Master of Science in Computing & Data Analytics (MCDA)

Students study the complete software development process in a business environment, including the system analysis, design, implementation, and testing of software systems. Students will work in teams to develop software systems for business applications using real world methodologies.

This course will allow students to acquire skillsets and build a portfolio in a designated computing and/or data science paradigm based on current trends in the industry. The students will learn theoretical and practical aspects of the paradigm by implementing real-world solutions for one or two projects.

This course will allow students to acquire skillsets and build a portfolio in a designated computing and/or data science paradigm based on current trends in the industry. The students will learn theoretical and practical aspects of the paradigm by implementing real-world solutions for one or two projects.

Emphasis in this course is on developing the conceptual foundations and an in-depth understanding of statistical techniques used in data analytics. Following the descriptive/predictive/prescriptive framework that is commonly imposed on topics in analytics, it emphasizes the analysis and solution of complex business decision problems using computer-based models.

Students design, prototype and evaluate user interfaces using a variety of methods. Topics covered include business analysis; human capabilities; interface technology; interface design methods; interface evaluation; quality assurance and visualization methods for data analytics.

Students examine the design, implementation, and management of database (db) systems. Students consider the implications of data structures and indexing on performance; query processing algorithms and optimization; and concurrency control. In addition to relational db models. Students study data models intended to manage alternative types of data such as structured text, multimedia data, and unstructured ones in the context of Big Data.

Students develop applications that are accessible through the internet on a variety of platforms including cloud environments and mobile devices. An emphasis is placed on designing and deploying mobile applications; push technology; data structures and memory management; interface design; Scalable Vector Graphics (SVG); cloud computing; and privacy/security.

Students use tools and techniques for customer and product profiling using classification and clustering, analysis of demographic information for business decision making, and supply and demand management using predictive models. Professional business intelligence software is used with real-world data sets to effectively analyze statistical patterns for strategic decision making.

Students are provided with processes, models, and frameworks to develop organizational IT strategy in the context of big data. Students focus on leading software and hardware platforms, business software applications and their strategy maps (CRM, ERP, supply chain management, product lifecycle management. Technology adoption, emerging technologies, and diffusion of innovations are covered.

With the availability of large databases to store, manage and assimilate data, the new thrust of data mining lies at the intersection of DB systems, artificial intelligence, and algorithms that efficiently analyze data. Highly complex big data techniques present many interesting computational challenges. Students use concepts from pattern recognition, statistics, data analysis, text mining, network analysis, and sentiment analysis, and machine learning for actionable analytics.

This is the first of a two-part series of courses involving the design, development, implementation, and testing of a computing system with focus on data analytics. Depending on the nature of the project students may choose a varying degree of balance between data analytics and system development. Students work in teams to develop a system under supervision of a faculty member. The first phase involves system analysis and development of the graphical interface. The course also includes seminars on project management from academic and industrial faculty members.

This is the second of a two-part series of courses involving implementation of the complete system, testing, and simulated cut-over to production.

This 16 week internship provides students an experiential learning opportunity through work experience in a Computing and Data Analytics environment. Students apply the concepts learned in the program and are required to submit reports during and at the end of the work term. An employer assessment of student performance will be conducted. The course also includes seminars on project management from academic and industrial faculty members.

This second 16 week internship provides students with experiential learning opportunity through work experience in a Computing and Data Analytics environment. Students apply the concepts learned in the program and are required to submit reports during and at the end of the work term. An employer assessment of student performance will be conducted.