Data science, also known as data-driven science, is an interdisciplinary field about scientific methods, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining.Data science is about dealing with large quality of data for the purpose of extracting meaningful and logical results/conclusions/patterns. It’s a newly emerging field that encompasses a number of activities, such as data mining and data analysis. It employs techniques ranging from mathematics, statistics, and information technology, computer programming, data engineering, pattern recognition and learning, visualization, and high performance computing. This paper gives a clear idea about the different data science technologies used in Big data Analytics. Data science is a "concept to unify statistics, data analysis and their related methods" in order to "understand and analyze actual phenomena" with data. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science, in particular from the subdomains of machine learning, classification, cluster analysis, data mining, databases, and visualization.Data Science is much more than simply analysing data. There are many people who enjoy analysing data who could happily spend all day looking at histograms and averages, but for those who prefer other activities, data science offers a range of roles and requires a range of skills. Data science includes data analysis as an important component of the skill set required for many jobs in the area, but is not the only skill. In this paper the authors effort will concentrated on to explore the different issues, implementation and challenges in Data science.
Data science, information, data, analytics,unstructured data, preservation, data visualization, extraction
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