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M.S. IN DATA SCIENCE

UTA’s Master of Science degree in Data Science helps meet the growing need for data science professionals in STEM, health-related and other fields, expanding students’ marketable skills and preparing them to enter a fast-growing job field.

The program is unique for its breadth, offering a widely encompassing set of courses that allows students without a computer science background to develop the necessary skills for data science jobs in business, engineering, healthcare and science settings. It aims to instill the acumen needed to draw insights from data, to make sound decisions using data, and to effectively communicate about data-driven findings and decisions.

Students will use real-world problems, methods and data in instruction materials through collaboration with community partners; problem-based, experiential learning which emphasizes hands-on coding exercises; service learning in which students learn while doing for social good; and inclusive learning to broaden student participation and strengthen student retention in data science education.

The degree requires 30 credit hours and can be completed in two years. After completion of a set of core courses, students choose to pursue one of five tracks: engineering, biology, geology, mathematics, or psychology. They will participate in a required capstone experience which can be integrated with workplace projects. Elective options offer additional flexibility to craft a degree that fits his or her specific situation.

Students will come from different backgrounds, but the most important factor for their growth and success will be a keen interest in learning the powerful ways in which data can be applied in various fields.

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M.S. IN DATA SCIENCE

ADMISSION REQUIREMENTS

Admission to the MSDS program is based on the applicant's perceived ability to do graduate work in data science as shown by the applicant’s test scores, transcripts, and other application documents.

To begin the program, an applicant must submit a completed application and fee to the UTA Graduate Admissions Office. When all application materials have been collected by Graduate Admissions, the information is forwarded to the program directors for evaluation. The admission decision is then communicated to Graduate Admissions, with the final decision sent via email from Graduate Admissions to the applicant.

If there is a delay in receiving materials, the application may be deferred until all required materials are available. The applicant is notified of the deferral by the Graduate Admissions Office via email.

Present requirements for the MSDS program include:

1. An undergraduate degree, preferably in natural and/or physical sciences, technology, engineering or mathematics (STEM) or related field, or in other fields if the applicant has professional work experience in STEM related fields.
2. A 3.0 grade point average (on a 4.0 scale) on the last two years of undergraduate coursework. In particular, performance on STEM-related courses is emphasized.
3. A sum of verbal plus quantitative scores of at least 300 on the GRE, with GRE quantitative score of at least 155 and GRE verbal score of at least 145.
4. International applicants and applicants whose native language is not English will need to take the Test of English as a Foreign Language (TOEFL) and score at least 80 with no area score of less than 20, or take the International English Language Testing System (IELTS) exam and score at least 6.5 in all areas.

Notes:

• Currently, the GRE requirement is waived for all applicants, due to circumstances related to the pandemic.
• In general, an applicant who graduated with a Bachelor's degree from an accredited U.S. institution within the last five years with a GPA of 3.2 or better on a 4.0 scale, and/or who is currently conducting professional work in related fields should contact the MSDS graduate advisors about the possibility of a GRE waiver.
• Applications with significant deficiencies in college-level mathematics may be deferred/denied as determined by the MSDS program advisors.
• If an applicant has a bachelors or master's degree from an accredited  U.S. institution, the English Proficiency requirement on TOEFL/IELTS is waived. However, it is waived for admission purposes only. If the applicant wishes to be considered for possible funding as a Graduate Teaching Assistant (GTA) or have any teaching responsibility, the applicant must have a U.S. bachelor’s degree or a TOEFL speaking score of at least 23, or an IELTS speaking score of at least 7. A master’s degree from a U.S. institution does not suffice for a waiver of the English proficiency requirement for international applicants desiring consideration for GTA support. An applicant who does not achieve the stated English proficiency standards may be required to take the Graduate English Skills Program (GESP) qualifying exam upon arrival at UTA to determine the need for additional English language courses after admission.
• Only the following application documents are required: application, fee, transcripts from all higher education institutions attended, and GRE and TOEFL scores unless they are waived. The MSDS program neither requires nor reviews letters of recommendation, statements of purpose, or any other supplemental materials from applicants. 

COURSE REQUIREMENTS (30 HOURS)

Core Courses (12 hours)

DASC/CSE 5300	FOUNDATION OF COMPUTING ♦
DASC 5301/CSE 5332	DATA SCIENCE
DASC 5302/IE 5317	INTRODUCTION TO PROBABILITY AND STATISTICS
DASC 5303/IE 5315	DATA SCIENCE PROJECT MANAGEMENT

♦	A student is required to earn a ‘C’ or higher grade on DASC 5300/CSE 5300 to move forward in the program. A student who receives ‘D’ or lower may be allowed to repeat the course in order to fulfill the requirement. However, the repeated credit hours will not be counted toward the degree.

Specialization Courses (9 hours)

ENGINEERING TRACK, COMPUTER SCIENCE (CHOOSE 3) 

CSE 5330	DATABASE SYSTEMS
CSE 5334	DATA MINING
CSE 5335	WEB DATA MANAGEMENT
CSE 5360	ARTIFICIAL INTELLIGENCE I
CSE 5367	PATTERN RECOGNITION
CSE 5368	NEURAL NETWORKS
CSE 6332	CLOUD COMPUTING & BIG DATA
CSE 6363	MACHINE LEARNING
CSE 6367	COMPUTER VISION

ENGINEERING TRACK, INDUSTRIAL ENGINEERING (CHOOSE 3)

IE 5301	INTRODUCTION TO OPERATIONS RESEARCH ♦
IE 5303	QUALITY SYSTEMS
IE 5304	ADVANCED ENGINEERING ECONOMY ♦
IE 5322	SIMULATION AND OPTIMIZATION
IE 5323	AGENT BASED SIMULATION ♦

♦	Mandatory for students in the Industrial Engineering specialization

SCIENCE TRACK, BIOLOGY

BIOL 5340	BIOINFORMATICS
***TBD*** BIOL UPPER LEVEL 1
***TBD*** BIOL UPPER LEVEL 2

***NOTE: Previously required BIOL 5361 and 5362 are no longer available. Replacement courses which sufficiently fulfill the biology specialization requirements will be determined at a later date. 

SCIENCE TRACK, GEOLOGY

GEOL 5320	UNDERSTANDING GEOGRAPHIC INFORMATION SYSTEMS
GEOL 5322	GLOBAL POSITIONING SYSTEM
GEOL 5323	REMOTE SENSING FUNDAMENTALS

SCIENCE TRACK, MATHEMATICS (CHOOSE 3)

MATH 5314	EXPERIMENTAL DESIGN
MATH 5353	APPLIED LINEAR MODELS
MATH 5358	REGRESSION ANALYSIS
MATH 6310	FOUNDATION OF DATA SCIENCES
MATH 6311	OPTIMIZATION ON BIG DATA

SCIENCE TRACK, PSYCHOLOGY

PSYC 5407	EXPERIMENTAL DESIGN
PSYC 6349	PSYCHOMETRIC THEORY
PSYC 6355	MULTIVARIATE ANALYSIS

Electives (6 hours)

• Any specialization courses outside a student's own specialization OR others approved by program faculty.

Capstone Project (3 hours)

DASC 5309

DATA SCIENCE CAPSTONE PROJECT ♦

♦	This requirement can be fulfilled by equivalent course approved by the program director.

COURSES FROM OUTSIDE MSDS

Course requirements may be met using coursework from outside the MSDS program, as follows:

• The requirements for DASC 5300/CSE 5300 and DASC 5302/IE 5317 can be fulfilled by appropriate undergraduate computing-related and statistics-related courses, respectively. For DASC 5301/CSE 5332 and DASC 5303/IE 5315, external courses must be at the graduate level and cover all essential topics of the corresponding core course. The requirements for specialization courses can be fulfilled by an external course if (a) the external course is at the graduate level and covers all essential topics of the specialization course; and (b) given any other specialization course in the list, the student has either taken it or the course can be fulfilled by an external course, as in (a). See the chart above for the list of specialization courses in each track.
• The student must still meet the 30-hour degree requirement. In lieu of each core or specialization course that is to be fulfilled by an external course, the student must take an extra elective in addition to the two required electives. See above for requirements regarding electives.
• External courses must be vetted by and arrangements must be approved by an MSDS program advisor.

CURRICULUM SCHEDULE

• Students should meet the requirements of DASC 5300, DASC 5301, DASC 5302, before or in the same semester when they are enrolled in any other course for fulfilling MSDS degree requirements.

• Students should only be enrolled in DASC 5309 in their final semester.

• The specialization and elective courses can be taken in any order, as long as prerequisites are satisfied.

• It is recommended that DASC 5303 is taken prior to or concurrent with any specialization and elective course.

CORE COURSES

DASC 5300/CSE 5300. Foundation of Computing. 3 Hours.
Basics of programming, data structures, and algorithms. Introduction to databases and operating systems. Basics of discrete structures and computability. Course is used for the Master’s in Data Science degree program and certificate programs for non-CSE majors. It cannot be taken for credit towards any CSE degree.

DASC 5301/CSE 5332. Data Science. 3 Hours.
This inspirational course follows a data-science-for-all perspective that views data acumen as part of literacy. It aims to instill in students the data acumen, i.e., the basic skills to wrestle with data, to draw insights from data, to make sound decisions responsibly using data, and to effectively communicate about data-driven findings and decisions. Topics include 1) data management: data curation, preparation, model, and querying; 2) data description and visualization: exploratory data analysis, graphics, user interface and user experience design; 3) machine learning and knowledge discovery: supervised learning, unsupervised learning, pattern and knowledge extraction, deep learning, model evaluation and interpretation. Prerequisite: MATH1301 or MATH1302, or MATH1308, or MATH1426, or equivalent and permission of advisor.

DASC 5302/IE 5317. Introduction to Probability and Statistics. 3 Hours.
Topics include descriptive statistics, set theory, combinatorics, mathematical expectation, probability distributions, confidence interval estimation, regression analysis, analysis of variance, and design of experiments.  Prerequisite: MATH 1426 or equivalent and permission of advisor.

DASC 5303/IE 5315. Data Science Project Management. 3 Hours.
Management and control of multifaceted science and engineering projects. Coordination and interactions between client and various service organizations. Project manager selection. Typical problems associated with various phases of project life cycle. Case studies illustrate theories and concepts. Students will be expected to demonstrate an understanding of communication and collaboration, including workflow, reproducibility, codebase management, collaboration tools, oral and written communication, presentation and storytelling, and team management, as well as ethics, such as understanding bias, fairness, credibility and misinformation, security, privacy, and codes of conduct.

SPECIALIZATION COURSES

ENGINEERING TRACK, COMPUTER SCIENCE 

CSE 5330. DATABASE SYSTEMS. 3 Hours.
Database system architecture; management and analysis of files, indexing, hashing, and B+- trees; the relational model and algebra; the SQL database language; database programming techniques, database design using Entry-Relationship, extended E-R, and UML modeling; basics of normalization. Introduction to database security, query processing and transaction management.  Prerequisite: CSE 2320.

CSE 5334. DATA MINING. 3 Hours.
Preparing data for mining, using preprocessing, data warehouses and OLAP; data mining primitives, languages and system architecture; data mining techniques including association rule mining, classification/prediction and cluster analysis.

CSE 5335. WEB DATA MANAGEMENT. 3 Hours.
This course provides an in depth study of models, languages and techniques for large-scale Web data management in distributed and heterogeneous environments. Topics include: Web programming with an emphasis on Web data management, Web Services, semi-structured data, XML standards, modern Web search engines, web information systems, Web query languages, distributed computing, metadata management with RDF, and Semantic Web.

CSE 5360. ARTIFICIAL INTELLIGENCE I. 3 Hours.
Introduction to the methods, concepts and applications of artificial intelligence, including knowledge representation, search, theorem proving, planning, natural language processing, and study of AI programming languages.  Prerequisite: CSE 2320 and CSE 3315, or consent of instructor

CSE 5367. PATTERN RECOGNITION. 3 Hours.
Principles and various approaches of pattern recognition processes, including Bayesian classification, parametric/non-parametric classifier design, feature extraction for signal representation, and techniques for classification and clustering. Current issues in pattern recognition research will also be examine.  Prerequisite: CSE 2320, MATH 3313.

CSE 5368. NEURAL NETWORKS. 3 Hours.
Theoretical principles of neurocomputing. Learning algorithms, information capacity, and mapping properties of feedforward and recurrent networks. Different neural network models will be implemented and their practical applications discussed.  Prerequisite: CSE 5301 or consent of instructor.

CSE 6332. CLOUD COMPUTING & BIG DATA. 3 Hours.
The focus of this course is on data management techniques and tools for storing and analyzing very large volumes of data. Topics include: cloud computing; virtualization; distributed file systems; large data processing using Map-Reduce; data modeling, storage, indexing, and query processing for big data; key-value storage systems, columnar databases, NoSQL systems; big data technologies and tools; large-scale stream processing systems; data analytics frameworks; big data applications, including graph processing, recommendation systems, and machine learning.

CSE 6363. MACHINE LEARNING. 3 Hours.
A detailed investigation of current machine learning methods, including statistical, connectionist, and symbolic learning. Presents theoretical results for comparing methods and determining what is learnable. Current issues in machine learning research will also be examined.  Prerequisite: CSE 5301 or consent of instructor.

CSE 6367. COMPUTER VISION. 3 Hours.
Advanced techniques for interpretation, analysis, and classification of digital images. Topics include methods for segmentation, feature extraction, recognition, stereo vision, 3-D modeling, and analysis of time-varying imagery. Also taught as EE 6358.  Prerequisite: CSE 5301 or CSE 5360 or EE 5356 or EE 5357, and consent of instructor.

ENGINEERING TRACK, INDUSTRIAL ENGINEERING

IE 5301. INTRODUCTION TO OPERATIONS RESEARCH. 3 Hours.
A survey of quantitative methods to develop modeling and decision-making skills. Topics include linear programming, goal programming, the simplex and dual simplex algorithms, transportation and assignment problems, integer programming, network analysis, nonlinear programming, decision trees, Markov Chains, and queuing theory.  Prerequisites: IE 3301 or IE 5317 or equivalent.

IE 5303. QUALITY SYSTEMS. 3 Hours.
Principles and practices of industrial quality control. Topics include the Deming philosophy, process improvements, statistical process control, process capability analysis and product acceptance.  Prerequisite: IE 5317 or equivalent or IE 5318 concurrent.

IE 5304. ADVANCED ENGINEERING ECONOMY. 3 Hours.
Analysis of capital investments in engineering and technical projects. Topics include decision analysis methods, cash flows, revenue requirements, activity-based analysis, multi-attribute decisions, probabilistic analysis and sensitivity/risk analysis.  Prerequisite: graduate standing.

IE 5322. SIMULATION AND OPTIMIZATION. 3 Hours.
An in-depth study of discrete event simulation theory and practice. Optimization and search techniques used in conjunction with simulation experiments are introduced. A commercial simulation software application is used. Prerequisite: IE 5317 or equivalent or IE 5318 concurrent.

SCIENCE TRACK, BIOLOGY

BIOL 5340. BIOINFORMATICS. 3 Hours.

This course is an applied introduction to bioinformatics and computational genomics. The course is geared toward the student with a biology background and limited programming experience. The course provides an entrance to commonly used programming/scripting languages and an introduction to numerous aspects of modern genomic data analyses (e.g. identification of coding and regulatory features in novel sequences, expression analysis, and comparative/phylogenetic analyses).

***TBD*** BIOL UPPER LEVEL 1

***TBD*** BIOL UPPER LEVEL 2

SCIENCE TRACK, GEOLOGY

SCIENCE TRACK, MATHEMATICS (CHOOSE 3)

MATH 5314. EXPERIMENTAL DESIGN. 3 Hours
This course covers the classical theory and methods of experimental design, including randomization, blocking, one-way and factorial treatment structures, confounding, statistical models, analysis of variance tables and multiple comparisons procedures.  Prerequisite: MATH 5305/STATS 5305 or MATH 5355/STATS 5355 or permission of instructor.

MATH 5353. APPLIED LINEAR MODELS. 3 Hours.
The course covers, at an operational level, three topics: 1) the univariate linear model, including a self-contained review of the relevant distribution theory, basic inference methods, several parameterizations for experimental design and covariate-adjustment models and applications, and power calculation; 2) the multivariate linear model, including basic inference (e.g. the four forms of test criteria and simultaneous methods), applications to repeated measures experiments and power calculation; and 3) the univariate mixed model, including a discussion of the likelihood function and its maximization, approximate likelihood inference, and applications to complex experimental designs, missing data, unbalanced data, time series observations, variance component estimation, random effects estimation, power calculation and a comparison of the mixed model's capabilities relative to those of the classical multivariate model. Knowledge of the SAS package is required.  Prerequisite: MATH 5358/STATS 5358 (Regression Analysis) or equivalent.

MATH 5358. REGRESSION ANALYSIS. 3 Hours.
A comprehensive course including multiple linear regression, non-linear regression and logistic regression. Emphasis is on modeling, inference, diagnostics and application to real data sets. The course begins by developing a toolbox of methods via a sequence of guided homework assignments. It culminates with projects based on consulting-level data analysis problems involving stratification, covariate adjustment and messy data sets. Some knowledge of the SAS package is required.  Prerequisites: MATH 5312/STATS 5312 or MATH 5305/STATS 5305 with a B or better or permission of the instructor.

MATH 6310. FOUNDATION OF DATA SCIENCES. 3 Hours.
Basic knowledge and computational methods in data sciences, select topics in norms, semidefinite matrix, nonnegative matrix, Cholesky decomposition, QR decompositions, linear system, least squares problem, eigenvalue and singular value decompositions, low rank approximation, nonnegative matrix factorization, introduction to simplex method, KKT conditions for optimizations, Krylov subspace methods, and applications.  Prerequisite: MATH 3330 or consent of the instructor.

MATH 6311. OPTIMIZATION ON BIG DATA. 3 Hours.
Introduction to big data analysis; real world applications of data science; linear system solutions; linear programming; duality theory; convex sets; convex functions; optimality conditions; unconstrained optimization; constraint optimization; conjugate direction methods; alternating direction method of multipliers; classification/regression models and algorithms; dimensionality reduction for visualization; projects on real data.  Prerequisite: MATH 3330 or consent of the instructor.

SCIENCE TRACK, PSYCHOLOGY

PSYC 5407. EXPERIMENTAL DESIGN. 4 Hours.

Statistical aspects of complex experimental designs used in psychological research. Prerequisite: PSYC 5406.