Graduate Study topics such as database compression, encryption and security.
from probability theory, statistics, and mathematical modeling such as probability distributions, cumulative probability distributions, descriptive statistics, hypothesis testing, correlation analysis, linear regression, multivariate regression, and mathematical model design. The course then proceeds to examine modern tools for conducting analyses using these quantitative methods on both small scale and large scale datasets. Case studies from a variety of settings are used to develop students' abilities to successfully apply the techniques learned in this course to practical circumstances that often, because of the ambiguities involved, present limitations to the power of these mathematical tools. Topics from this course also provide the foundation for some subjects covered in the analytical methods course and the data mining and business intelligence course.
MDS 534 Data Mining and Business Intelligence Business intelligence represents a conceptual framework for decision support. It combines analytics, data warehouses, applications, and methodologies to facilitate the transformation of data into meaningful and functional information. The major objective of business intelligence is to enhance the decision-making process at all levels of management. Data mining is a process that utilizes statistical analysis, probability theory, mathematical modeling, artificial intelligence, and machine learning techniques to extract useful information and subsequent knowledge from large data repositories, commonly referred to as “big data.� This course examines a number of emerging methods proven to be of value in recognizing patterns and making predictions from an applications perspective. Students will be provided the opportunity for hands-on experimentation using software and case studies.
MDS 549 Data Mining Project Each student completes a project incorporating the practical application of several of the program's data mining techniques to one or more data sets provided by the instructor. In addition to the correct use of the techniques and interpretation of the results, emphasis is placed on the student's ability to gauge the resultant impact on the organization's business intelligence processes and procedures. Prior to the submission of the final project, students submit a proposal describing the application and the data mining tools to be utilized.
MDS 535 Programming Language and Environments This course covers the application of appropriate high-level programming languages for expressing software design patterns used for extracting and processing big data. These highlevel languages include imperative, objectoriented languages, such as C++, Java, Python, Matlab, along with the associated libraries and language pragmatics for framework and patterns (e.g. map-reduce) relevant to processing massive amounts of data. Query languages, spreadsheet macro languages, and web-client scripting languages are also studied in the context of data mining.
MDS 556 Analytical Methods This course builds upon the foundation established in the quantitative methods course to develop the advanced analytical methods required for in-depth applications of data science. Topics covered include advanced techniques in statistics and mathematical modeling such as exploratory data analysis, logistic regression and stochastic models; modern techniques for network analysis such as measures of network centrality, hierarchical and other clustering techniques, and models of network
MDS 546 Quantitative Methods The ability to move data along the continuum from information to insight to action requires a strong foundation of skills in various quantitative methods. This course begins with a systematic and integrated overview of concepts 406