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What is Data Scientist?

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Data science though has been perceived many a time as synonymous to computer science itself, it is now widely recognized and practiced as an independent discipline within the broad spectrum of computer science. A practitioner of data science or one who practices in the field of data science as his or her vocation is called a data scientist. Before we go into the details concerning the job and task of a data scientist, let us take a closer look at the subject of data science itself.

Data science though has been perceived many a time as synonymous to computer science itself, it is now widely recognized and practiced as an independent discipline within the broad spectrum of computer science. Data science is the particular computer science discipline that extending towards the fields of statistics and mathematical analysis for the purpose of analyzing data advances computing by providing superior analytical outcomes to the end user. A practitioner of data science or one who practices in the field of data science as his or her vocation is called a data scientist. Data science is typically a field that incorporates various elements from multitude of scientific disciplines including statistics, mathematics, data engineering, uncertainty modeling, advanced computing, visualization, pattern recognition, etc. Before we go into the details concerning the job and task of a data scientist, let us take a closer look at the subject of data science itself.

What is Data Science?

The discipline data science with its distinct identity as an important analytical discipline, especially for high performance computing analysis and data engineering has existed for more than a decade and continued to evolve in technical significance and applying skills. Data science like other computer disciplines is a performance driven applied science that continues to push the boundaries of our gained knowledge, skill set and applied methods over period of time. A data scientist unlike practitioners of many other scientific disciplines and fields can come from different scientific practicing backgrounds and incorporate his skills in the field of data science. A statistician, mathematical analysts or specialist in uncertainty modeling or a computer engineer, all can see himself in the role of a data scientist by orienting himself in the performance driven field of data science in one or numerous projects.

In the final analysis, barring most of the technical terms data science is the field of practice within the broad spectrum of computing that looks after analyzing the data to derive valuable insights from the data researched. These insights then again can result in business analysis in relation to other specific set of information leading to superior decision making or performance in a particular business or operative field. Any business enterprise, especially the large ones with numerous departments or sections of its operative force produce great chunks of unclassified or non-structured or semi-structured data comprising various kinds of calculations, trends and signals of forthcoming course of events that a data scientist produces as the outcome of his analysis.

What a Data Scientist does?

As we have already seen from the above description of magnitude of scientific and computing fields incorporated within the spectrum of data science, a practitioner of this field called data scientist require really versatile skill set. In most cases data scientists are normally specialists in different skill sets relating to the specific domains like sales and marketing, finance, medical security, fraud and human resource, operation and infrastructure, etc. From statistical analysis to retrieval of texts to graphical analysis of quantitative and qualitative data, a data scientist performs variety of analytical task within his specialized domain or comprising several such domains. The academic areas of research that a data scientist must undergo include cloud computing, database and information integration, natural language processing and extraction of information, information retrieval, computer visualization, analysis of data from social network, etc.

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Posted on: May 6, 2013

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