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Msc in Data Science and Business Analytics

Course overview

Qualification Master's Degree
Study mode Full-time, Part-time
Duration 1 year
Intakes March, June, July, August, September, October
Tuition (Local students) $ 9,012
Tuition (Foreign students) $ 11,698


This course programme is established in hopes that students will be equipped with the knowledge and applied skills in data science, data analytics and business intelligence. From here, students can also plan on building their analytical and investigative knowledge and mould their interpretation skills to a whole new level. However, it is always hoped that students will also be able to comprehend the impact of data science through modern processes and business, and other aspects that will be exercised in this course.

Apart from earning a degree, students will have the opportunity to obtain a Joint Professional Certification from the SAS Institute, USA. Around 30% of the curriculum is dedicated to mini projects, allowing for hands-on experience and skill development in Data Analytics. The curriculum covers a wide range of topics including Analytical Technologies, tools like R & SAS Modelers, Data Visualization, Customer/User Behavioral Studies, Forecasting Methods, and Business Intelligence report presentation. External Program Annual Reviews are conducted by International University Partners. The program is supported by an Industry Advisory Panel comprising data analytical experts from various renowned organizations. Additionally, students can engage in research opportunities through APU’s Centre of Analytics - APCA.





$ 9,012
Local students
$ 11,698
Foreign students

Estimated cost as reported by the Institution.


$ 32
Local students
$ 149
Foreign students

Student Visa

$ 513
Foreign students

Every effort has been made to ensure that information contained in this website is correct. Changes to any aspects of the programmes may be made from time to time due to unforeseeable circumstances beyond our control and the Institution and EasyUni reserve the right to make amendments to any information contained in this website without prior notice. The Institution and EasyUni accept no liability for any loss or damage arising from any use or misuse of or reliance on any information contained in this website.

Entry Requirements


• Bachelor’s degree in Computing or related fields with a minimum CGPA of 2.50, or its equivalent qualification as accepted by the Senate.

• Bachelor’s degree in Computing or related fields with a minimum CGPA of 2.00 and not meeting a CGPA of 2.50 can be accepted, subject to a rigorous internal assessment.

• Bachelor’s degree in non-related fields with a minimum CGPA of 2.00 as accepted by the Senate and with relevant working experience, subject to a rigorous internal assessment.

​• Bachelor’s degree in non-related fields with a minimum CGPA of 2.00 as accepted by the Senate and without relevant working experience, subject to passing pre-requisite courses.

Δ Fundamental skills in programming, database, mathematics and statistics would be an added advantage.
* Applicants without a Computing-related Bachelor’s degree must pass the pre-requisite modules to continue with the Master’s Degree.


Note: The above entry requirements may differ for specific programmes based on the latest programme standards published by Malaysian Qualifications Agency (MQA).




• IELTS : 6.0

For more information please click HERE



  • Introduction to R-programming
  • Statistics
  • Database for Data Science
  • Programming in Python

Core Modules:

  • Big Data Analytics & Technologies
  • Behavioural Science, Social Media and Marketing Analysis
  • Data Management
  • Business Intelligence Systems
  • Research Methodology
  • Applied Machine Learning
  • Data Analytical Programming
  • Multivariate Methods for Data Analysis
  • Capstone Project 1
  • Advanced Business Analytics and Visualisation
  • Capstone Project 2


Pathway 1 (Business Intelligence):


  • Behavioural Science, Social Media and Marketing Analytics
  • Time Series Analysis and Forecasting
  • Strategies in Emerging Markets OR Multilevel Data Analysis OR Operational Research and Optimization

Pathway 2 (Data Engineering):

  • Cloud Infrastructure and Services
  • Deep Learning
  • Natural Language Processing OR Building IoT Applications OR Data Protection and Management

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