Master of Science Data Science

Contact our friendly and experienced programme consultant, Dr Gavin, by email at cswgavin@singapore.amity.edu or by Phone/WhatsApp at +65 8181 8052.

Data scientists use a range of computational and statistical techniques to unlock insight from data and solve complex problems. This field is central to a multitude of professions and is integral to informed decision-making in technology, business strategies, national policies and more.

Some examples of professions that make use of data science include: data analysts in healthcare, who use patient data to improve treatment outcomes; financial analysts employing algorithms to predict stock market trends; marketing professionals analysing consumer behaviour to craft targeted advertising strategies; and urban planners optimising traffic flow and public transportation systems. The Masters in Data Science programme in Singapore equips graduates with the specialist skills and knowledge to make an immediate and meaningful contribution by facilitating data-driven decisions in a range of industry environments.

Programme Objective

The Master of Science in Data Science programme in partnership with Teesside University is designed to advance the analytical and technical skills of professionals beyond the foundational knowledge acquired in a Bachelor’s degree. This programme focuses on deepening expertise in data science methodologies, enabling graduates to tackle more complex challenges and lead innovative projects within their fields.

The MSc in Data Science emphasises advanced research, specialised techniques, and the application of data science in solving real-world problems at a strategic level. Graduates are thus prepared not only to contribute but to drive decision-making processes and foster innovation in their respective fields.

What to Expect

With a Masters in Data Science, you will delve into the underpinnings of data collection, management, and analysis, learning how to:

• Understand the theoretical background processes for efficient collection, management, secure storage and analysis of large data sets
• Formulate hypotheses about data and develop innovative strategies for testing them by implementing appropriate algorithms to analyse both large and small datasets
• Extract valid and meaningful conclusions from various types of large data sets that can support evidence-based decision making
• Communicate approaches and solutions to data science problems to a range of audiences in a variety of modes
• Identify, select and use appropriate open source and proprietary data management and analysis tools to identify patterns or relationships in large volumes of data

The learning method for this data science course includes keynote lectures from industry guest speakers, as well as tutorials with case studies to illustrate real-world applications of data science. Students will develop their own advanced reflective practices for problem-solving, and assess their solutions against their own research criteria. This approach ensures a deep understanding of data collection and analysis, preparing graduates to lead in the field.

Course Structure & Duration

The Master of Science in Data Science programme is meticulously structured to encompass critical areas of the field, ensuring students develop a comprehensive understanding and practical skills. The core modules are:

Big Data and Business Intelligence: Focuses on the system development life cycle for large scale database applications and services, emphasising the management and visualisation of data for business intelligence.

Data Visualisation: Explores a wide range of charting methods, from traditional bar and pie charts to more innovative stream graphs and tree maps, to represent and interpret quantitative data.

Interactive Visualisation: Teaches dynamic data exploration through interactive graphics, using JavaScript libraries like D3.js to create animated web visualisations that reveal multidimensional data patterns.

Statistical Methods for Data Analytics: Covers essential statistical techniques for both quantitative and qualitative data analysis, preparing students for experimental work with a focus on correlation and regression.

Research Methods: Prepares students for their master’s project with skills in academic research, evidence material generation, and evaluation of professional practice within computing disciplines.

Machine Learning: Introduces students to computational models for pattern recognition and prediction, essential for tasks where algorithmic solutions are unknown or there is a need to adapt to new data.

Computing Masters Project: An in-depth individual study in a specific computing area, leading to the creation of a significant deliverable, such as software, design, prototype, or research findings.

Students can choose from two distinct routes to earn their MSc in Data Science:

Regardless if you opt for a full-time or part-time programme, all students receive the same comprehensive coverage of modules, access to lectures, and tutorial materials, ensuring that every graduate is equally prepared for the demands of a career in data science.