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Big data provides customer behavior pattern spotting for marketers, since all human actions are being quantified into readable numbers for marketers to analyze and use for their research. [116] In addition, big data can also be seen as a customized product recommendation tool.
Data analysis is a process for obtaining raw data, and subsequently converting it into information useful for decision-making by users. [1] Data is collected and analyzed to answer questions, test hypotheses, or disprove theories. [11] Statistician John Tukey, defined data analysis in 1961, as:
Exploratory data analysis is an analysis technique to analyze and investigate the data set and summarize the main characteristics of the dataset. Main advantage of EDA is providing the data visualization of data after conducting the analysis.
Many organizations, including governments, publish and share their datasets. The datasets are classified, based on the licenses, as Open data and Non-Open data . The datasets from various governmental-bodies are presented in List of open government data sites .
A considerable amount of data quality research involves investigating and describing various categories of desirable attributes (or dimensions) of data. Nearly 200 such terms have been identified and there is little agreement in their nature (are these concepts, goals or criteria?), their definitions or measures (Wang et al., 1993).
Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession. [4] Data science is "a concept to unify statistics, data analysis, informatics, and their related methods" to "understand and analyze actual phenomena" with data. [5]
In this view, data becomes information by interpretation; e.g., the height of Mount Everest is generally considered "data", a book on Mount Everest geological characteristics may be considered "information", and a climber's guidebook containing practical information on the best way to reach Mount Everest's peak may be considered "knowledge".
From an applied perspective, research in high-dimensional statistics was motivated by the realisation that advances in computing technology had dramatically increased the ability to collect and store data, and that traditional statistical techniques such as those described in the examples above were often ill-equipped to handle the resulting ...