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.
The key pillars of data science include data analytics, data visualizations and data engineering. Here is a short introduction of each of these pillars:
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.
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.
It is about building the engineering capability to process data at scale, in real time, efficiently and at low cost. More data gives better decisions. Ability to process more data efficiently make analytical models useful. Data engineering involves acquiring the right data from a variety of sources, storing it, integrating data together, and managing the associated processes to ensure data quality, integrity and reliability of underlying data.