"Manage your huge data’s with data science engineering techniques and software’s"
Data Science is a disciplinary field of scientific methods, processes and systems that are needed for extracting knowledge or learning from data, that can further be available in structured or unstructured forms. It is also known as data-driven science where the works or researches are done based on the information gathered from the available data as raw inputs to the systems. This technique is very similar to Data Mining.

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Data Science includes the concept for unifying statistics problems, analysing huge amounts of data, and many more similar methods in order to attain an understanding with the data and their forms. This discipline draws the theories and techniques from broad areas of mathematics, statistics, information technology & science, and computer science.
The fields of it’s implication hugely includes among the following –
1) Machine learning
2) Classification
3) Cluster analysis
4) Data mining
5) Databases
6) Visualization

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Data Science is considered to be the 4th paradigm of science, as estimated by Turing award winner – Jim Gray. He also stated that every other disciples of science is getting hugely effected and changed due to the impact of the IT (Information Technology).

Data science scientists are expected to produce the results or answers in days, often in months as well. They uses their abilities for the following –

1) Finding and interpreting rich data sources.
2) Managing large loads of data despite of the availability of the hardware, software or network bandwidth constraints.
3) Merging data sources.
4) Ensuring data consistency.
5) Visual representation of data for easy understanding.
6) Building mathematical models.
7) Presenting and communicating with and from the data.

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There have been a lot of software applications that have been used throughout these years for performing the tasks by the data scientists, but in between the year 2010 and 2011, data science software reached a point where it could no longer be used for further processing as the amounts of data increased hugely and the time for processing also extended to an unacceptable stage. Due to this, the supplementing to the proprietary data science software has been done by the open-source software, that enables the modifications and extenuations to the proprietary software and also allowing generating and sharing of the result algorithms.


Data Science is not only a field of technology and mathematics, rather the data scientists also requires effective combination skill of both technical skill and soft skills to transform the raw data inputs into a presentable form that is acceptable by the readers or other consumers.

• Subrata Debnath
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