Page 290 - 2024.2025 - 澳門理工大學學士學位課程手冊 (電子書)

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Code Module Credits Duration Prerequisite(s)
Elective Subjects - Group B (Cont.)

COMP4128 Text Corpus Technology and Application 3 45 hrs ---
A text corpus is a machine-readable collection of a relatively large amount of text, and they
are frequently used in various tasks in text mining and natural language processing. Students
will learn the fundamentals of text corpora (e.g., what is a corpus, its characteristics and how
to create one), their use in natural language processing, and common computational tools
for text corpora. In addition, applications of text corpus technologies, such as n-grams,
pattern finding, collocation, will be introduced and discussed.
COMP4129 Introduction to Internet-of-Things 3 45 hrs ---
This module provides a comprehensive overview of the Internet of Things (IoT) from the
global context. A number of underlying technologies enabling IoT will be discussed, such as
different sensing technologies, wireless sensor networks, machine-to-machine
communications, Cloud and Fog computing technologies, etc. The IoT environment should
permit interaction among machines, smart devices, ubiquitous computers, physical objects
and human users. This module is an introduction to the fundamentals of IoT, designed for
either Information Communication Technology (ICT) or non-ICT students. In particular, the
course will define the core system architectures, including but not limited to, the middleware
to design single device and multi-device systems. In order to obtain more hands-on
experience in building IoT applications through different smart sensing devices,
constructions of smart sensor devices through experiencing the Arduino and Raspberry Pi
device programming will be covered.
COMP4130 Introduction to Big Data 3 45 hrs ---
This learning module covers the characteristics of Big Data, the sources of massive data in
enterprises and sensor networks, and the challenges in data ingestion, data storage and
analytic processing. The students will acquire skills and working knowledge of the Big Data
tools and technologies. This course focuses on the planning, designing and implementing Big
Data solutions. Examples and exercises of Big Data systems are used to provide hands-on
experiences in the workings of major components in Big Data solutions. The students will
also be able to integrate the Big Data tools to form coherent solutions for business problems.
Finally, additional related topics in the area of Big Data, such as alternative large-scale
processing platforms, non-relational data stores, and Cloud Computing execution
infrastructure are presented.
COMP4131 E-commerce with Big Data 3 45 hrs ---
Recent advances in information and communication technologies (ICTs) have led to the rapid
explosion of consumer and user data. Business intelligence derived from Big Data can help
firms to better understand market needs, develop new products and services, improve
operational efficiency, and acquire competitive advantages. This learning module provides
an overview of common big data applications and analysis techniques (e.g., market basket
analysis, sentiment analysis, decision tree, clustering, etc.) in business and discusses some
implementation issues related to big data projects. As part of a group project, students will
need to demonstrate the ability to come up with a business plan based on a given case study
and a relevant data set.

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