In August 2019, a new second major in Data Science and Analytics (DSA) will be open to students across Singapore Management University’s six Schools. The first targeted batch of students are all first-year students from the intake year of Academic Year 2019/2020 and all second-year students from the intake of Academic Year 2018/2019. This new major aims to equip students with the ability to transform large amount of data into useful information for decision-making in an increasingly data-driven world.

Speaking at an information session on 14 March 2019, Education Professor Kwong Koon Shing, said: “The increasing volume of data and advanced computing technologies pose new opportunities as well as challenges for data analysts to transform data into useful information for decision making. Wherever there is data, we will be trying to analyse it in the most effective manner such that it becomes useful information. There is a need to train more data analysts to meet demand.”

Prof Kwong shared that the significant shortage of data analysis talent in Singapore today has created a pressing need for formal and rigorous training for students to develop appropriate skills to analyse big data with powerful statistical software. To help students better discern how data science differs from statistics and computing, Prof Kwong presented these points in his presentation:

• Statistics is the science of learning from data through statistical inference, stochastic modelling, and predictive analysis
• Computing is any operation that involves computers, such as computer engineering, software engineering, information systems and technology, etc.
• Data Science is the integration of Statistics and Computing to learn from raw data and then extract useful information for decision making in the most efficient and effective way

Andy Goldin, Data & Analytics Director, PwC South East Asia Consulting, said, “In 2019 the ability to conduct analytics at scale is necessary for organisations to innovate and stay competitive. A company’s capability to process voluminous data sources and translate them into actionable insights allows for better, faster and smarter decision making. Investment in this area is at new heights, and with the advent of Artificial Intelligence, insights are becoming more accessible but less well understood.”

Within this context, the new DSA major offered by SMU’s School of Economics in close collaboration with the School of Information Systems, trains students to deploy simulation and predictive approaches to solve real-life problems in all private and public institutions. Students would learn to apply state of the art data analysis approaches, gain an understanding of computer intensive methods and build reliable stochastic and predictive models by conducting proper data checking and validation, among other learning outcomes. Mastering these skills and knowledge would open doors to careers in business, economics, finance, insurance, risk management and social sciences.

Prof Kwong underscored the fact that students taking the DSA second major will learn practical applications of statistical modelling, simulation and predictive modelling. In line with SMU’s future-ready curriculum, the DSA second major had been rigorously designed with the objective of strengthening and creating a pipeline of talent with skills that empower them to thrive in a data-driven world. The curriculum of DSA adopts a hands-on pedagogy with extensive training in R* programming, an open source and powerful language used ubiquitously for statistical analysis and data management.

“R is one of the most popular statistical languages, used by the likes of Google, Microsoft and Uber,” said Prof Kwong. Prof Kwong deep dived into a practical and illustrative example of how he deployed R programming to effectively analyse and present data on how students from SMU’s schools fared in their assignments within the statistics module he was teaching.

Lim Kah Lok, presently a first-year student at SMU’s School of Accountancy, commented: “We are living in an era of Big Data, where data science now plays a key role in many different industries, including healthcare, social science, education and finance. I am considering the DSA second major, with a view towards putting the skills that I will pick up to practical use, to implement solutions that will effectively address business challenges.”

Sanaya Mahajan, currently a second-year student at SMU’s School of Economics, also expressed interest in taking up the DSA second major. Sanaya, who is majoring in Quantitative Economics, commented: “Data is everywhere today but to know what it is saying is becoming more and more important. Personally, I enjoy working with qualitative and quantitative data, and I truly believe in its power to resolve real world problems. Presently, with my first major in Quantitative Economics, I like exploring economic models, econometrics and forecasting. I think a second major in DSA will complement this interest. It will help me stay relevant in this data-driven world. What also catches my attention is the fact that it brings together your domain knowledge with elements of mathematics, statistics and computer science – subjects I enjoy studying and look forward to having them applied in the career I pursue.”

Sanaya shared that her sister, who is working with data and analytics in the financial services industry, is a major source of motivation, keeping her encouraged and passionate about a career revolving around data.

Darren Fong, presently a first-year student at SMU’s Lee Kong Chian School of Business, said: “I believe the second major in Data Science and Analytics will equip me with the necessary statistical and programming skills required for any data scientist career path. The major will also train me to become proficient in statistical and data analysis. After graduation, I am keen to pursue the career path of a data analyst. I hope to help businesses improve their decision-making process by transforming and interpreting their data to useful information.”

Andrew Koh, presently a second-year student at SMU’s School of Economics, was initially interested in taking up Applied Statistics as a second major. He decided that the DSA second major would be a better fit with his career aspirations given that its curriculum involves a combination of statistical theory with the hands-on application through R-programming.

“While R-programming may be unfamiliar, I believe that it can be an extremely useful tool for economics students. Especially for those with an interest in the statistical side of economics such as econometrics (like myself), R-programming is one of the most useful tools that is widely adopted by most companies. Hence, literacy in R-programming is often a requirement and not a ‘plus’. Furthermore, because of my career aspiration of becoming an economist, I believe that this second major will equip me well for it,” said Andrew.

Andrew aspires to be an economist. He shared, “While the role of an economist does not necessary require hard coding like a data-scientist, the application of statistical modelling coupled with computing is essential. Economists today will often be required to work with large data sets to perform statistical tests and analysis, to produce models as well as to generate forecasts. Without strong foundations in statistics and programming (specifically for statistics), it will make performing that role extremely difficult since it would be limiting oneself to just the theoretical aspect. Especially in today’s world where we are working with big data, we simply cannot rely on manual by-hand calculation of statistical estimates. In the case of time-series / panel data models where we work with thousands of observations (or even more), we will have to use computer programming tools to assist us.

Therefore, I believe that being able to fully utilise these programming tools (instead of relying on a separate colleague at work) for economic analysis would be advantageous. This is because we would possess better economic intuition and be able to identify inaccuracy immediately if the result seems incongruent. In fact, the importance of programming for statistics was further made certain to me after taking ECON207 and ECON233 in SMU, where Prof Anthony Tay gave us a hands-on experience with R-programming in our assignments. In order to fully apply our theoretical knowledge, I found that having a deeper knowledge of using R is imperative to excel in higher level econometrics.”

*R is one of the most popular open source software packages for statistical analysis. Data scientists and statisticians use R programming language for data analysis in many fields. R users help to add new features to tackle new statistical issues every day, and encourage different domains of experts to work together by communicating in the R programming language. DSA will equip students with up-to-date R programming skills in data extraction, data cleansing, statistical analysis, predictive modelling and data visualisation.