Introduction

This program is designed to produce graduates who are knowledgeable and skilled in key concepts in the areas of data science.  The program contents are tailored to enable graduates to translate their knowledge and skills to market needs and demonstrate creativity, innovative problem solving and competitiveness.

Program Objectives

1. To produce graduates who are knowledgeable in the field of data science, and able to extract meaningful insights to help organizations cope with challenges and issues arising from big data.
2. To prepare graduates that have a deep understanding of the core concepts, practices and tools in the domain of Data Science and big data analytics.
3. To produce professional human capital who can translate business and research problems into useful technical solutions, which requires communication, teamwork, leadership skills, responsibility and good ethics.

Program Learning Outcomes

1. Master the important concepts and theories in the field of data science, that can be utilized in relevant domains such as business and social sciences.
2. Apply the knowledge in data science in designing and developing data models, systems, and applications.
3. Solve problems in various disciplines through research, and knowledge of data science and scientific computing.
4. Be able to work cooperatively with all internal and external stakeholders to solve data-driven problems in real-world.
5. Be able to communicate the outcomes of data analytics and visualization to a wide range of audience for better decision making

Course Structure

This 45 credit hours program consists of 9 subjects and a dissertation which the candidate must pass for graduation.

CODE	COURSE	CREDIT HRS
CS1001	Advanced Algorithms	3
CS1002	Research Methodology	3
CS1003	Principles of Data Science	3
CS1004	Parallel and Distributed Computing	3
CS1005	Statistics	3
DS3001	Programming for Data Science	3
DS3002	Machine Learning for Data Science	3
DS3003	Datamining/Big Data Management	3
DS3004	Data Analytics and Visualization	3
	TOTAL	27
CS2001	DISSERTATION	18
	TOTAL PROGRAM CREDIT HOURS	45

Course Plan

CODE	COURSE	CREDIT HOUR
SEMESTER I
CS1001	Advanced Algorithms	3
CS1003	Principles of Data Science	3
SUB-TOTAL		6
SEMESTER II
CS1004	Parallel and Distributed Computing	3
CS1005	Statistics	3
DS3001	Programming for Data Science	3
SUB-TOTAL		9
SEMESTER III
CS1002	Research Methodology	3
DS3002	Machine Learning for Data Science	3
CS2001	DISSERTATION (Topic selection, problem definition, objectives, research questions, hypothesis)	3
SUB-TOTAL		9
SEMESTER IV
DS3003	Data mining/Big Data Management	3
DS3004	Data Analytics and Visualization	3
CS2001	DISSERTATION (proposal defence)	3
SUB-TOTAL		9
SEMESTER V
CS2001	DISSERTATION (completion, prepare publication)	12
SUB-TOTAL		12
GRAND-TOTAL		45 Credit Hours

Entry Requirements

2. Applicants of this program should possess a bachelor’s degree in a related field with a minimum CGPA of 2.5
3. All Applicants must pass the university English proficiency test or provide evidence for international English certification.

Duration

16 to 20 Months = 4 to 5 Semester,    1 Semester = 4 Months

Program Fees:

• Tuition Fees: $2,200 ($110 / Month)
• Registration Fees: $80

Graduation Requirements

Candidates must fulfil the following requirement for graduation to qualify for the degree:

1. Satisfactorily completed all the courses and the total number of credit hours specified for the program.
2. Obtained a final CGPA of at least 2.67 on completion of the program.
3. Paid all fees due to the university.

Grading System

The grading system for all courses is as follows:

Marks	Grade	Grade Point (GPA)	Meaning
90-100	A+	4.00	High Distinction
80-89	A	4.00	Distinction
75-79	A-	3.67	Very Good
70-74	B+	3.33	Good
65-69	B	3.00	Pass
60-64	B-	2.67
55-59	C+	2.00	Fail
50-54	C	1.67
Less than 50	F	0.0