Master of Science in Data Science and Business Analytics (ODL)
Asia Pacific University of Technology & Innovation · Kuala Lumpur, Malaysia
QS World University Ranking: N/A
Quick Overview
Apply Now📘 Program Overview
APU’s Master of Science in Data Science and Business Analytics (ODL) is offered through Open & Distance Learning (ODL) and is designed for applied learning of data science and analytics skills in line with Industry 4.0.
The programme includes compulsory core modules, a chosen specialisation pathway with elective modules, and a Capstone Project made up of two parts, with a listed programme duration of 2 years 6 months.
🎯 Course & Curriculum
Course structure
- Complete pre-requisite modules (for non-computing students): Programming in Python, Database Fundamentals, and Statistics (ODL duration stated as 4 months).
- Start core study including Big Data Analytics & Technologies, Data Management, and Business Intelligence Systems.
- Build the foundation for your Capstone work through Research Methodology for Capstone Project.
- Progress through Applied Machine Learning and Data Analytical Programming.
- Develop analytical depth with Multivariate Methods for Data Analysis.
- Begin Capstone Project 1 as part of the programme’s Capstone (2 parts).
- Choose 1 pathway (Business Intelligence or Data Engineering) and complete 3 specialisation modules.
- Business Intelligence pathway specialisation examples include Behavioural Science, Social Media and Marketing Analytics and Time Series Analysis and Forecasting.
- Data Engineering pathway specialisation examples include Cloud Infrastructure and Services and Deep Learning.
- Complete Capstone Project 2 and Advanced Business Analytics and Visualisation.
Curriculum
Curriculum credit-hours table for MSc in Data Science and Business Analytics (ODL) at Asia Pacific University of Technology & Innovation, using the official module schedule and module list published by APU for DSBA (credit hours per module shown in the module codes as 3-M for taught modules and 5-M/7-M for capstone modules).
| Semester | No. of modules | Module / subject category | credit hours | Semester total |
|---|---|---|---|---|
| Semester 1 | Core modules | Big Data Analytics & Technologies | 3 | 15 |
| Data Management | 3 | |||
| Business Intelligence Systems | 3 | |||
| Applied Machine Learning | 3 | |||
| Research Methodology for Capstone Project | 3 | |||
| Semester 2 | Core modules and research module progression | Multivariate Methods for Data Analysis | 3 | 15 |
| Data Analytical Programming | 3 | |||
| Advanced Business Analytics and Visualization | 3 | |||
| Behavioural Science, Social Media and Marketing Analytics | 3 | |||
| Time Series Analysis and Forecasting | 3 | |||
| Semester 3 | Pathway/module electives | Multilevel Data Analysis | 3 | 15 |
| Strategies in Emerging Markets | 3 | |||
| Operational Research and Optimization | 3 | |||
| Cloud Infrastructure and Services | 3 | |||
| Data Protection and Management | 3 | |||
| Semester 4 | Capstone projects | Capstone Project 1 | 5 | 12 |
| Capstone Project 2 | 7 | |||
| Total | Core modules, pathway modules/electives, and Capstone Projects | |||
Career outcomes
- You graduate with job-ready skills across data science, big data analytics, and business intelligence, helping you target roles such as Data Engineer, Data Analyst, Business Analyst, and Data Scientist.
- Your pathway choice (Business Intelligence or Data Engineering) helps you focus your learning toward the type of analytics work employers recruit for, from forecasting and decision analytics to cloud and deep learning-oriented projects.
- The programme includes a Capstone Project (two parts), so you build real portfolio-style work around analytics problem-solving rather than only theory.
- You gain industry-aligned exposure through professional certification linked to SAS (joint professional certificate) and TIBCO certification requirements for completing specified analytics learning areas.
- You receive ongoing programme support through an industry advisory panel and annual programme reviews with international university partners, strengthening the industry relevance of your learning.
- You learn to present business intelligence reports and apply data analytics tools and techniques, supporting career progression into analytics management and decision analytics roles.
Employability rate
APU states a 100% employability rate for this programme’s cohort.
Together, this suggests strong demand for graduates with data science and business analytics skills and a reliable career outlook for international students who complete the programme requirements.
✅ Entry Requirements
Academic qualification
| Requirement | Details |
|---|---|
| Bachelor's degree | International applicants must hold a bachelor’s degree. Accepted options include a bachelor’s degree in Computing or related fields with a minimum CGPA of 2.50, or with a minimum CGPA of 2.00 where the candidate is subject to rigorous internal assessment. |
| Computing-related bachelor’s degree (or bridging) | If your bachelor’s degree is not computing-related, you must pass the pre-requisite modules to continue with the master’s degree. The programme lists pre-requisite modules for non-computing students as Programming in Python, Database Fundamentals, and Statistics. |
Language requirements
English requirements for this programme are listed for international students. If your previous bachelor’s or master’s qualification was taught in English (or you come from an English-speaking country), you may apply for an English requirements exemption with supporting documents.
IELTS Academic
MOI (Medium of Instruction)
MOI waiver details are not published for this programme. See the English test requirements above.
💶 Fees & Funding
| Item | Amount | Notes |
|---|---|---|
| Total tuition fee | MYR 37800 / total program | Total for International: RM 37,800 (excluding deposits and other miscellaneous fees). Fees stated exclude the 6% Sales and Service Tax (SST) imposed by the Malaysian Government. |
Proof of financial resources
Important dates
Application window
| Milestone | October - Winter intake |
|---|---|
| Application portal opens | August 21, 2026 |
| Application deadline (final submit) | N/A |
| Application deadline (EU students) | N/A |
| Application deadline (Non-EU students) | N/A |
| Recommended apply-by | N/A |
| Semester begins | October 30, 2026 |
Pre-application & reviews
| Milestone | October - Winter intake |
|---|---|
| VPD request - recommended | N/A |
| VPD result expected | N/A |
| APS - recommended | N/A |
| Start eligibility check | N/A |
Document submission deadlines
| Milestone | October - Winter intake |
|---|---|
| Application submission deadline | N/A |
| Document submission deadline (EU students) | N/A |
| Document submission deadline (Non-EU students) | N/A |
| Language certificate deadline | N/A |
| Official transcripts deadline | N/A |
| Certified translations deadline | N/A |
| Postal / hard-copy documents deadline | N/A |
| Missing documents (if requested) | N/A |
📝 How to Apply
Prepare your eligibility documents
Check that you meet the programme entry requirements for international applicants, including your bachelor’s degree background and CGPA. If your bachelor’s is non-computing-related, plan for pre-requisite modules as stated in the programme entry requirements. Prepare your passport and academic documents before you start the online application.
Submit the online application during the intake window
Apply during the published intake period for this programme. Upload the required documents as part of the application submission. Ensure you include your English test result (IELTS Academic) or apply for an English requirements exemption with supporting documents if eligible.
Complete admission and registration steps
After your application is assessed, follow the university instructions for offer and enrolment processing. Pay the required fees as scheduled to secure your place in the intake. If any documents require certified translation, provide them as requested by the admissions process.