Data Analytics (DAT)


DAT 332. Matrix Analysis and Numerical Optimization. 4 Hours.

This course is an introduction to matrices and numerical optimization with applications in engineering and science. Topics include Algebra of matrices and systems of linear algebraic equations, rank, inverse, eigenvalues, eigenvectors, vector spaces, subspaces, basis, independence, orthogonal projection, determinant, linear programming and other numerical methods. Course Information: Prerequisites: MAT 115 or MAT 113 or equivalent.

DAT 444. Operations Research Methods. 4 Hours.

Quantitative methods necessary for analysis, modeling, and decision making. Topics include linear programming, transportation model, network models, decision theory, games theory, PERT-CPM, inventory models, and queueing theory. Additional topics may be chosen from integer linear programming, system simulation, and nonlinear programming.

DAT 472. Introduction to Database Systems. 4 Hours.

Examine of file organizations and file access methods, as well as data redundancy. Studies various data models including relational, heretical, network, and object-oriented. Emphasis given to the relational data model SQL, the data definition and manipulation language for relational databases, is described, including database security. Course Information: Course is restricted to MS CSC majors and MS DAT majors only. Prerequisites: CSC 275. Same as CSC 472.

DAT 502. Introduction to Statistical Computation. 4 Hours.

Explore the use of various statistical software packages, such as SAS, SPSS, and R. Topics will be selected from construction of data set, descriptive analysis, regression analysis, analysis of design experiment, multivariate analysis, categorical data analysis, discriminant analysis, cluster analysis, and presentation of data in graphic forms. Course Information: Prerequisites: CSC 225 or equivalent and MAT 121 or equivalent.

DAT 550. Advanced Statistical Methods. 4 Hours.

Topics include multiple linear regression, statistical inferences for regression model, diagnostics and remedies for multicollinearity, outlier and influential cases, model selection, logistic regression, multivariate analysis, categorical data analysis, discriminant analysis, cluster analysis. Course Information: Prerequisites: MAT 121 or equivalent.

DAT 551. Data Mining. 4 Hours.

This course teaches advanced techniques for discovering hidden patterns in the rapidly growing data generated by businesses, science, web, and other sources. Focus is on the key tasks of data mining, including data preparation, classification, clustering, association rule mining, and evaluation. Course Information: Course is restricted to MS CSC majors and MS DAT majors only. Prerequisites: CSC 385. Same as CSC 573.

DAT 552. Introduction to Machine Learning. 4 Hours.

Machine learning explores the design and the study of algorithms that can learn from data or experience, improve their performance, and make predictions. The course provides an overview of many concepts, techniques, and algorithms in machine learning, including supervised learning, unsupervised learning, reinforcement learning, and neural networks. Course information: Prerequisites: DAT 550, CSC 385, and DAT 332.

DAT 553. Big Data Analytics. 4 Hours.

This course teaches concepts and techniques in managing and analyzing large data sets for data discovery and modeling. Focus is on big data management, storage solutions, query processing, analytics, and big data applications. Topics include: introduction to Hadoop and YARN, MapReduce, Apache Spark, Big Data Warehousing with Hive and Spark SQL, large scale recommender systems and Large Scale Clustering and Classification. Course information: Prerequisites: CSC 385, CSC 472, CSC 573.

DAT 554. Data Analytics Capstone. 4 Hours.

This is a practicum course that allows students to apply the appropriate methods and tools for data analysis in a real-world organizational setting. The capstone course provides the opportunity to exercise different techniques for data storage, preprocessing, integration and analysis covered throughout the Master of Data Analytics curriculum in order to address challenges from different areas. Course Information: Prerequisites: DAT 552 and DAT 553.

DAT 565. Advanced Database Concepts. 4 Hours.

Study of the implementation of relational database management systems. Topics include database design algorithms, query implementation, execution and optimization, transaction processing, concurrency control, recovery, distributed query processing, and database security. One of the following advanced database topics will also be discussed: deductive databases, parallel databases, knowledge discovery/data mining, data warehousing. Course Information: Course is restricted to MS CSC majors and MS DAT majors only. Prerequisites: CSC 472. Same as CSC 572.

DAT 566. NoSQL Databases. 4 Hours.

Traditional data management techniques (schema-driven databases) do not meet the need to manage the varying storage techniques and technologies used for today?s data. NoSQL (Not only SQL) databases have emerged as a means of managing distributed, high-volume, complex data. This course will use a hands-on laboratory approach to explore the different types of NoSQL Databases. Course Information: Course is restricted to MS CSC majors and MS DAT majors only. Prerequisites: CSC 385 and CSC 472. Same as CSC 561.

DAT 568. Web Analytics. 4 Hours.

This course focuses on the algorithm and techniques for automatic discovery of useful patterns from the structure, usage, and content of web resources. Topics include: link analysis, search, social network analysis, structures data extraction, information integration, opinion mining and sentiment analysis, web usage mining, and unstructured text processing. Course Information: Prerequisites: CSC 573.

DAT 569. Data Visualization. 4 Hours.

This course is designed to help students acquire the knowledge and skills to analyze information and, more importantly, to draw conclusions from analysis. This course is not about using advanced mathematics to solve problems. It?s about learning to use computer technology, especially visualization (graphs, histograms, pie charts), to look at and understand data in a more intuitive and visual manner. Course information: Course is restricted to MS CSC majors and MS DAT majors only. Prerequisites: CSC 385. Same as CSC 562.

DAT 570. Advanced Topics in Data Analytics. 4 Hours.

Topics and prerequisites vary. Students may refer to the course schedule for topics and prerequisites. Restricted to Graduate Students, Data Analytics majors or Computer Science majors.