Page 122 - University Bulletin

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IS 434 SYSTEM TESTING AND QUALITY ASSURANCE
Credits: 3(3,0,1) Prerequisites: IS 231

This course is designed to give an understanding of the key concepts and principles in
creating and managing successful software testing to meet specific requirements using
best practices of software quality assurance. Topics covered include software quality
assurance, testing process, test design & coverage techniques and testing strategy. Best
practice strategies in object- oriented software testing and web application are also
discussed. An overview of test automation methods and tools is also covered.

IS 435 DYNAMIC WEBSITE DEVELOPMENT WITH GRAPHICS
Credits: 3 (3,0,1) Prerequisite: IS 311

This course has two main objectives. The first objective is to introduce visual principles as
the basis for graphic design in order to develop a sound foundation that enables students
to cater to stakeholder’s requirements of communication using business logos,
website graphics, and colors. The second objective is to enable the students to develop
and combine the expertise of visual communication with dynamic website development.
The course contents are specially designed to produce experts for graphics and website
design industry with skills in constructing interactive Graphical User Interfaces, design,
and development of creating graphics for websites and dynamic website development.

IS 442 INFORMATION RETRIEVAL SYSTEMS
Credits: 3(3,0,1) Prerequisites: IS 241, STAT 101

This course looks at traditional and web-based information retrieval (IR) techniques. Items
covered include IR concepts, basic IR models, vector-space retrieval, textual document
tokenization, indexing, organization, and classification, stemming, statistical text
representation, text categorization and clustering, query languages, and web search
techniques.

IS 446 KNOWLEDGE DISCOVERY AND DATA MINING
Credits: 3(3,0,1) Prerequisites: IS 241, STAT 101

This course introduces students to the fundamental techniques and practical tools used
for transforming corporate data into business intelligence. Topics covered include:
terminology, importance, techniques, such as: Online Analytical Processing (OLAP)
systems, artificial neural networks (ANN), rule-based systems (RBS), fuzzy logic (FL),
machine learning (ML), classification trees, classification and regression trees (CART
Algorithm), and applications.

IS 448 CLOUD COMPUTING AND BIG DATA ANALYTICS
Credits: 3(3,0,1) Prerequisites: IS 371

This course has a mainly practical approach dealing with the related technologies to the
creation of Big Data Analytics applications on the Cloud. The students will learn the
principles and the state of the art of large-scale distributed computing in a service-based
model. Students will study how scale affects system properties, models, architecture, and
requirements. Regarding principles, this course looks at how scale affects systems
properties, issues (such as virtualization, availability, locality, performance, and
adaptation), system models, architectural models, environment and application