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Around the 1970s/1980s the term information engineering methodology (IEM) was created to describe database design and the use of software for data analysis and processing. [3] [4] These techniques were intended to be used by database administrators (DBAs) and by systems analysts based upon an understanding of the operational processing needs of organizations for the 1980s.
Required skills for database administrators include knowledge of SQL, database queries, database theory, database design, specific databases, such as Oracle, Microsoft SQL Server, or MySQL, storage technologies, distributed computing architectures, operating systems, routine maintenance, recovery, and replication/failover.
According to the Open Group Architecture Framework (TOGAF), a data architect is expected to set data architecture principles, create models of data that enable the implementation of the intended business architecture, create diagrams showing key data entities, and create an inventory of the data needed to implement the architecture vision. [3]
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]
Data scientists often work with unstructured data such as text or images and use machine learning algorithms to build predictive models and make data-driven decisions. In addition to statistical analysis, data science often involves tasks such as data preprocessing, feature engineering, and model selection.
The KSAOs required for a job are inferred from the most frequently-occurring, important tasks. In a worker-oriented job analysis, the skills are inferred from tasks and the skills are rated directly in terms of importance of frequency. This often results in data that immediately imply the important KSAOs.
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