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MSc in Data Science and Business Analytics (ODL)

Course overview

Statistics
Qualification Master's Degree
Study mode Part-time, Online
Duration 3 years
Intakes March, June, August, October
Tuition (Local students) ₹ 675,393
Tuition (Foreign students) ₹ 688,688

About

APU's MSc in Data Science and Business Analytics program is designed to give you an advantage with the latest technologies associated with the Fourth Industrial Revolution.

Besides earning a degree, you can also get a Joint Professional Certification from SAS Institute, USA. About 30% of the program involves doing mini projects, which help you learn practical skills in Data Analytics.

You'll cover a lot of topics, including Analytical Technologies, using tools like R & SAS Modelers, Data Visualization, studying Customer/User Behavior, Forecasting Methods, and presenting Business Intelligence reports.

The program gets reviewed yearly by International University Partners. Plus, you'll have support from an Industry Advisory Panel made up of experts from companies like Petronas ICT, RedTone, SharePoint, CyberSecurity Malaysia, Maxis, IBM, Microsoft, Fusionex, and Axiata.

There are also research opportunities available through APU’s Centre of Analytics (APCA).

Admissions

Intakes

Fees

Tuition

₹ 675,393
Local students
₹ 688,688
Foreign students

Estimated cost as reported by the Institution.

Application

₹ 2,659
Local students
₹ 12,408
Foreign students

Student Visa

₹ 42,544
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

GENERAL 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).

 

ENGLISH REQUIREMENTS

INTERNATIONAL STUDENTS

• IELTS : 6.0

For more information please click HERE

Curriculum

PRE-REQUISITE MODULES
(FOR NON-COMPUTING STUDENTS: DURATION: 1 MONTH (FULL-TIME) / 4 MONTHS (PART-TIME))

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

CORE MODULES

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

SPECIALIZATION MODULES (CHOOSE 1 PATHWAY ONLY)

 

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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