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Introduction

Data is the currency of all context-based scientific research. It also underpins our modern world, from the flow of data across international banking networks to the complex models of weather forecasting.

The constant generation of data from our digital society feeds into our everyday lives, affecting how we receive healthcare to influencing our shopping habits. In order to handle, make sense of, and exploit large volumes of available data requires highly skilled human insight, analysis and visualisation. The professionals working in this field are called ‘data scientists’, who blend advanced mathematical and statistical skills with programming, database design, machine learning, modelling, simulation and innovative data visualisation.

This programme aims and learning outcomes are built around two guiding principles:

  • To provide comprehensive understanding of the fundamental mathematical and statistical concepts underlying data science, and how they are implemented in algorithms and machine learning techniques to solve a variety of data processing and analysis problems.
  • To provide training in the practical skills relevant to data science, central of which is the ability to write clean and efficient code in industry-recognised languages (in particular, Python and R), but also includes data handling, manipulation, mining and visualisation techniques.

This programme is distinctive in its philosophy of widening participation and provides a route to gain skills and training in data science to those from a background not traditionally associated with the STEM-themes of mathematics, statistics and programming. The programme is designed to be appealing to a broad range of students who are seeking training or up-skilling in data science.

Student Stories

  • test
    To other students considering further education, my advice would be: if you have a strong interest in a subject, don’t be afraid to step out of your comfort zone.
    Alumni Class of 2023 | School of Engineering and Technology
    Master of Science in Data Science, University of Hertfordshire
View More Stories

Duration

Students will be studying 7 modules and are expected to complete the course within 12 months.

Learning Mode

Blended (City Campus & online)

Modules

• Fundamentals of Data Science
• Applied Data Science 1
• Machine Learning and Neural Networks
• Data Mining and Discovery
• Applied Data Science 2
• Data Handling and Visualisation
• Data Science Project

The delivery of modules is subject to change and may not follow the sequence as shown above.

Intakes

Jan, May, Sept

Minimum Class Size

15

Mode of Assessment
Assessment of this programme may include assignments, projects and written exams.

Maximum Candidature
2 years

Graduation Requirements

In order to graduate students must pass all prescribed modules

Duration

Students will be studying 7 modules and are expected to complete the course within 16 months.

Learning Mode

Blended (City Campus & online)

Modules

  • Fundamentals of Data Science
  • Applied Data Science 1
  • Machine Learning and Neural Networks
  • Data Mining and Discovery
  • Applied Data Science 2
  • Data Handling and Visualisation
  • Data Science Project

 

The delivery of modules is subject to change and may not follow the sequence as shown above.

Intakes

  • Jan, May, Sept

 

Minimum Class Size
15

Mode of Assessment
Assessment of this programme may include assignments, projects and written exams.

Maximum Candidature
2 years

Graduation Requirements

In order to graduate students must pass all prescribed modules

Academic Requirements

  • A minimum of a bachelor’s degree in a STEM subject such as Computer Science, Mathematics, Physics or Engineering; OR
  • A minimum of a bachelor’s degree in a near-STEM subject with at least two years of relevant work experience of computing that goes beyond that of an end-user; OR
  • Students who successfully complete the BSc (Hons) Data Science, BSc (Hons) Computing Science and Bachelor of Information Technology at PSB can typically progress onto the MSc Data Science.

English Language Requirement

Students entering the course whose first language is not English, or students whose medium of instruction on their qualifying programme was not English, will be required to demonstrate a proficiency in the English to IELTS 6.5 or equivalent.

Academic Requirements

  • A minimum of a bachelor’s degree in a STEM subject such as Computer Science, Mathematics, Physics or Engineering; OR
  • A minimum of a bachelor’s degree in a near-STEM subject with at least two years of relevant work experience of computing that goes beyond that of an end-user; OR
  • Students who successfully complete the BSc (Hons) Data Science, BSc (Hons) Computing Science and Bachelor of Information Technology at PSB can typically progress onto the MSc Data Science.

English Language Requirement

  • Students entering the course whose first language is not English, or students whose medium of instruction on their qualifying programme was not English, will be required to demonstrate a proficiency in the English to IELTS 6.5 or equivalent.

 

Minimum Age Requirement

  • 18 years old

Course Fees

Singapore-based Students & International Students:

Total Course Fee: S$ 26,748.60

Scenario-Based Mandatory Fees

Singapore-based Students

Application Fee (New Students): S$260.00
Student Development and Administration Fee: S$620.00

International Students:

Application Fee (New Students): S$490.00
Student Development and Administration Fee (New Students): S$1,470.00
Student Development and Administration Fee (Progressing Students):S$770.00

Note: All fees are subject to annual revision.

Miscellaneous Fees

Total Course Fee

Singapore-based Students:

Total Course Fee: S$ 26,748.60 S$ 17,832.40*

*A course fee rebate of S$8,916.20 is available to Singapore-based applicants for the 2025 intake. Contact our programme consultants to find out more.

Scenario-Based Mandatory Fees

Singapore-based Students

  • Application Fee (New Students): S$260.00
  •  

Note: All fees are subject to annual revision.

Miscellaneous Fees

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