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Master of Science Big Data

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Programme Overview

(Intake Dates: To be confirmed)

Manner of instruction delivery

The MSc Big Data is a taught advanced Masters degree covering the technology of Big Data and the science of data analytics. You’ll gain practical skills in big data technology, advanced analytics and industrial and scientific applications.
The course will teach you how to collect, manage and analyse big, fast moving data for science or commerce. You’ll learn skills in cutting-edge technology such as Python, R, Hadoop, NoSQL and Machine Learning. At the same time, you’ll delve into important maths and computing theory, and learn the advanced computational techniques you need to develop your career in data science.

Programme Duration & Mode Full-Time : 12 months Part-Time : 12 months


Age Requirement

  • 20 years old

Academic Requirements

A minimum of a second class honours degree or equivalent in a numerate subject such as maths, computing, engineering or an analytic science.

Mature Applicants

Candidates who are at least 30 years of age with at least 8 years of working experience may apply for admission as Mature Applicants.
To qualify as a mature applicant, you are required to submit a Personal Statement of not more than 200 words and 2 Referee Reports from at least one employer. Shortlisted applicants may be required to attend an interview

English Language Requirements

If English is not your first language you must have one of the following qualifications as evidence of your English language skills:

  • IELTS: 6.0 with 5.5 minimum in each skill
  • Cambridge Certificate of Proficiency in English (CPE): Grade C or above
  • Cambridge Certificate of Advanced English (CAE): Grade C or above
  • Pearson Test of English (Academic): 54 with 51 in each component
  • IBT TOEFL: 80 with no subtest less than 17


Course fees: 20000 for both local and international

Course Application fee:

Course Application Fee* Local Students : S$150 (Onetime Only)
International Students : S$350 (Onetime Only)
  •  Course application fee is non refundable. 
  •  Fees indicated are exclusive of GST.

  • Course Fees
  • Miscellaneous Fees
  • Curriculum / Programme & Award Structure

Fees Breakdown

Total Payable (S$)




Tuition fee



Total Course Fees Payable




(i.)Course Fee is inclusive of Tuition Fees, Exam Fees (one time) and 1 Laptop* or Ipad*
(ii.) FPS Insurance premiums are paid by Amity, unless stated in the Student Contract which has to be paid by the student separately.
(iii.) Fees indicated are exclusive of 7% GST
(iv. ) Payment Mode
  • Cash
  • Cheque (Payable to : Amity Global Institute Pte Ltd)
  • ATM Transfer
  • Internet Banking and Telegraphic Transfer (TT) directly to
  • Bank Name : DBS Bank Ltd
  • Account Name : Amity Global Institute Pte Ltd
  • Account Number : 003-923926-2
  • Bank Code : 7171
  • Branch Code : 003
  • Swift Address : DBSSSGSG
  • Bank Address : DBS Shenton Way Branch, 6 Shenton Way DBS Building Tower 2, Singapore 068809

Students should email (email address: and notify Amity immediately of any payment made via ATM Transfer, Internet Banking and TT.

*Terms and Conditions:
  1. iPad / Laptop will be given to students who have enrolled in the following:
    • University of Stirling -- Bachelor degrees starting at Year 1 or Year 2 only
    • University of Stirling – Master degrees full-time students only
  2. Students who are on Scholarship / Discount are not be eligible for iPad / Laptop.
  3. Students who are eligible must specify their choice for iPad or Laptop during Admission.
  4. iPad / Laptop will be issued within 3 weeks after class commencement with payment of FULL course fee or 1st Instalment (for courses more than 12 months duration) and subject to availability of stock.

Curriculum / Programme & Award Structure

Mathematical Foundation
(10 credits)
Statistics for Data Science
(10 credits)
Representing and Manipulating Data
(20 credits)
Choose ONE (60 credits)
from the following:
  • Dissertation Project
  • Research Dissertation Project
Commercial and Scientific Applications
(20 credits)
Data Analysis
(20 credits)
Cluster Computation
(20 credits)
Relational and Non-Relational Database
(20 credit)

Module Outline

Mathematical Foundations Core : 10 credits

This course will equip student with the some basic mathematical knowledge and problem solving skills.

Statistics for Data Science Core : 10 credits

The course is intended to give students: (i) a basis for the analysis and interpretation of quantitative information; (ii) an understanding of the basic ideas underlying statistical methods at an introductory level; (iii) an understanding of how to overcome problems when analysing big data sets.

Representing and Manipulating Data Core : 20 credits

Basic python will be covered, along with some more advanced libraries such as NumPy and Pandas. Sourcing, collecting and manipulating large amounts of data.

Commercial and Scientific Applications Core : 20 credits

Students will be able to understand the nature of Big Data projects in commerce and in academia. They will gain an appreciation of the advantage gained for companies and researchers from Big Data projects, and an understanding of the difficulties and issues involved in creating such projects. They will be exposed to a wide variety of Big Data applications.


Data Analytics Core : 20 credits

This module covers the analysis of structured and unstructured (text) data using a number of advanced machine learning methods. The module is geared towards teaching an understanding of the methods of processes of applying data analytics to real world problems.

Cluster Computing Core : 20 credits

When the size of a data collection exceeds the capacity of a single computer, the choices are to scale up by buying a bigger computer, which is expensive and limited or to scale out, by adding more cheap computers to a clusters and distributing the data across them. This module covers methods for distributing both data and processing across a cluster of computers using tools such as MapReduce and Spark.

Relational and Non-Relational Databases Core : 20 credits

This course compares traditional relational databases with new NoSQL models and concentrates on the MongoDB document store.

Select ONE (60 credits) from the following: Elective : 60 credits

  • Dissertation Project
  • Research Dissertation Project
This dissertation project will allow students to demonstrate the range of skills learned throughout their course and apply them to a substantial and sustained individual work.
Some students will contribute to ongoing commercially-relevant research projects in the Department, some will be inspired or specified by industry (including through company placement projects), and others will bring along relevant research problems from their own employer/work places.
The term ‘big data’ is a popular phase used to describe a broad range of computing technologies designed to collect, store, access, analyze and understand large quantities of data. Pioneers in the field are companies such as Google, Facebook, Apple and Amazon, who process large quantities of semi-structured data. Most of the methods can be practiced on smaller data sets and across smaller networks for the purposes of teaching, and their scalability studied as part of research projects to cope with Big Data challenges of velocity, variety and multi-dimensionality. This research project will allow students to progress from the MSc Big Data programme into the Professional Doctorate in Big Data Science.
Some students will contribute to ongoing commercially-relevant research projects in the Department, some will be inspired or specified by industry (including through company placement projects provided by The Data Lab), and others will bring along relevant research problems from their own employer/work places.

For more information please refer to