Master of Science in Information Technology (Data Science)

Master of Science in Information Technology (Data Science)

Data Science is a specialty field that is continuing to grow significantly across all industries. Data Science experts are needed in most government agencies. Businesses depend on big data to better serve their customers. Data Science careers are in high demand, and this trend will continue to grow exponentially. Currently, the fastest growing role in Singapore is the Data Scientist1 and the demand for talent is outstripping the supply.

MIT Data Science Infographic MIT Data Science Infographic

The growth of big data has led many organisations to categorise Data Scientists as a "crucial role" within their technology department. Data Scientists who can aptly apply mathematical and analytical skills, as well as business acumen, are highly sought after in today’s market.

Annual demand for the fast-growing new roles of Data Scientists, Data Developers and Data Engineers will reach nearly 700,000 openings by 20204. 59% of all Data Science and Analytics job demand is in Finance and Insurance, Professional Services and IT5.

Pursue senior management roles by acquiring specialised skills in Data Analytics and Data Science. Upskill yourself with a Master of Science in Information Technology (Data Science) Degree programme. Having advanced education could provide you an edge and a higher salary.

Data Scientist's Essential Skills for the Competitive Edge

A Data Scientist is one of the most sought-after positions in the technology and business industries. Data Science and Analytics professionals with MapReduce, Apache PIG, Hive and Hadoop skill sets have a competitive advantage over others. Other must-have skill sets include Machine Learning, Big Data, Data Science, NoSQL and Predictive Analytics.

Top Hiring Industries
  • Computer Software
  • Finance
  • Financial Services
  • Higher Education
  • Insurance
  • Internet
  • IT
  • Professional Services
Career Opportunities
  • Track the behaviour of applications used within a business and how they interact with each other and with users. Application Architects are focused on designing the architecture of applications, including building components like user interface and infrastructure.

  • Business Intelligence (BI) Developers design and develop strategies to assist business users in quickly finding the information they need to make better business decisions. Extremely data savvy, they use BI tools or develop custom BI analytic applications to facilitate the end users’ understanding of their systems.

  • Transform and manipulate large data sets to suit the analytical outcome for companies. For many companies, this role can also include tracking web analytics and analysing A/B testing. Data Analysts also aid in the decision-making process by preparing reports for organisational leaders who effectively communicate trends and insights gleaned from their analysis.

  • Ensure data solutions are built for performance and design analytics applications for multiple platforms. In addition to creating new database systems, Data Architects often find ways to improve the performance and functionality of existing systems, as well as working to provide access to Database Administrators and Analysts.

  • Perform batch processing or real-time processing on gathered and stored data. Data Engineers are also responsible for building and maintaining data pipelines which create a robust and interconnected data ecosystem within an organisation, making information accessible for Data Scientists.

  • Find, clean and organise data for companies. Data Scientists will need to be able to analyse large amounts of complex raw and processed information to find patterns that will benefit an organisation and help drive strategic business decisions.

  • An Enterprise Architect is responsible for aligning an organisation’s strategy with the technology needed to execute its objectives. To do so, they must have a complete understanding of the business and its technological needs to design the systems architecture required to meet those needs.

  • Oversee that all business systems are working optimally and can support the development of new technologies and system requirements. A similar job title is the Cloud Infrastructure Architect, who oversees a company’s cloud computing strategy.

  • Machine Learning Engineers create data funnels and deliver software solutions. They typically need reliable statistics and programming skills as well as a knowledge of software engineering. In addition to designing and building machine learning systems, they are also responsible for running tests and experiments to monitor such systems' performance and functionality.

  • Research new data approaches and algorithms to be used in adaptive systems, including supervised, unsupervised and deep learning techniques.

  • Statisticians work to collect, analyse and interpret data to identify trends and relationships which can inform organisational decision making. Additionally, their daily responsibilities include designing data collection processes, communicating findings to stakeholders and advising corporate strategy.

Data Science Salary Guide
Data Science Salary Guide
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