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Bachelor of Engineering in Data Science (Honours)

Xiamen University Malaysia Campus

Bachelor of Engineering in Data Science (Honours)

Xiamen University Malaysia Campus

Bachelor of Engineering in Data Science (Honours)

Qualification

Bachelor's Degree

Duration

4 years

Intake

FebFebAprAprSepSep

English Requirement

Not Required

Offer Letter

Free

Class Type

Physical

Course Fee for International Students

First year from MYR N/A excludes 6% SST & other fees

Estimated total tuition from ~MYR 0 4 years programme

Published fees don't include possible scholarships and YourUni discounts — most students pay less than the sticker price.

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The Bachelor of Engineering in Data Science (Honours) is a new programme offered by the School of Computing and Data Science, developed to meet the growing market demand for data science graduates across various sectors, including banking, commercial, industrial, medical, and public sectors. The programme aims to cultivate talents with a global mindset and analytical capabilities to navigate the constantly changing world. Upon completion, students will possess mathematical, statistical, and computational skills necessary for solving complex data problems.

This programme is supported by both local and international teams of academicians, including experts from Computer Science, Artificial Intelligence, Software Engineering, and Mathematics. These academicians, with diverse backgrounds in niche fields, bring unique advantages to teaching in the interdisciplinary realm of data science. Besides teaching, they are actively engaged in research and publication, further enhancing the programme's academic depth.

The curriculum provides a robust analytical and statistical foundation, enabling students to apply data science knowledge and techniques effectively. Graduates will be well-prepared to excel in their careers and pursue postgraduate studies in renowned universities worldwide.

ENTRY REQUIREMENTS:

STPM (Science Stream): A pass in STPM with at least a Grade C (GP2.0) in Mathematics AND 1 Science/ICT subject.
STPM (Non Science Stream): A pass in STPM with at least a Grade C (GP2.0) in any 2 subjects AND a credit in Additional Mathematics in SPM or its equivalent.
A-LEVEL: A pass in A-Level with at least a Grade D in any 2 subjects.
UEC: A pass in UEC with at least a Grade B in 5 subjects.
Foundation/Matriculation: A pass in Foundation/Matriculation with at least a CGPA of 2.0 out of 4.0.
Diploma: A pass in Diploma in Computing fields (Computer Science/Software Engineering/Information Technology/Information System/Data Science) with at least a CGPA of 2.5 out of 4.0 OR a pass in any Diploma in Science and Technology or the equivalent with at least a CGPA of 2.75 out of 4.0. Also, candidates must fulfill one of the following:
- Additional Mathematics — a credit in SPM or the equivalent; OR
- Mathematics and any 1 Science/Technology/Engineering subject — a credit in SPM or the equivalent AND pass a Mathematics placement test organised by XMUM before joining the programme.

Candidates with a CGPA of less than 2.5 but more than 2.0 may be accepted subject to a stringent internal evaluation process.
Candidates with a CGPA of less than 2.75 but more than 2.5 may be accepted subject to a stringent internal evaluation process.
The requirement for Additional Mathematics at SPM level can be exempted if the Foundation/Matriculation or its equivalent offers a Mathematics course that is of a similar or higher level compared to the Additional Mathematics at SPM level.

MAIN COURSES

Year 1:

Calculus
Linear Algebra
C and C++ Programming
Introduction to Intelligence Application
Data Structures
Introduction to Data Science

Year 2:

Python and Tensorflow Programming Language
Principles of Artificial Intelligence
Database
Probability Theory
Design and Analysis of Algorithms
Statistics
Applied Machine Learning
Software Engineering
- Major Elective (Choose 2):
  - Principles of Operating Systems
  - Computer Architecture
  - Computer Networks and Communication

Year 3:

Regression Analysis
Statistical Programming using R
Data Mining
Methods and Applications of Deep Learning
Fundamental Research in Academic Project
Time Series
Big Data Analytics
- Major Elective (Choose 1):
  - Object-Oriented Programming-Java
  - Introduction to Cloud Computing
  - Bayesian Statistics
- Major Elective (Choose 1):
  - Natural Language Processing
  - Statistical Learning
  - Multivariate Statistical Analysis

Year 4:

Data Science Academic Project
Advanced Machine Learning
Advanced Data Analysis
Industrial Training
- Major Elective (Choose 1):
  - Deep Reinforcement Learning and Control
  - Computer Graphics

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