Data Science, M.S.

Qiang Wu, Program Director
(615) 898-2053
Qiang.Wu@mtsu.edu

The Data Science program aims to prepare students to be competitive in the job market. The courses will equip students with advanced data science skills including, but not limited to, computer programming, data visualization and manipulation, predictive modeling, business analytics, and communications.

Admission Requirements

Admission to the Master of Science in Data Science program requires

1. an earned bachelor's degree from a regionally accredited university or college;
2. a GPA of 2.75 or higher in all college work taken.

Application Procedures

All application materials are to be submitted to the College of Graduate Studies.

Application deadline is July 31 for those wishing to begin the following Fall and December 31 for those wishing to begin the following Spring.

Applicant must

1. submit an application with the appropriate application fee (online at www.mtsu.edu/graduate/apply.php). Once this initial application has been accepted, the applicant will receive directions on how to enter the graduate portal to be able to submit other materials.
2. submit a resume or curriculum vitae;
3. submit official transcripts of all previous college work;
4. submit scores from an official Graduate Record Examination (GRE) or the Graduate Management Admission Test (GMAT). The GRE/GMAT requirement may be waived for applicants meeting any of the following conditions:
   1. an earned bachelor's degree from a regionally accredited college or university with a GPA of 3.00 or higher;
   2. an earned graduate or professional degree from a regionally accredited college or university.

International students who do not speak English as a native language should provide evidence of language proficiency. They must submit a Test of English as a Foreign Language score (TOEFL, www.ets.org/toefl, minimum score of 71 Internet based); International English Language Testing System score (IELTS, www.ielts.org, minimum score of 6.0); International Test of English Proficiency score (iTEP, www.itepexam.com, minimum score of 4.5); E.L.S. instruction (www.els.edu, completion of level 112), as a demonstration of English proficiency in order to be admitted to graduate studies at MTSU. Note that certain programs may require higher standards, so please consult the program coordinator for more information.

Order a course-by-course evaluation of transcript(s) from institutions outside the United States by an acceptable evaluation service. All acceptable evaluation services are listed at www.naces.org/members. Evaluations should be sent electronically to internationalgrad@mtsu.edu or, if necessary, mailed to College of Graduate Studies, MTSU Box 42, Murfreesboro, TN 37132.

Degree Requirements

Students may choose between a non-thesis option and a thesis option in the Master of Science in Data Science. The non-thesis option requires completion of 36 credit hours with a cumulative grade point average of 3.0 or above. The thesis option requires completion of 36-39 credit hours of course work with a grade point average of 3.0 or above, including enrollment of 3-6 credit hours thesis research in DATA 6640, and successful publication of a thesis with the graduate school.

Curriculum: Data Science

The following illustrates the minimum coursework requirements.

Non-thesis Option

Required Courses (24 hours)

• DATA 6300 - Data Understanding  3 credit hours  

  
  

  DATA 6300 - Data Understanding

  3credit hours

  Applications used to understand the problem-solving process for data science. Data collection and cleansing techniques used to visualize and summarize the data in order to prepare it for modeling for various data types through statistical analysis with Python programming.  

• DATA 6310 - Data Exploration  3 credit hours  

  
  

  DATA 6310 - Data Exploration

  3credit hours

  Prerequisite: DATA 6300. Data science techniques to explore numerical and text data. Unsupervised learning and NLP applications used to explore data to understand its impact and use to make data-driven decisions.  

• DATA 6320 - Predictive Modeling  3 credit hours  

  
  

  DATA 6320 - Predictive Modeling

  3credit hours

  Prerequisite: DATA 6300. Develop models to predict outcomes through the use of supervised learning techniques. Applications in regression and classification modeling used to develop data driven problem solving to predict and support decisions and analysis.  

• DATA 6330 - Model Optimization and Deployment  3 credit hours  

  
  

  DATA 6330 - Model Optimization and Deployment

  3credit hours

  Prerequisites: DATA 6310 and DATA 6320. The optimization and deployment of machine learning models. Techniques for fine-tuning parameters for developing the best model for the presented business problems. Applications through internal and cloud infrastructures also used to identify optimal techniques for deployment of models to operationalize into production.

• DATA 6500 - Cases in Data Science  3 credit hours  

  
  

  DATA 6500 - Cases in Data Science

  3credit hours

  Prerequisite: DATA 6320 with C (2.0) or better. Cases that integrate innovative data science techniques from various real-world problems and scenarios. Topics may include supervised and unsupervised learning, NLP, databases, SQL, NoSQL, cloud computing, and data ethics.

• DATA 6550 - Data Ethics and Responsibility  3 credit hours  

  
  

  DATA 6550 - Data Ethics and Responsibility

  3credit hours

  Prerequisites: DATA 6310 and DATA 6320. Issues and challenges associated with working with data, which includes ethics and bias, as well as data governance and regulatory requirements; privacy and other ethical issues related to data collection and selection; post-deployment feedback and model revisions.

• DATA 6990 - Topics Seminar in Data Science  3 credit hours  

  
  

  DATA 6990 - Topics Seminar in Data Science

  3credit hours

  Prerequisite: DATA 6500 with C (2.0) or better. Application of various data science skills with an emphasis on full-scale projects and oral presentations from initial business and data understanding, data cleansing, modeling and analysis, findings, and deployment.

• STAT 6020 - Applied Statistical Methods  3 credit hours  

  
  

  STAT 6020 - Applied Statistical Methods

  3credit hours

  Prerequisites: MATH 1530, MATH 2050, or STAT 3150 or permission of instructor. Contemporary and medical research methodology for biostatistics. Descriptive and inferential statistics including parametric and nonparametric hypothesis testing methods, sample size, statistical significance and power, survival curve analysis, relative risk, odds ratios, chi square modeling, and analysis of variance. Data will be analyzed using statistical software.

Elective Courses (12 hours)

• ACSI 6110 - Predictive Analysis  3 credit hours  

  
  

  ACSI 6110 - Predictive Analysis

  3credit hours

  Prerequisite: ACSI 5140 or consent of instructor. Topics include generalized linear models, logistic regression, discriminant analysis, support vector machines, ridge regression, lasso, sparse modeling, variable selection, model selection, and other selected topics from computational statistics, machine learning, and data mining.

• BIA 6905 - Applied Business Analytics  3 credit hours  

  
  

  BIA 6905 - Applied Business Analytics

  3credit hours

  (Same as MBAI 6905.) An applied approach to the understanding, development, and application of prescriptive and data analytic tools to model and analyze business data. A hands-on focus utilized with both commonly-used spreadsheet software and specialized business intelligence software for the student to develop skills for self-service business analytics.

• BIA 6910 - Business Intelligence  3 credit hours  

  
  

  BIA 6910 - Business Intelligence

  3credit hours

  Prerequisite: BIA 6905 or QM 6770 or equivalent. A more advanced look at the application of business intelligence tools to solving business problems. Coverage will include the development and deployment of sophisticated reporting and dashboard systems to monitor and manage operations. Industry-standard business intelligence software utilized.

• COMS 6100 - Fundamentals of Computational Science  3 credit hours  

  
  

  COMS 6100 - Fundamentals of Computational Science

  3credit hours

  Prerequisite: Admission to the Computational and Data Science Ph.D. program or permission of instructor. Foundational overview of the mathematical and scientific underpinnings of computational science. Introduces the principles of finding computer solutions to contemporary science challenges. Offers preparation for core and elective courses in the Ph.D. program in Computational Science by reviewing essential mathematical methods and basic science principles drawn from biology, chemistry, and physics. Special topics include techniques of high performance computing and applications, parallel systems, and theory of computation, case studies in computational chemistry, physics, and mathematical biology.

• COMS 6500 - Fundamentals of Scientific Computing  4 credit hours  

  
  

  COMS 6500 - Fundamentals of Scientific Computing

  4credit hours

  Prerequisite: Graduate standing or permission of instructor. Fundamentals of problem solving approaches in computational science, including computer arithmetic and error analysis, linear and nonlinear equations, least squares, interpolation, numerical differentiation and integration, optimization, random number generations and Monte Carlo simulation. Students will gain computational experience by analyzing case studies using modern software packages such as MATLAB.

• CSCI 5300 - Data Communication and Networks  3 credit hours  

  
  

  CSCI 5300 - Data Communication and Networks

  3credit hours

  Prerequisite: CSCI 3240 or CSCI 3250. Computer network architectures, protocol hierarchies, and the open systems interconnection model. Modeling, analysis, design, and management of hardware and software on a computer network.

• CSCI 5350 - Introduction to Artificial Intelligence  3 credit hours  

  
  

  CSCI 5350 - Introduction to Artificial Intelligence

  3credit hours

  Prerequisites: CSCI 3110 and CSCI 3080 or equivalent. Principles and applications of artificial intelligence. Principles include search strategies, knowledge representation, reasoning, and machine learning. Applications include expert systems and natural language understanding.

• CSCI 5560 - Database Management Systems  3 credit hours  

  
  

  CSCI 5560 - Database Management Systems

  3credit hours

  Prerequisites: CSCI 3080 and CSCI 3110. The relational and object models of database design along with relational algebras, data independence, functional dependencies, inference rules, normal forms, schema design, modeling languages, query languages, and current literature.

• CSCI 5850 - Neural Nets  3 credit hours  

  
  

  CSCI 5850 - Neural Nets

  3credit hours

  Prerequisite: CSCI 3080. Various neural net architectures, theory, and applications, including models such as Perceptron, back propagation, Kohonen, ART, and associative memory. Learning and conditioning methods also studied.

• CSCI 6020 - Data Abstraction and Programming Fundamentals  4 credit hours  

  
  

  CSCI 6020 - Data Abstraction and Programming Fundamentals

  4credit hours

  Prerequisites: Previous programming experience in a high-level language and consent of instructor. Advanced introduction to data abstraction, problem solving, and programming. Programming language concepts, recursion, program development, algorithm design and analysis, data abstraction, objects and fundamental data structures such as stacks, queues, and trees. Three hours lecture and two hours lab. Will not count toward a major or minor.

• CSCI 6050 - Computer Systems Fundamentals  4 credit hours  

  
  

  CSCI 6050 - Computer Systems Fundamentals

  4credit hours

  Prerequisite: CSCI 6020 or COMS 6100 with minimum grade of B or equivalent. Advanced introduction to computer systems. Data representations, computer arithmetic, machine-level representations of programs, program optimization, memory hierarchy, linking, exceptional control flow, virtual memory and memory management, basic network concepts, and basic concurrent concepts and programming. Three hours lecture and two hours lab. Will not count toward a major or minor unless approved by the department.

• CSCI 6100 - Analysis of Algorithms  3 credit hours  

  
  

  CSCI 6100 - Analysis of Algorithms

  3credit hours

  Prerequisites: CSCI 3080 and CSCI 3110 or consent of instructor. Topics include the analysis and design of algorithms; efficiency of algorithms; design approaches including divide and conquer, dynamic programming, the greedy approach, and backtracking; P and NP; and algorithms in many areas of computing.

• CSCI 6300 - Networks  3 credit hours  

  
  

  CSCI 6300 - Networks

  3credit hours

  Prerequisite: CSCI 4300 or CSCI 5300. Computer communications, network architectures, protocol hierarchies, and the open systems interconnection model. Modeling, analysis, and specification of hardware and software on a computer network. Wide area networks and local area networks including rings, buses, and contention networks.

• CSCI 6330 - Parallel Processing Concepts  3 credit hours  

  
  

  CSCI 6330 - Parallel Processing Concepts

  3credit hours

  Prerequisites: [CSCI 3130 and either (CSCI 3240 or CSCI 3250)] or CSCI 6050 and a working knowledge of either C or C++. Parallel processing and programming in a parallel environment. Topics include classification of parallel architectures, actual parallel architectures, design and implementation of parallel programs, and parallel software engineering.

• CSCI 6350 - Selected Topics in Artificial Intelligence  3 credit hours  

  
  

  CSCI 6350 - Selected Topics in Artificial Intelligence

  3credit hours

  Prerequisites: CSCI 3110 and CSCI 4350 or CSCI 5350. In-depth study of the principal areas of the field: artificial intelligence programming, problem-solving methods, knowledge representation methods, deduction and reasoning, and applications such as natural language processing and expert systems. Repeatable up to 6 hours.

• CSCI 6430 - Selected Topics in Parallel Processing  3 credit hours  

  
  

  CSCI 6430 - Selected Topics in Parallel Processing

  3credit hours

  Prerequisite: CSCI 4330 or CSCI 6330. An in-depth investigation of one or more topics in parallel processing. Topic(s) to be selected by the professor. Possible topics include parallel algorithms, parallel programming languages, parallel programming tools, parallel software engineering, parallel architectures, parallel applications, and parallel VLSI. Repeatable up to 6 hours.

• CSCI 6560 - Selected Topics in Database  3 credit hours  

  
  

  CSCI 6560 - Selected Topics in Database

  3credit hours

  Prerequisite: CSCI 4560 or CSCI 5560. An in-depth investigation of one or more topics in database. Topic(s) to be selected by the professor. Possible topics include object-oriented database systems, distributed database systems, client-server database systems, deductive databases, multimedia databases, and database theory (concurrency, query optimization, recovery, security). Repeatable up to 6 hours.

• CSCI 7300 - Scientific Visualization and Databases  3 credit hours  

  
  

  CSCI 7300 - Scientific Visualization and Databases

  3credit hours

  Prerequisites: CSCI 6020, COMS 6100, and COMS 6500 with minimum grade of B or equivalent or consent of instructor. Introduction to the concepts, theories, and applications of database and visualization methodologies for scientific data. Relational database design along with relational algebras, data independent, functional dependencies, inference rules, normal forms, schema design, modeling language, and query languages discussed. Methods corresponding to the visualization of scalar, vector, and tensor fields as well as multifield problem discussed. Database and visualization discussed in the context of scientific applications.

• CSCI 7350 - Data Mining  3 credit hours  

  
  

  CSCI 7350 - Data Mining

  3credit hours

  Prerequisite: Fundamental courses in the Computational Science Ph.D. program and CSCI 6020 or equivalent or consent of instructor. Introduction to concepts, theories, techniques, issues, and applications of data mining. Data preprocessing, association rule analysis, classification analysis, cluster and outlier analysis, deviation detection, statistical modeling, consideration of emergent technologies.

• DATA 6700 - Independent Study in Data Science  1 to 3 credit hours  

  
  

  DATA 6700 - Independent Study in Data Science

  1 to 3credit hours

  Prerequisite: DATA 6300 with C (2.0) or better. Assigned research or projects in the data science discipline under direct faculty supervision. Topics for intensive study chosen in joint consultation between student and instructor. 

• DATA 6910 - Internship in Data Science  1 to 3 credit hours  

  
  

  DATA 6910 - Internship in Data Science

  1 to 3credit hours

  Prerequisites: Data Science major; approval of program director; plan of activities with internship sponsor prior to registration. Practical experience related to data science. A minimum of 60 hours for each credit. Maximum 3 credits. Pass/Fail.

• ECON 6070 - Econometrics II  3 credit hours  

  
  

  ECON 6070 - Econometrics II

  3credit hours

  Second core course in econometrics for students pursuing an M.A. in Economics. Emphasizes methods of time series analysis, including Box-Jenkins methods, general-to-specific modeling, volatility models, vector autoregressions, unit roots and cointegration, unobserved component and state space models, and neural networks. Integrates practical applications in various computing environments including SAS, RATS, and MATLAB.

• MATH 6300 - Optimization  3 credit hours  

  
  

  MATH 6300 - Optimization

  3credit hours

  Prerequisite: MATH 5320 or consent of instructor. Constrained and unconstrained optimization problems, including the generalized least squares problem and Eigenvalue problems. Methods include orthogonalization, conjugate gradient, and quasi-Newton algorithms.  

• PGEO 5490 - Remote Sensing  4 credit hours  

  
  

  PGEO 5490 - Remote Sensing

  4credit hours

  Various vehicles of remote sensing such as radar, satellite imagery, and infrared data. Use of data in preparation of maps and applications to land use and environmental problems examined. Selection of data from either a numeric or image remote sensing system, interpreting, and developing a report from the interpretations. Three hours lecture and one two-hour laboratory per week.

• PGEO 5511 - Advanced Remote Sensing  3 credit hours  

  
  

  PGEO 5511 - Advanced Remote Sensing

  3credit hours

  Prerequisite: PGEO 5490 or PGEO 4490. Lecture and laboratory in the study of advanced topics in remote sensing, including but not limited to, active sensors (LiDAR and RADAR), hyperspectral, and spectroscopy. Three hours lecture/laboratory per week.

• PGEO 5530 - Geographic Information Systems  3 credit hours  

  
  

  PGEO 5530 - Geographic Information Systems

  3credit hours

  Lecture and laboratory work relative to computer-manipulated geographic data base. Laboratory work will involve experience in practical application of a geographic information system (GIS) to problem-solving. Student will take appropriate data and compile an environmental impact statement (EIS). Three hours lecture and two hours laboratory per week.

• PGEO 5560 - Intermediate Geographic Information Systems  3 credit hours  

  
  

  PGEO 5560 - Intermediate Geographic Information Systems

  3credit hours

  Prerequisite: PGEO 4530 or PGEO 5530. Lecture and laboratory work related to the principles and applications of geographic information systems (GIS). Continued training in GIS analysis including raster analysis, spatial analysis, network analysis, and geocoding. Data management including data editing, geodatabase design, and creation also examined. Other topics include resource management, demographic, and civic application. Three hours lecture per week.

• PGEO 6050 - Programming for Geospatial Database Applications  3 credit hours  

  
  

  PGEO 6050 - Programming for Geospatial Database Applications

  3credit hours

  Prerequisite: PGEO 5570. Development of custom/tailored GIS-based computer programming to analyze geospatial datasets for making inferences about the Earth's natural and human systems. Extend commercially available geographic information systems software packages through the development of novel computer programs to perform GIS tasks such as spatial analysis, data transformation, map generation, and geospatial database integration.

• PSY 6210 - Advanced Psychometrics  3 credit hours  

  
  

  PSY 6210 - Advanced Psychometrics

  3credit hours

  Prerequisites: PSY 6280, HHP 6700, or equivalent. Classical test theory and item response theory. Model, assumptions, and problems of classical test theory. Mathematical modeling, parameter estimating, and adaptive testing procedures using item response theory. Both theories utilized for test construction.

• PSY 6280 - Psychological Statistics: Regression  3 credit hours  

  
  

  PSY 6280 - Psychological Statistics: Regression

  3credit hours

  Prerequisite: PSY 3020 or equivalent or admission to Psychology graduate program. Corequisite: PSY 6281. Review of basic statistics; various correlation coefficients; multiple and partial correlation; simple and multiple regression. Laboratory included.

• PSY 6565 - Behavioral Statistics Using R  3 credit hours  

  
  

  PSY 6565 - Behavioral Statistics Using R

  3credit hours

  Prerequisite: PSY 4070 or PSY 6280/PSY 7280. Use of the R programming language to solve data management issues and to conduct basic and advanced statistical analyses.

• PSY 6575 - Multilevel Analysis  3 credit hours  

  
  

  PSY 6575 - Multilevel Analysis

  3credit hours

  Prerequisite: PSY 4070 or PSY 6280/PSY 7280. Use of multilevel modeling techniques to analyze data with complex data structure. 

• PSY 6580 - Multivariate Data Analysis  3 credit hours  

  
  

  PSY 6580 - Multivariate Data Analysis

  3credit hours

  Prerequisites: PSY 6280, HHP 6700, or equivalent. Surveys each of the major multivariate data analysis techniques, with main focus on their application. Nature, power, procedure, computer programming, interpretation, and limitations of each.

• STAT 5700 - Analysis of Large-Scale Data Sets  3 credit hours  

  
  

  STAT 5700 - Analysis of Large-Scale Data Sets

  3credit hours

  The analysis and applications of large-scale data sets. Scalable machine learning and data mining applications in a practical clinical environment. Statistical software used in the application of these techniques.

• STAT 7400 - Computational Statistics  3 credit hours  

  
  

  STAT 7400 - Computational Statistics

  3credit hours

  Prerequisites: COMS 6100 and MATH 2530 or equivalent. Statistical visualization and other computationally intensive methods. The role of computation as a fundamental tool of discovery in data analysis, statistical inference, and development of statistical theory and methods. Monte Carlo studies in statistics, computational inference, tools for identification of structure in data, numerical methods in statistics, estimation of functions (orthogonal polynomials, splines, etc.), statistical models, graphical methods, data fitting and data mining, and machine learning techniques.

Sample Program of Study

Fall Year 1

• DATA 6300 - Data Understanding  3 credit hours  

  
  

  DATA 6300 - Data Understanding

  3credit hours

  Applications used to understand the problem-solving process for data science. Data collection and cleansing techniques used to visualize and summarize the data in order to prepare it for modeling for various data types through statistical analysis with Python programming.  

• DATA 6310 - Data Exploration  3 credit hours  

  
  

  DATA 6310 - Data Exploration

  3credit hours

  Prerequisite: DATA 6300. Data science techniques to explore numerical and text data. Unsupervised learning and NLP applications used to explore data to understand its impact and use to make data-driven decisions.  

• STAT 6020 - Applied Statistical Methods  3 credit hours  

  
  

  STAT 6020 - Applied Statistical Methods

  3credit hours

  Prerequisites: MATH 1530, MATH 2050, or STAT 3150 or permission of instructor. Contemporary and medical research methodology for biostatistics. Descriptive and inferential statistics including parametric and nonparametric hypothesis testing methods, sample size, statistical significance and power, survival curve analysis, relative risk, odds ratios, chi square modeling, and analysis of variance. Data will be analyzed using statistical software.

Spring Year 1

• DATA 6320 - Predictive Modeling  3 credit hours  

  
  

  DATA 6320 - Predictive Modeling

  3credit hours

  Prerequisite: DATA 6300. Develop models to predict outcomes through the use of supervised learning techniques. Applications in regression and classification modeling used to develop data driven problem solving to predict and support decisions and analysis.  

• DATA 6330 - Model Optimization and Deployment  3 credit hours  

  
  

  DATA 6330 - Model Optimization and Deployment

  3credit hours

  Prerequisites: DATA 6310 and DATA 6320. The optimization and deployment of machine learning models. Techniques for fine-tuning parameters for developing the best model for the presented business problems. Applications through internal and cloud infrastructures also used to identify optimal techniques for deployment of models to operationalize into production.

• Elective 3 credit hours

Fall Year 2

• DATA 6500 - Cases in Data Science  3 credit hours  

  
  

  DATA 6500 - Cases in Data Science

  3credit hours

  Prerequisite: DATA 6320 with C (2.0) or better. Cases that integrate innovative data science techniques from various real-world problems and scenarios. Topics may include supervised and unsupervised learning, NLP, databases, SQL, NoSQL, cloud computing, and data ethics.

• DATA 6550 - Data Ethics and Responsibility  3 credit hours  

  
  

  DATA 6550 - Data Ethics and Responsibility

  3credit hours

  Prerequisites: DATA 6310 and DATA 6320. Issues and challenges associated with working with data, which includes ethics and bias, as well as data governance and regulatory requirements; privacy and other ethical issues related to data collection and selection; post-deployment feedback and model revisions.

• Elective 3 credit hours

Spring Year 2

• DATA 6990 - Topics Seminar in Data Science  3 credit hours  

  
  

  DATA 6990 - Topics Seminar in Data Science

  3credit hours

  Prerequisite: DATA 6500 with C (2.0) or better. Application of various data science skills with an emphasis on full-scale projects and oral presentations from initial business and data understanding, data cleansing, modeling and analysis, findings, and deployment.

• Electives 6 credit hours

Thesis Option

Required Courses (24 hours)

• DATA 6300 - Data Understanding  3 credit hours  

  
  

  DATA 6300 - Data Understanding

  3credit hours

  Applications used to understand the problem-solving process for data science. Data collection and cleansing techniques used to visualize and summarize the data in order to prepare it for modeling for various data types through statistical analysis with Python programming.  

• DATA 6310 - Data Exploration  3 credit hours  

  
  

  DATA 6310 - Data Exploration

  3credit hours

  Prerequisite: DATA 6300. Data science techniques to explore numerical and text data. Unsupervised learning and NLP applications used to explore data to understand its impact and use to make data-driven decisions.  

• DATA 6320 - Predictive Modeling  3 credit hours  

  
  

  DATA 6320 - Predictive Modeling

  3credit hours

  Prerequisite: DATA 6300. Develop models to predict outcomes through the use of supervised learning techniques. Applications in regression and classification modeling used to develop data driven problem solving to predict and support decisions and analysis.  

• DATA 6330 - Model Optimization and Deployment  3 credit hours  

  
  

  DATA 6330 - Model Optimization and Deployment

  3credit hours

  Prerequisites: DATA 6310 and DATA 6320. The optimization and deployment of machine learning models. Techniques for fine-tuning parameters for developing the best model for the presented business problems. Applications through internal and cloud infrastructures also used to identify optimal techniques for deployment of models to operationalize into production.

• DATA 6500 - Cases in Data Science  3 credit hours  

  
  

  DATA 6500 - Cases in Data Science

  3credit hours

  Prerequisite: DATA 6320 with C (2.0) or better. Cases that integrate innovative data science techniques from various real-world problems and scenarios. Topics may include supervised and unsupervised learning, NLP, databases, SQL, NoSQL, cloud computing, and data ethics.

• DATA 6550 - Data Ethics and Responsibility  3 credit hours  

  
  

  DATA 6550 - Data Ethics and Responsibility

  3credit hours

  Prerequisites: DATA 6310 and DATA 6320. Issues and challenges associated with working with data, which includes ethics and bias, as well as data governance and regulatory requirements; privacy and other ethical issues related to data collection and selection; post-deployment feedback and model revisions.

• DATA 6990 - Topics Seminar in Data Science  3 credit hours  

  
  

  DATA 6990 - Topics Seminar in Data Science

  3credit hours

  Prerequisite: DATA 6500 with C (2.0) or better. Application of various data science skills with an emphasis on full-scale projects and oral presentations from initial business and data understanding, data cleansing, modeling and analysis, findings, and deployment.

Elective Courses (9 hours)

• ACSI 6110 - Predictive Analysis  3 credit hours  

  
  

  ACSI 6110 - Predictive Analysis

  3credit hours

  Prerequisite: ACSI 5140 or consent of instructor. Topics include generalized linear models, logistic regression, discriminant analysis, support vector machines, ridge regression, lasso, sparse modeling, variable selection, model selection, and other selected topics from computational statistics, machine learning, and data mining.

• BIA 6905 - Applied Business Analytics  3 credit hours  

  
  

  BIA 6905 - Applied Business Analytics

  3credit hours

  (Same as MBAI 6905.) An applied approach to the understanding, development, and application of prescriptive and data analytic tools to model and analyze business data. A hands-on focus utilized with both commonly-used spreadsheet software and specialized business intelligence software for the student to develop skills for self-service business analytics.

• BIA 6910 - Business Intelligence  3 credit hours  

  
  

  BIA 6910 - Business Intelligence

  3credit hours

  Prerequisite: BIA 6905 or QM 6770 or equivalent. A more advanced look at the application of business intelligence tools to solving business problems. Coverage will include the development and deployment of sophisticated reporting and dashboard systems to monitor and manage operations. Industry-standard business intelligence software utilized.

• COMS 6100 - Fundamentals of Computational Science  3 credit hours  

  
  

  COMS 6100 - Fundamentals of Computational Science

  3credit hours

  Prerequisite: Admission to the Computational and Data Science Ph.D. program or permission of instructor. Foundational overview of the mathematical and scientific underpinnings of computational science. Introduces the principles of finding computer solutions to contemporary science challenges. Offers preparation for core and elective courses in the Ph.D. program in Computational Science by reviewing essential mathematical methods and basic science principles drawn from biology, chemistry, and physics. Special topics include techniques of high performance computing and applications, parallel systems, and theory of computation, case studies in computational chemistry, physics, and mathematical biology.

• COMS 6500 - Fundamentals of Scientific Computing  4 credit hours  

  
  

  COMS 6500 - Fundamentals of Scientific Computing

  4credit hours

  Prerequisite: Graduate standing or permission of instructor. Fundamentals of problem solving approaches in computational science, including computer arithmetic and error analysis, linear and nonlinear equations, least squares, interpolation, numerical differentiation and integration, optimization, random number generations and Monte Carlo simulation. Students will gain computational experience by analyzing case studies using modern software packages such as MATLAB.

• CSCI 5300 - Data Communication and Networks  3 credit hours  

  
  

  CSCI 5300 - Data Communication and Networks

  3credit hours

  Prerequisite: CSCI 3240 or CSCI 3250. Computer network architectures, protocol hierarchies, and the open systems interconnection model. Modeling, analysis, design, and management of hardware and software on a computer network.

• CSCI 5350 - Introduction to Artificial Intelligence  3 credit hours  

  
  

  CSCI 5350 - Introduction to Artificial Intelligence

  3credit hours

  Prerequisites: CSCI 3110 and CSCI 3080 or equivalent. Principles and applications of artificial intelligence. Principles include search strategies, knowledge representation, reasoning, and machine learning. Applications include expert systems and natural language understanding.

• CSCI 5560 - Database Management Systems  3 credit hours  

  
  

  CSCI 5560 - Database Management Systems

  3credit hours

  Prerequisites: CSCI 3080 and CSCI 3110. The relational and object models of database design along with relational algebras, data independence, functional dependencies, inference rules, normal forms, schema design, modeling languages, query languages, and current literature.

• CSCI 5850 - Neural Nets  3 credit hours  

  
  

  CSCI 5850 - Neural Nets

  3credit hours

  Prerequisite: CSCI 3080. Various neural net architectures, theory, and applications, including models such as Perceptron, back propagation, Kohonen, ART, and associative memory. Learning and conditioning methods also studied.

• CSCI 6020 - Data Abstraction and Programming Fundamentals  4 credit hours  

  
  

  CSCI 6020 - Data Abstraction and Programming Fundamentals

  4credit hours

  Prerequisites: Previous programming experience in a high-level language and consent of instructor. Advanced introduction to data abstraction, problem solving, and programming. Programming language concepts, recursion, program development, algorithm design and analysis, data abstraction, objects and fundamental data structures such as stacks, queues, and trees. Three hours lecture and two hours lab. Will not count toward a major or minor.

• CSCI 6050 - Computer Systems Fundamentals  4 credit hours  

  
  

  CSCI 6050 - Computer Systems Fundamentals

  4credit hours

  Prerequisite: CSCI 6020 or COMS 6100 with minimum grade of B or equivalent. Advanced introduction to computer systems. Data representations, computer arithmetic, machine-level representations of programs, program optimization, memory hierarchy, linking, exceptional control flow, virtual memory and memory management, basic network concepts, and basic concurrent concepts and programming. Three hours lecture and two hours lab. Will not count toward a major or minor unless approved by the department.

• CSCI 6100 - Analysis of Algorithms  3 credit hours  

  
  

  CSCI 6100 - Analysis of Algorithms

  3credit hours

  Prerequisites: CSCI 3080 and CSCI 3110 or consent of instructor. Topics include the analysis and design of algorithms; efficiency of algorithms; design approaches including divide and conquer, dynamic programming, the greedy approach, and backtracking; P and NP; and algorithms in many areas of computing.

• CSCI 6300 - Networks  3 credit hours  

  
  

  CSCI 6300 - Networks

  3credit hours

  Prerequisite: CSCI 4300 or CSCI 5300. Computer communications, network architectures, protocol hierarchies, and the open systems interconnection model. Modeling, analysis, and specification of hardware and software on a computer network. Wide area networks and local area networks including rings, buses, and contention networks.

• CSCI 6330 - Parallel Processing Concepts  3 credit hours  

  
  

  CSCI 6330 - Parallel Processing Concepts

  3credit hours

  Prerequisites: [CSCI 3130 and either (CSCI 3240 or CSCI 3250)] or CSCI 6050 and a working knowledge of either C or C++. Parallel processing and programming in a parallel environment. Topics include classification of parallel architectures, actual parallel architectures, design and implementation of parallel programs, and parallel software engineering.

• CSCI 6350 - Selected Topics in Artificial Intelligence  3 credit hours  

  
  

  CSCI 6350 - Selected Topics in Artificial Intelligence

  3credit hours

  Prerequisites: CSCI 3110 and CSCI 4350 or CSCI 5350. In-depth study of the principal areas of the field: artificial intelligence programming, problem-solving methods, knowledge representation methods, deduction and reasoning, and applications such as natural language processing and expert systems. Repeatable up to 6 hours.

• CSCI 6430 - Selected Topics in Parallel Processing  3 credit hours  

  
  

  CSCI 6430 - Selected Topics in Parallel Processing

  3credit hours

  Prerequisite: CSCI 4330 or CSCI 6330. An in-depth investigation of one or more topics in parallel processing. Topic(s) to be selected by the professor. Possible topics include parallel algorithms, parallel programming languages, parallel programming tools, parallel software engineering, parallel architectures, parallel applications, and parallel VLSI. Repeatable up to 6 hours.

• CSCI 6560 - Selected Topics in Database  3 credit hours  

  
  

  CSCI 6560 - Selected Topics in Database

  3credit hours

  Prerequisite: CSCI 4560 or CSCI 5560. An in-depth investigation of one or more topics in database. Topic(s) to be selected by the professor. Possible topics include object-oriented database systems, distributed database systems, client-server database systems, deductive databases, multimedia databases, and database theory (concurrency, query optimization, recovery, security). Repeatable up to 6 hours.

• CSCI 7300 - Scientific Visualization and Databases  3 credit hours  

  
  

  CSCI 7300 - Scientific Visualization and Databases

  3credit hours

  Prerequisites: CSCI 6020, COMS 6100, and COMS 6500 with minimum grade of B or equivalent or consent of instructor. Introduction to the concepts, theories, and applications of database and visualization methodologies for scientific data. Relational database design along with relational algebras, data independent, functional dependencies, inference rules, normal forms, schema design, modeling language, and query languages discussed. Methods corresponding to the visualization of scalar, vector, and tensor fields as well as multifield problem discussed. Database and visualization discussed in the context of scientific applications.

• CSCI 7350 - Data Mining  3 credit hours  

  
  

  CSCI 7350 - Data Mining

  3credit hours

  Prerequisite: Fundamental courses in the Computational Science Ph.D. program and CSCI 6020 or equivalent or consent of instructor. Introduction to concepts, theories, techniques, issues, and applications of data mining. Data preprocessing, association rule analysis, classification analysis, cluster and outlier analysis, deviation detection, statistical modeling, consideration of emergent technologies.

• DATA 6700 - Independent Study in Data Science  1 to 3 credit hours  

  
  

  DATA 6700 - Independent Study in Data Science

  1 to 3credit hours

  Prerequisite: DATA 6300 with C (2.0) or better. Assigned research or projects in the data science discipline under direct faculty supervision. Topics for intensive study chosen in joint consultation between student and instructor. 

• DATA 6910 - Internship in Data Science  1 to 3 credit hours  

  
  

  DATA 6910 - Internship in Data Science

  1 to 3credit hours

  Prerequisites: Data Science major; approval of program director; plan of activities with internship sponsor prior to registration. Practical experience related to data science. A minimum of 60 hours for each credit. Maximum 3 credits. Pass/Fail.

• ECON 6070 - Econometrics II  3 credit hours  

  
  

  ECON 6070 - Econometrics II

  3credit hours

  Second core course in econometrics for students pursuing an M.A. in Economics. Emphasizes methods of time series analysis, including Box-Jenkins methods, general-to-specific modeling, volatility models, vector autoregressions, unit roots and cointegration, unobserved component and state space models, and neural networks. Integrates practical applications in various computing environments including SAS, RATS, and MATLAB.

• MATH 6300 - Optimization  3 credit hours  

  
  

  MATH 6300 - Optimization

  3credit hours

  Prerequisite: MATH 5320 or consent of instructor. Constrained and unconstrained optimization problems, including the generalized least squares problem and Eigenvalue problems. Methods include orthogonalization, conjugate gradient, and quasi-Newton algorithms.  

• PGEO 5490 - Remote Sensing  4 credit hours  

  
  

  PGEO 5490 - Remote Sensing

  4credit hours

  Various vehicles of remote sensing such as radar, satellite imagery, and infrared data. Use of data in preparation of maps and applications to land use and environmental problems examined. Selection of data from either a numeric or image remote sensing system, interpreting, and developing a report from the interpretations. Three hours lecture and one two-hour laboratory per week.

• PGEO 5511 - Advanced Remote Sensing  3 credit hours  

  
  

  PGEO 5511 - Advanced Remote Sensing

  3credit hours

  Prerequisite: PGEO 5490 or PGEO 4490. Lecture and laboratory in the study of advanced topics in remote sensing, including but not limited to, active sensors (LiDAR and RADAR), hyperspectral, and spectroscopy. Three hours lecture/laboratory per week.

• PGEO 5530 - Geographic Information Systems  3 credit hours  

  
  

  PGEO 5530 - Geographic Information Systems

  3credit hours

  Lecture and laboratory work relative to computer-manipulated geographic data base. Laboratory work will involve experience in practical application of a geographic information system (GIS) to problem-solving. Student will take appropriate data and compile an environmental impact statement (EIS). Three hours lecture and two hours laboratory per week.

• PGEO 5560 - Intermediate Geographic Information Systems  3 credit hours  

  
  

  PGEO 5560 - Intermediate Geographic Information Systems

  3credit hours

  Prerequisite: PGEO 4530 or PGEO 5530. Lecture and laboratory work related to the principles and applications of geographic information systems (GIS). Continued training in GIS analysis including raster analysis, spatial analysis, network analysis, and geocoding. Data management including data editing, geodatabase design, and creation also examined. Other topics include resource management, demographic, and civic application. Three hours lecture per week.

• PGEO 6050 - Programming for Geospatial Database Applications  3 credit hours  

  
  

  PGEO 6050 - Programming for Geospatial Database Applications

  3credit hours

  Prerequisite: PGEO 5570. Development of custom/tailored GIS-based computer programming to analyze geospatial datasets for making inferences about the Earth's natural and human systems. Extend commercially available geographic information systems software packages through the development of novel computer programs to perform GIS tasks such as spatial analysis, data transformation, map generation, and geospatial database integration.

• PSY 6565 - Behavioral Statistics Using R  3 credit hours  

  
  

  PSY 6565 - Behavioral Statistics Using R

  3credit hours

  Prerequisite: PSY 4070 or PSY 6280/PSY 7280. Use of the R programming language to solve data management issues and to conduct basic and advanced statistical analyses.

• PSY 6575 - Multilevel Analysis  3 credit hours  

  
  

  PSY 6575 - Multilevel Analysis

  3credit hours

  Prerequisite: PSY 4070 or PSY 6280/PSY 7280. Use of multilevel modeling techniques to analyze data with complex data structure. 

• PSY 6580 - Multivariate Data Analysis  3 credit hours  

  
  

  PSY 6580 - Multivariate Data Analysis

  3credit hours

  Prerequisites: PSY 6280, HHP 6700, or equivalent. Surveys each of the major multivariate data analysis techniques, with main focus on their application. Nature, power, procedure, computer programming, interpretation, and limitations of each.

• PSY 6280 - Psychological Statistics: Regression  3 credit hours  

  
  

  PSY 6280 - Psychological Statistics: Regression

  3credit hours

  Prerequisite: PSY 3020 or equivalent or admission to Psychology graduate program. Corequisite: PSY 6281. Review of basic statistics; various correlation coefficients; multiple and partial correlation; simple and multiple regression. Laboratory included.

• PSY 6210 - Advanced Psychometrics  3 credit hours  

  
  

  PSY 6210 - Advanced Psychometrics

  3credit hours

  Prerequisites: PSY 6280, HHP 6700, or equivalent. Classical test theory and item response theory. Model, assumptions, and problems of classical test theory. Mathematical modeling, parameter estimating, and adaptive testing procedures using item response theory. Both theories utilized for test construction.

• STAT 5700 - Analysis of Large-Scale Data Sets  3 credit hours  

  
  

  STAT 5700 - Analysis of Large-Scale Data Sets

  3credit hours

  The analysis and applications of large-scale data sets. Scalable machine learning and data mining applications in a practical clinical environment. Statistical software used in the application of these techniques.

• STAT 7400 - Computational Statistics  3 credit hours  

  
  

  STAT 7400 - Computational Statistics

  3credit hours

  Prerequisites: COMS 6100 and MATH 2530 or equivalent. Statistical visualization and other computationally intensive methods. The role of computation as a fundamental tool of discovery in data analysis, statistical inference, and development of statistical theory and methods. Monte Carlo studies in statistics, computational inference, tools for identification of structure in data, numerical methods in statistics, estimation of functions (orthogonal polynomials, splines, etc.), statistical models, graphical methods, data fitting and data mining, and machine learning techniques.

Thesis Course (3-6 hours)

• DATA 6640 - Thesis Research  1 to 6 credit hours  

  (3 credit hours required)(3 credit hours required)  dotslash:(3 credit hours required) title:(3 credit hours required) 

  (3 credit hours required) 
  

  DATA 6640 - Thesis Research

  1 to 6credit hours

  Prerequisite: Consent of instructor. Enrollment in the Data Science master's program required. Selection of a research problem related to data science, review of pertinent literature, collection and analysis of data, and composition of thesis. Once enrolled in this course, student should register for at least one credit hour in this course each semester until the completion of a master thesis.

Sample Program of Study

Fall Year 1

• DATA 6300 - Data Understanding  3 credit hours  

  
  

  DATA 6300 - Data Understanding

  3credit hours

  Applications used to understand the problem-solving process for data science. Data collection and cleansing techniques used to visualize and summarize the data in order to prepare it for modeling for various data types through statistical analysis with Python programming.  

• DATA 6310 - Data Exploration  3 credit hours  

  
  

  DATA 6310 - Data Exploration

  3credit hours

  Prerequisite: DATA 6300. Data science techniques to explore numerical and text data. Unsupervised learning and NLP applications used to explore data to understand its impact and use to make data-driven decisions.  

• STAT 6020 - Applied Statistical Methods  3 credit hours  

  
  

  STAT 6020 - Applied Statistical Methods

  3credit hours

  Prerequisites: MATH 1530, MATH 2050, or STAT 3150 or permission of instructor. Contemporary and medical research methodology for biostatistics. Descriptive and inferential statistics including parametric and nonparametric hypothesis testing methods, sample size, statistical significance and power, survival curve analysis, relative risk, odds ratios, chi square modeling, and analysis of variance. Data will be analyzed using statistical software.

Spring Year 1

• DATA 6320 - Predictive Modeling  3 credit hours  

  
  

  DATA 6320 - Predictive Modeling

  3credit hours

  Prerequisite: DATA 6300. Develop models to predict outcomes through the use of supervised learning techniques. Applications in regression and classification modeling used to develop data driven problem solving to predict and support decisions and analysis.  

• DATA 6330 - Model Optimization and Deployment  3 credit hours  

  
  

  DATA 6330 - Model Optimization and Deployment

  3credit hours

  Prerequisites: DATA 6310 and DATA 6320. The optimization and deployment of machine learning models. Techniques for fine-tuning parameters for developing the best model for the presented business problems. Applications through internal and cloud infrastructures also used to identify optimal techniques for deployment of models to operationalize into production.

• Elective 3 credit hours

Fall Year 2

• DATA 6500 - Cases in Data Science  3 credit hours  

  
  

  DATA 6500 - Cases in Data Science

  3credit hours

  Prerequisite: DATA 6320 with C (2.0) or better. Cases that integrate innovative data science techniques from various real-world problems and scenarios. Topics may include supervised and unsupervised learning, NLP, databases, SQL, NoSQL, cloud computing, and data ethics.

• DATA 6550 - Data Ethics and Responsibility  3 credit hours  

  
  

  DATA 6550 - Data Ethics and Responsibility

  3credit hours

  Prerequisites: DATA 6310 and DATA 6320. Issues and challenges associated with working with data, which includes ethics and bias, as well as data governance and regulatory requirements; privacy and other ethical issues related to data collection and selection; post-deployment feedback and model revisions.

• DATA 6700 - Independent Study in Data Science  1 to 3 credit hours  

  
  

  DATA 6700 - Independent Study in Data Science

  1 to 3credit hours

  Prerequisite: DATA 6300 with C (2.0) or better. Assigned research or projects in the data science discipline under direct faculty supervision. Topics for intensive study chosen in joint consultation between student and instructor. 

Spring Year 2

• DATA 6640 - Thesis Research  1 to 6 credit hours  

  
  

  DATA 6640 - Thesis Research

  1 to 6credit hours

  Prerequisite: Consent of instructor. Enrollment in the Data Science master's program required. Selection of a research problem related to data science, review of pertinent literature, collection and analysis of data, and composition of thesis. Once enrolled in this course, student should register for at least one credit hour in this course each semester until the completion of a master thesis.

• DATA 6990 - Topics Seminar in Data Science  3 credit hours  

  
  

  DATA 6990 - Topics Seminar in Data Science

  3credit hours

  Prerequisite: DATA 6500 with C (2.0) or better. Application of various data science skills with an emphasis on full-scale projects and oral presentations from initial business and data understanding, data cleansing, modeling and analysis, findings, and deployment.

• Elective 3 credit hours (needed if only 3 hours of DATA 6640 taken)