What is Data Science?

January 6, 2020

In a recent webinar held by the Trinidad and Tobago International Financial Centre, Mr. Eddy Devisse, Managing Director – DIV Solutions Ltd., defined data science as “a multi-disciplinary field focused on deriving insight from data and development of predictive models to help organisational improvement”.

The field of data science has become increasingly relevant in recent times, as large amounts of structured and unstructured data or ‘big data’ is generated along with the computing capabilities and tools to analyse it, answer questions, and make predictions. Data science blends the use of statistics, computer science, and mathematics, and involves data cleaning and formatting, data visualisation, predictive analysis, and machine learning.

Data scientists therefore collect, analyse, and interpret large volumes of data, to detect patterns, trends, and relationships in data sets. In 2008, Hal Varian, Chief Economist at Google and UC Berkeley Professor of Information Sciences, Business, and Economics, stated that, “the ability to take data — to be able to understand it, to process it, to extract value from it, to visualise it, and to communicate it would be a hugely important skill”, and today we see the importance of the data scientist field as individuals find answers to novel problems. Data scientists are increasingly recognised as important, not just in business and academia but across industries, and has been ranked as one of the top three jobs in America by Glassdoor for five years in a row.


Companies are applying big data and Data Science to everyday activities and several common examples are presented below:

  1. Travel

Data science is used in dynamic pricing models to constantly adjust the price of goods/services depending on real time data such as weather patterns, availability of transport, preferences, holidays, sporting events and even phone battery levels. For example, Uber uses a dynamic pricing algorithm to adjust rates based on a number of variables, such as time and distance of your route, traffic and the current rider-to-driver demand. Thus, customers pay a higher price during peak times when demand is higher, but this change in price is also expected to attract more drivers to an area so everyone can get a ride.

  1. Healthcare

Data Scientist are also currently tracking, modelling, understanding and predicting the spread of COVID-19. Francesca Dominici, Co-Director of the Harvard Data Science Initiative, has identified data science as paramount to “understanding the factors that slow the rate of infection, understanding the role of airborne transmission -which is critical to understanding whether we can reopen the schools – identifying environmental and socioeconomic factors, and tracking mobility to better understand key behavioural interventions to contain the spread of the virus”.

  1. Sports

Most football clubs have data analyst departments and the Liverpool Football Club uses Data Science to improve their games. Players and sporting trends are evaluated by crunching numbers and pitch control visualisation which captures the regions of space controlled by certain players to see how best to utilise the pitch. Tim Waskett, member of Liverpool’s data science team, has indicated that by combining event data and tracking data, Liverpool can figure out how each action on the pitch impacts the probability of a goal being scored.

  1. Social Media

The automatic tagging suggestion which occurs when Facebook users upload an image with a group of friends or people on Facebook is designed on an algorithm which uses face recognition.

  1. Sales

Netflix held a competition in 2006-2009 called “The Netflix Prize” with a million-dollar prize to anyone that could create the best collaborative filtering algorithm to predict user ratings for films using a training data set of over 100 million ratings from 480 thousand randomly-chosen, anonymous Netflix customers over 17 thousand movie titles. This algorithm now assists Netflix with knowing just what shows you’ll love to binge watch!

  1. Credit and Insurance: Finance

Data Science has also impacted the development of InsurTech (Insurance Technology) as companies use technology to disrupt the traditional insurance industry. Financial institutions gather and store a wide range of data such as customer’s personal details, insurance claims data, membership and provider data, benefits, and medical records etc. Data can be compiled,


A Data Science Innovation Hub which was launched in July 2020 in partnership with DIV Solutions Ltd is expected to identify a Pilot Project which will serve to identify opportunities in the local market for the design and applications of Data Science in the Financial Services sector in Trinidad and Tobago. The Trinidad and Tobago International Financial Centre will continue to be a “Resourceful Ally” and drive innovative research in the local Financial Services sector, as we continue to work with our partners and stakeholders.