Bachelor of Engineering in Data Science (Honours)
- Key Information
Course Name:
Bachelor of Engineering in Data Science (Honours)
University Name:
Xiamen University Malaysia (XMUM)
Campus:
Selangor
Qualification:
Bachelor
Duration:
4 years
Intake:
February, April, September
IELTS:
Not Required
Study Mode:
On-campus
Study Type:
Full time study
Program Mode:
Coursework
Yearly Tuition Fees
| Year | Fee |
|---|---|
| 1st Year | MYR 29,000 |
| 2nd Year | MYR 29,000 |
| 3rd Year | MYR 29,000 |
| 4th Year | MYR 29,000 |
Other Fees
| Description | Fee |
|---|---|
| Application Fee | MYR 100 |
| International Student Fee (Including visa application & renewal) | MYR 2,500 |
| Registration Fee | MYR 200 |
| Security Deposit (Refundable) | MYR 1,000 |
Campus
Selangor
Qualification
Bachelor
المدة
4 years
Intake
February, April, September
IELTS
Not Required
Study Mode
On-campus
Study Type
Full time study
Program Mode
Coursework
Course Fee for International Students
Yearly Tuition Fees
| Year | Fee |
|---|---|
| 1st Year | MYR 29,000 |
| 2nd Year | MYR 29,000 |
| 3rd Year | MYR 29,000 |
| 4th Year | MYR 29,000 |
Other Fees
| Description | Fee |
|---|---|
| Application Fee | MYR 100 |
| International Student Fee (Including visa application & renewal) | MYR 2,500 |
| Registration Fee | MYR 200 |
| Security Deposit (Refundable) | MYR 1,000 |
Course Overview
the Bachelor of Engineering in Data Science (Honours) at Xiamen University Malaysia (XMUM) operates as a sophisticated pedagogical framework designed to ground scholars in the epistemological foundations and transformative methodologies of systemic information governance and predictive stewardship. This program facilitates an intellectual transition to executive-level analytical proficiency by synthesizing rigorous theoretical inquiry with applied data praxis, ensuring that candidates can navigate the multifaceted complexities inherent in extracting actionable intelligence from high-volume, high-velocity datasets. To address the high-fidelity demands of the globalized “Big Data” economy, the curriculum is strategically engineered to bridge the technical divide between raw computational power and sector-specific problem solving. A pivotal feature of the program at XMUM is its focus on interdisciplinary synergy, leveraging the collective expertise of PhD-level faculty from the Computer Science, Artificial Intelligence, Software Engineering, and Mathematics programs. By integrating technical expertise with a focus on heuristic critical analysis, the curriculum prepares students to deploy advanced statistical and mathematical models across diverse domains such as healthcare, commerce, and the public sector. Through intensive study of computational frameworks and statistical theory, the program emphasizes the development of the “hard skills” required for high-level data mining, machine learning, and quantitative visualization. The pedagogy, supported by a research-active faculty with global perspectives, ensures that graduates possess the analytical fluency necessary to solve complex data challenges in a rapidly evolving market. Ultimately, the program serves as a critical mechanism for professional and technological advancement, establishing the foundational competencies and strategic mindset necessary for graduates to thrive as data architects and contribute meaningfully to the evidence-based evolution of the global digital landscape.
Curriculum
Programme Structure
| Year | Themes | Key Modules |
|---|---|---|
| Year 1 | Data & Programming Foundations | Calculus, Linear Algebra, C and C++ Programming, Data Structures, Introduction to Data Science, and Intelligence Application. |
| Year 2 | Machine Learning & Algorithm Core | Applied Machine Learning, Python and Tensorflow Programming, Principles of Artificial Intelligence, Statistics, Probability Theory, Database, and Design and Analysis of Algorithms. |
| Year 3 | Advanced Analytics & Deep Learning | Methods and Applications of Deep Learning, Big Data Analytics, Data Mining, Regression Analysis, Time Series, Statistical Programming using R, and Fundamental Research. |
| Year 4 | Advanced Research & Industry | Advanced Machine Learning, Advanced Data Analysis, Data Science Academic Project, and Industrial Training. |
| Major Electives | Specialized Computation & Stats | Natural Language Processing, Deep Reinforcement Learning and Control, Cloud Computing, Statistical Learning, Operating Systems, and Computer Networks. |
Field of Research
N/A