The Pinoy Business Chronicle

Business news and analysis. Covering financial news, economic issues, stock market data, local business, business policy and more.

Business opinions

Data Science and Finance

data science and finance - Data Science and Finance
business statistics 100319 - Data Science and Finance

We are now living in the age of Data Science and Big Data, as the ubiquity and availability of large amounts of data plus advances in technology to store, process, and analyze such data have revolutionized ways of thinking about things and of doing business. If you take a look at your social media accounts and wonder how these outfits are able to anticipate the kind of content you like to consume, the answer is that data science and big data analytics are being harnessed to try to guess exactly that, and with very good results. Want to buy a book from your favorite online merchant and out pop some other suggested books that you never even thought about, but you buy them anyway thanks to the prompt? You guessed it, data science and data analytics had a hand in this as well.

Even the CFA Institute, the organization that grants the Chartered Financial Analyst designation globally, has for years integrated data science and big data into its curriculum, with a major emphasis on finance as the domain expertise. Thus, data science, machine learning, fintech, etc. are all par for the course and part of the study materials, as applied to finance and investments.

But what is Data Science in the first place? Lillian Pearson defines it as “…the practice of using a set of analytical techniques and methodologies to derive and communicate valuable and actionable insights from raw data.” This key is really “insighting,” the ability to extract insights from data that is processed in order to help, say, target customers to consume a particular product or determine which assets to trade via algorithmic trading, etc.

To come up with such insights, there is a great reliance on statistical analyses and procedures such as multivariate linear regression, cluster analysis, principal components analysis, factor analysis, etc. At any rate, this kind of quantitative analytical work is an exciting growth area and demand for data scientists has been steadily increasing through the years. So, for somebody who wishes to study data science and its application specifically in finance, where can one start?

Well, De La Salle University has a graduate program, MS Computational Finance (MSCF), where modern finance and investments are covered, alongside a healthy dose of data science and computer applications. In the program, students learn modern portfolio theory, standard finance, plus behavioral finance, in addition to core quant subjects used in data science, from linear regression, statistics, probabilities, factor analysis, cluster analysis, etc. On top of that, there is heavy use of computer applications for analytical work, from the extensive use of MS Excel and VBA programming for data analytics, plus coding using R, widely used as the main statistical analysis software in the field of big data and which is now being especially emphasized in the program.

What the MSCF Program actually aims to do is marry all three major areas in one seamless application: finance, quantitative analysis, and computer software and technology. But this is exactly what data science does as well, marrying a domain expertise with quantitative analysis and computer technology, so graduates of the program can rightfully consider themselves data scientists, but with a particular domain expertise in finance. What’s even better is that the skills learned in the MSCF Program, while geared towards finance, can be ported to other data science domains since the skills themselves are really widely applicable and portable in the first place.

Additionally, in the current Trading, Software, and Programming class, Day Trading is being introduced as a unique and standalone module, this with the help of a former Wall Street trader. This is because the previous treatment of trading has mostly been from an investments angle rather than from a day trading angle but the current market reality is that there is a need for proprietary traders who can do day trading as a profession. There are a lot of urban myths out, and there are claims that one can do day trading and be a millionaire by just mastering the art of technical analysis. But the truth is more complicated than that. What is sure is that there appears to be big need for a course on Day Trading treated as a profession and the MSCF Program is being upgraded to address this market need as well, on top of its core strengths.

Interested in Data Science with a particular twist in Finance and Day Trading? Maybe the DLSU MSCF Program is the right place for you to start.


Ildemarc C. Bautista, CFA is Vice-President and Head of Research at Metrobank, and has been teaching the core courses of the De La Salle University MS Computational Finance program for almost two decades.

Leave a Reply

Theme by Anders Norén