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The term "data science" has been traced back to 1974, when Peter Naur proposed it as an alternative name to computer science. [6] In 1996, the International Federation of Classification Societies became the first conference to specifically feature data science as a topic. [6] However, the definition was still in flux.
The materials in the Data Science and Predictive Analytics (DSPA) textbook have been peer-reviewed in the Journal of the American Statistical Association, [5] International Statistical Institute’s ISI Review Journal, [3] and the Journal of the American Library Association. [4] Many scholarly publications reference the DSPA textbook. [6] [7]
Then, analyze the source data to determine the most appropriate data and model building approach (models are only as useful as the applicable data used to build them). Select and transform the data in order to create models. Create and test models in order to evaluate if they are valid and will be able to meet project goals and metrics.
Data science process flowchart from Doing Data Science, by Schutt & O'Neil (2013) Analysis refers to dividing a whole into its separate components for individual examination. [ 10 ] Data analysis is a process for obtaining raw data , and subsequently converting it into information useful for decision-making by users. [ 1 ]
List of scientific method topics; List of analyses of categorical data; List of fields of application of statistics; List of graphical methods; List of statistical software. Comparison of statistical packages; List of graphing software; Comparison of Gaussian process software; List of stochastic processes topics; List of matrices used in statistics
Social data scientists use both digitized data [22] (e.g. old books that have been digitized) and natively digital data (e.g. social media posts). [23] Since such data often take the form of found data that were originally produced for other purposes (commercial, governance, etc.) than research, data scraping, cleaning and other forms of preprocessing and data mining occupy a substantial part ...
If the trend can be assumed to be linear, trend analysis can be undertaken within a formal regression analysis, as described in Trend estimation. If the trends have other shapes than linear, trend testing can be done by non-parametric methods, e.g. Mann-Kendall test, which is a version of Kendall rank correlation coefficient.