Elective Courses

Course Code: COMP 6300
Course Name: Advanced Internet Technologies
Description: This course covers the technologies, protocols and architectures of the Internet. A major focus of this course is the technology and the drive towards Service Oriented Architecture (SOA), web services and ebusiness. To achieve this, we will examine the extensible markup language (XML) and associated technologies as well as JSON and REST based technologies. This is followed by exploring the technology used in web services such as web services description language (WSDL), simple object access protocol (SOAP), universal description, discovery and integration (UDDI). With this background, we will look at the concept of semantic web as well as the technologies that are being used in it. Simultaneously, another aspect of the course will look at Java-script and AJAX (Asynchronous Java-script And XML) that are used to deliver modern web-based and mobile applications. nical skillsets are vital for students seeking to start businesses or engage in scientific research.
Course Code: COMP 6401
Course Name: Advanced Algorithms
Description: This course is concerned with equipping students with the necessary background in algorithm design techniques that would enable them to deploy, develop, and evaluate sophisticated algorithms in a variety of circumstances.
Course Code: COMP 6905
Course Name: Cloud Technologies
Description: The course helps to understand the technologies and applications of cloud computing and its virtualization foundation used in servers, desktops, embedded devices and mobile devices. The objective is to train students for the growing area of cloud services.
Course Code: COMP 6802
Course Name: Distributed and Parallel Database Systems
Description: This course covers the principles and system organization of distributed and parallel databases. It focuses on issues of Database System Architectures, Database Design and Query Optimization in Distributed and Parallel Database Systems. Emphasis is also placed on the fundamentals of Enterprise Database Systems.
Course Code: STAT 6181
Course Name: Computational Statistics I
Description: This course is meant to cover the basics methods in computational statistics. Techniques such as bootstrap, jack-knife, MCMC with particular reference to both hierarchical Bayesian and Empirical Bayes will be covered. The theoretical underpinnings of the course will be covered in conjunction with relevant computational aspects. The course will be hands on and practical and will rely heavily on the statistical software R. Matlab will be utilized where there is a need for numerical computations. We will rely on both real data and simulated data for illustrating the main concepts in the course. Datasets from different subject areas will be utilized. The course is the first in a sequence of two computational statistics courses, which can be done separately.
Course Code: STAT 6182
Course Name: Computational Statistics II
Description: This course is meant to cover the techniques in statistics that are computational in nature that would not have ordinarily been covered by the other courses in the Statistics masters program. The course covers topics such as spatial statistics, advanced Bayesian models and some data mining techniques. Both the theoretical underpinnings of the material and the application through computational aspects will be covered. The course will be hands on and practical and will rely heavily on the statistical software R. Matlab will be utilized where there is a need for numerical computations. We will rely on both real data and simulated data for illustrating the main concepts in the course. Datasets from different subject areas will be utilized. The course is the second in a sequence of two computational statistics courses
Course Code: STAT 6XXXX
Course Name: Data Analysis
Description: This is a course on getting the most out of data. The emphasis will be on hands-on experience, involving case studies with real data and using common statistical packages i.e. R, SPSS and STATA. The course covers, at a very high level, exploratory data analysis, model formulation, goodness of fit testing, and other standard and non-standard statistical procedures, categorical data, generalized linear models, survival analysis, and modern regression methods. Students will be expected to propose a data set of their choice for use as case study material.
Course Code: STAT 6XXX
Course Name: Multivariate Analysis
Description: This course provides graduate students with a set of statistical tools and methods that will enable them to analyze multivariate data properly using sound statistical methods and appropriate computer software. Possible topics to be covered include multivariate data screening, principal component analysis, MANOVA, MANCOVA, discriminant analysis, cluster analysis, multidimensional scaling, factor analysis and structural equation modeling. All methods will be illustrated via real data sets, using the open source statistical software R. This course will also expose students to use of statistical software such as Minitab, SPSS and JMP and STATA.