Data Science

Data science is a relatively new field referring to the multidisciplinary approaches required to transform data into intellectual property. Practical end-to-end data science solutions require expertise in engineering, computer science, statistics, mathematics and substantial domain knowledge depending on the specific application.  The role of the data scientist is not merely technical but also requires considerable experience of developing strategies that will allow organizations to harness the power of data analytics at all levels of management and from daily operations to long-term decisions. Large quantities of data are currently being generated by innovative technologies, including satellites, smart phones, social media, and the numerous sensors, machines and devices that are collectively know as the Internet of Things (IoT).  Big data refers to the volume, velocity and variety of this treasure trove of data that can help us understand how we interact with each other, technology, services and our environment. However, raw data has little value and only when shared and refined can this data be transformed into intelligent insights and actions supported by evidence. Data scientists have the exciting challenge of utilizing big data to improve decision-making, inform policymaking and increasing the efficiency and performance of many sectors.  While data science offers many opportunities, there are many challenges in terms of resources, infrastructure and expertise that must be overcome.  Some examples of data science applications include:

(1) Creating a dashboard to monitor key performance indicators

(2) Segmenting customers based on transactional data and profiles

(3) Forecasting crop productivity using weather observations

(4) Estimating socioeconomic status using satellite and mobile data

(5) Constructing data-driven credit risk scores for individual clients

(6) Transport planning based on mobile call records.