This course introduces students to topics applicable to e-government trends, issues, and applications. The technologies, frameworks, policies, evaluation methods, and best practices are also addressed, along with any implications. Issues related to managing ICT in the public sector are also included.

This course focuses on the basic concept of computer network security. Topics of discussions would include basic cryptosystem, secure encryption system, protocols and practices in encryption, malicious code, operating system and database security, network security, administering security, and legal issues in computer security. Current issues on network management especially security issues and policies would also be discussed.

This course is planning to expose the concept of operating system functions and the basic components. This course discusses generally the function of operating system components, policy, strategy and approach being used in constructing an Operating System. This course also exposes students to hands-on experience in using different operating systems for various computing devices. Besides that, several examples of popular Operating System from Windows, Linux, Mac, Android and IOS will be covered.

Logic programming spans various important aspects in AI including Prolog programming in terms of the language, constructing Prolog programs, computation and data structures, expressing procedural algorithms, input/output and other built-in predicates, and finally windows programming using Prolog.

This course is an introductory to the concepts of Knowledge Discovery in Database (KDD) as it applies to the database system environment. Several data mining task, models, methodologies, techniques, and common operational issues will be covered. Besides, student will also be introduced to several data mining models.

This course is an introduction course for natural language processing (NLP). The course discusses several main topics in natural language processing (NLP). These include theories, principles, components, methods, techniques, and applications of NLP. The major focus is given on the following subtopics: syntax analysis, syntactic analysis, semantic analysis, ambiguity analysis, and NLP applications.

The course will provide students with the necessary knowledge and skills to develop and carry out a research. In particular, the course aims at teaching students about research design, strategies, methods, and process. The important steps in planning a research project, such as problem identification and formulation of the research question, developing the study objective, reviewing related literature and formulating a model will also be covered. Additionally, methods for data collection and analysis are included. The course also discusses the preparations involved in writing a research proposal.

This course is designed for students to carry out the process of data pre-processing and exploratory analysis. Process of data pre-processing involved activities such as data cleaning, integration, reduction and transformation, while exploratory analysis consists of analyzing data using non-graphical or graphical approach with univariate or multivariate.

This course aims to provide the necessary skills and knowledge for undergraduate IT students specializing in Data Science to be able to use statistical learning tools to analyze data for modelling and understanding complex datasets. This course will cover introduction to statistical inference, variability and common distributions, confidence intervals and hypothesis testing, power, multiple testing, sensitivity analysis, Jacknife and re-sampling, bootstrapping and permutation tests. This course is aimed to equip students with the tools needed to analyze real-world data and to justify their use through theory. Together, students will study basic concepts related to statistical inference and examine commonly used methods, with an emphasis on understanding when and how to apply them. Students will also learn how to use these methods on the statistical software R.