and interaction techniques.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Fall, Spring
  • Pre-Requisite(s): CS 4611 or CS 5611

CS 5841 - Machine Learning

This course will explore the foundational techniques of machine learning. Topics are pulled from the areas of unsupervised and supervised learning. Specific methods covered include naive Bayes, decision trees, support vector machine (SVMs), ensemble, and clustering methods.

  • Credits: 3.0
  • Lec-Rec-Lab: (3-0-0)
  • Semesters Offered: Spring
  • Restrictions: Permission of instructor required; May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
  • Pre-Requisite(s): CS 4821

CS 5471 - Computer Security

This covers fundamentals of computer security. Topics include practical cryptography, access control, security design principles, physical protections, malicious logic, program security, intrusion detection, administration, legal and ethical issues.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Fall, Spring
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate
  • Pre-Requisite(s): CS 3411 or CS 4411

FW 5083 - Programming Skills for Bioinformatics

Students will learn computer programming skills in Perl for processing genomic sequences and gene expression data and become familiar with various bioinformatics resources.

  • Credits: 3.0
  • Lec-Rec-Lab: (3-0-0)
  • Semesters Offered: Fall - Offered alternate years beginning with the 2017-2018 academic year
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate

MA 4710 - Regression Analysis

Covers simple, multiple, and polynomial regression; estimation, testing, and prediction; weighted least squares, matrix approach, dummy variables, multicollinearity, model diagnostics and variable selection. A statistical computing package is an integral part of the course.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Fall
  • Pre-Requisite(s): MA 2710 or MA 2720 or MA 3710 or MA 3715 or MA 5701

MA 5770 - Bayesian Statistics

The theory of Bayesian inference. Topics include prior specifications, basics of decision theory, Markov chain, Monte Carlo, Bayes factor, linear regression, linear random effects model, hierarchical models, Bayesian hypothesis testing, Bayesian model selection.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Fall - Offered alternate years beginning with the 2016-2017 academic year
  • Pre-Requisite(s): MA 4330 and MA 4710 and MA 4760

MA 5781 - Time Series Analysis and Forecasting

Statistical modeling and inference for analyzing experimental data that have been observed at different points in time. Topics include models for stationary and non stationary time series, model specification, parametric estimation, model diagnostics and forecasting, seasonal models and time series regression models.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Spring
  • Pre-Requisite(s): (MA 2710 or MA 2720 or MA 3710 or MA 3715 or MA 5701) and (MA 3720 or EE 3180 or MA 4700)

MGT 4600 - Management of Technology and Innovation

Introduces disruptive innovation concepts and provides occasions for their application to timely and relevant cases. Provides an understanding of technology management and innovation processes as they occur inside and outside of organizations.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Fall, Spring
  • Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore

PSY 5210 - Advanced Statistical Analysis and Design I

An overview of data analysis methods including visualization, data programming, and univariate statistics such as t-test and ANOVA.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-2-2)
  • Semesters Offered: Fall - Offered alternate years beginning with the 2018-2019 academic year
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate

SAT 5114 - Artificial Intelligence in Healthcare

This course introduces students to clinical data and artificial intelligence (A1) methods in healthcare. Health AI topics such as risk prediction, imaging, natural language processing of clinical text, and the integration of AI into the clinical environment are covered.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Fall, Spring - Offered alternate years beginning with the 2019-2020 academic year
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate

UN 5390 - Scientific Computing

Set in a Linux environment, course offers exposure to Foss tools for developing computational and visualization workflows. Students will learn to translate problems into programs, understand sources of errors, and debug, improve the performance of and parallelize the code.

  • Credits: 3.0
  • Lec-Rec-Lab: (3-0-0)
  • Semesters Offered: Fall, Spring
  • Restrictions: Permission of instructor required; Must be enrolled in one of the following Level(s): Graduate