51
WIE2001
TRENDS IN
INFORMATION SYSTEMS
Credit:
3
Course Pre-requisite(s):
None
Medium of Instruction:
English
Learning Outcomes
1. Describe emerging computer technologies,
industry
specific information
systems
,
information systems, and current trends in
information systems.
2. Discuss critical issues related to managing
and administering the discussed information
systems or technologies.
3. Apply tools or model to relevant cases or data.
Synopsis of Course Content
This course explores selected topics of specialized
content (not usually covered by the other courses)
as determined by the department and the lecturer
with emphasis on current Information System
trends. Topics under this course heading vary from
year to year according to the developments in
computer technology and information systems.
Assessment Methods
Continuous Assessment: 50%
Final Examination: 50%
WIE2002
OPEN SOURCE PROGRAMMING:
APPLICATION AND TECHNOLOGY
Credit:
3
Course Pre-requisite(s):
None
Medium of Instruction:
English
Learning Outcomes
1. Explain basic characteristics and concepts of
open source applications and technology.
2. Write applications using open source
programming language to populate, retrieve and
update database.
3. Develop open source solution to resolve a
business problem.
Synopsis of Course Content
This course will enable students to learn the basic
characteristics and concepts of open source
applications and technology. Student will be able to
write applications using open source programming
in order to populate, retrieve ad update database.
They will also develop an open source solution to
resolve a business problem.
Assessment Methods
Continuous Assessment: 50%
Final Examination: 50%
WIE2003
INTRODUCTION TO DATA SCIENCE
Credit:
3
Course Pre-requisite(s) :
None
Medium of Instruction :
English
Learning Outcomes
1. Explain the key concepts relevant to data
science, including all processes in the data
science life cycle and data science
applications in real-world.
2. Determine the core algorithms underlying an
end-to-end data science workflow, including
the experimental design, data collection,
mining, analysis, and presentation of
information derived from large datasets.
3. Categorize suitable tools and technologies used
in data science.
Synopsis of Course Content
The course is designed to help the student learn
fundamental concepts of data science. It covers the
what, when, who, where, why and how (5W 1H) of
data science in the era of big data. Also
encompass, the life cycle of data science from
data preparation, data processing, data cleansing
and integration, to data analysis and visualization
of data in data-driven decision making. The role of
data scientist, the knowledge and skills required is
also presented. Machine learning algorithms and
statistical
models
are
included.Diverse
technologies, programming languages as well as
tools in data science are discussed.
Assessment Methods
Continuous Assessment: 60%
Final Examination: 40%
WIE2004
INFORMATION SERVICE ORIENTED
ARCHITECTURE
Credit:
3
Course Pre-requisite(s):
None
Medium of Instruction:
English
Learning Outcomes
1. Explain the need for Web services and
Service Oriented Architecture in business.
2. Describe the philosophy and architecture of
Web services and Service Oriented
Architecture.
3. Interpret the essential ingredients (SOAP,
UDDI, WSDL) of a Web Service.
4. Design a Service Oriented Architecture using
XML Web Services (Model of SOA).
