The key pillars of data science include data engineering, data analytics and data visualization. Here is a short introduction of each of these pillars:

Data Engineering – It is about building a sound data foundation. It involves acquiring the right data from a variety of sources, integrating data together, and managing the associated processes to ensure data quality, integrity and reliability of underlying data.

Data analytics – It is about building algorithms that can process data to take decisive actions. Machine learning methods can now enable analytical models to improve with time as more and more data is available.

Data visualizations – Apart from conveying the final insights, data visualizations also enable us to understand the details behind complex analytical algorithms, thereby providing us a way to understand how machines think in an extremely automated environment.

Big data and open source technologies have recently created a revolution in these areas, allowing us to process huge volumes of varied data in real time at drastically lower cost, thereby facilitating a lot of new applications that were previously either not possible or extremely expensive. Application programming interfaces facilitate collaboration by enabling the sharing of data and algorithms, so that we dont need to reinvent the wheel.

Data science is opening up numerous gateways to new, innovative and personalized service offerings and is automating many complex and tedious tasks. As the number of applications of data science is fast outpacing other information technology applications, it is gaining prominence as an academic discipline within universities and various academic institutions.