Capstone Project / 2 Elective Modules#
(8 Credit Units)
In this module, students are required to complete a substantial project that is the culmination of their education in the School of InfoComm Technology. The project can be a real-world problem proposed by a client, or it can be proposed by students in pursuit of their personal interests.
Ethical Hacking
(4 Credit Units)
This module aims to develop Penetration Testers for the information security industry. They will be taught to follow a process model to locate and establish targets, find vulnerabilities, and exploit the flaws to determine potential impact and business risk with the goal of helping the owner improve security practices. Students will learn the techniques hackers use to hack a system, and the steps to secure it. Students will have hands- on practice on actual pen-testing that involves reconnaissance to map out IT infrastructure, scanning vulnerable systems, and developing attack vectors to exploit loopholes in a system. Students will also be taught the necessary countermeasures to mitigate risks of exploitation through system hardening, intrusion detection and prevention.
Mobile Device Security & Forensics
(4 Credit Units)
This module covers techniques and tools in the context of a forensic methodology to extract and utilise digital evidence on mobile devices. Students will learn how to use current forensic tools to preserve, acquire & examine data stored in a mobile device. The module covers basic SIM Card examination and cell phone forensics on multiple platforms such as iPhone, Android & Windows Mobile. The module takes a practice-oriented approach to performing forensics investigation on mobile phones. This module carries a co-requisite: Digital Forensics.
Fundamentals for IT Professionals 3
(2 Credit Units)
This module provides a stepping-stone for students in their IT career. They will gain insights into the infocomm industry and keep abreast of the latest skill sets required in their IT career path. They will also have the opportunity to be exposed to various institutes of higher learning to further hone their skill sets.
Project ID: Connecting the Dots (IS)^
(4 Credit Units)
^ Interdisciplinary Studies (IS) modules account for 13 credit units of the diploma curriculum. They include modules in communication, innovation and world issues, as well as an interdisciplinary project. By bringing students from diverse diplomas together, the interdisciplinary project fosters collaboration to explore and propose solutions for real-world problems. IS aims to develop students to be agile and self-directed learners, ready for the future workplace. For more details on Interdisciplinary Studies (IS) electives
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#ELECTIVE MODULES
Governance & Data Protection
This module examines the relevant frameworks to ensure that information assets are protected within an organisation. It includes the processes and policies for administering and managing a company’s IT systems that follow the compliance framework. Concepts on risk management process, risk analysis and mitigation will also be introduced. Students will learn to evaluate risks against the company’s critical assets and deploy safeguards to mitigate them. Control frameworks such as PCI (Payment Card Industry), ISO 17799/27002, and COBIT will be covered.
Mobile Device Security & Forensics
This module covers techniques and tools in the context of a forensic methodology to extract and utilise digital evidence on mobile devices. Students will learn how to use current forensic tools to preserve, acquire & examine data stored in a mobile device. The module covers basic SIM Card examination and cell phone forensics on multiple platforms such as iPhone, Android & Windows Mobile. The module takes a practice-oriented approach to performing forensics investigation on mobile phones. This module carries a co-requisite: Digital Forensics.
Network Forensics
Network equipment, such as web proxies, firewalls, IDS, routers, and even switches, contain evidence that can make or break a case. This module provides students with the knowledge and skills to recover evidence from network-based devices. It will begin with an introduction of different network devices and the type of data that are useful from a forensic point of view. It then moves on to the most common and fundamental network protocols that the forensic investigators will likely face during an investigation. These include the Dynamic Host Configuration Protocol (DHCP), Network Time Protocol (NTP) and Microsoft Remote Procedure Call (RPC) protocol. The students will learn a variety of techniques and tools to perform sniffing and log analysis on the network. Commercial and Open Source tools will be used to perform deep packet analysis while SIEM tools such as Splunk will be used to perform log analysis on network devices.
Data Structures & Algorithms
This module aims to provide students with the knowledge and skills to analyse, design, implement, test and document programmes involving data structures. It teaches basic data structures and algorithms within the conceptual framework of abstract data types. The emphasis is on using the class feature of an Object-oriented language platform to give the concrete implementation of various abstract data types.
Deep Learning
This module introduces the fundamentals of Deep Learning and its applications and provides students with essential context and background knowledge around Artificial Intelligence and its subset, Deep Learning. Students will learn about relevant models such as Neural Networks and experience the practical applications of these models in areas such as computer vision and natural-language processing. These models will be implemented using leading softwares and associated libraries.
Developing Cloud Applications
This module covers the analysis of business and technical requirements of a cloud-based system, implementation of a cloud strategy with appropriate programming tools, deployment, and testing and debugging of the cloud application. Analysis of business requirements to determine how they can be mapped into a cloud environment is discussed in this module. The module extends its discussion to cloud computing design patterns, best practices, cloud migration issues and considerations. Students are exposed to a cloud computing platform such as Windows Azure to get extensive hands-on practice to build, migrate, host and scale web applications and services through the vendor’s data centres.
Machine Learning
This module introduces the fundamentals of Machine Learning (ML) and its applications. Students will be provided the essential context and background knowledge of Machine Learning. Students will gain exposure to both supervised and unsupervised learning models such as Linear & Logistic Regression, Decision Tree, K-means Clustering and more. Using leading software and associated libraries, learners will be able to implement and train Machine Learning models to address business challenges.
Mobile Applications Development
This module focuses on the design and development of applications for mobile devices like hand phones, personal digital assistants (PDAs) and handheld computers. Due to the nature of these handheld devices, issues such as memory storage, user interface and data input methods require more careful consideration. At the end of this module, students will be able to develop applications that can run on mobile devices and interact wirelessly with server-side programmes.
Cloud Architecture & Technologies
This module gives insight into the key concepts and technologies of cloud computing which include cloud characteristics, service models (SaaS, PaaS, and IaaS), deployment models (Public cloud, Private cloud, Community cloud, and Hybrid cloud), and the features of cloud computing technologies. It also covers the cloud computing architecture, emerging trends and issues such as clouds for mobile applications, cloud portability and interoperability, scalability, manageability, and service delivery in terms of design and implementation issues.
The module discusses the benefits and challenges of cloud computing, standards of cloud computing service delivery, and Service Level Agreement (SLAs) for cloud services. Hands-on activities are included to expose students to various cloud computing services offered by major cloud computing providers such as Amazon Web Services (AWS), Google App Engine (GAE), and Microsoft Windows Azure.
Applied Analytics
Data Visualisation
This module covers the techniques and tools for creating effective visualisations based on principles from graphic design, perceptual psychology and cognitive science. Students will learn how to process large volumes of data to create interactive visualisations for ease of exploration. Topics that are covered include visualising patterns, proportions, relationships, spatial and temporal elements, and multi-dimensional visualisations.