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Data presentation architecture weds the science of numbers, data and statistics in discovering valuable information from data and making it usable, relevant and actionable with the arts of data visualization, communications, organizational psychology and change management in order to provide business intelligence solutions with the data scope ...
Tukey defined data analysis in 1961 as: "Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data." [3]
The effective management of any organization relies on accurate data. Inaccurate data reporting can lead to poor decision-making based on erroneous evidence. Data reporting is different from data analysis which transforms data and information into insights. Data reporting is the previous step that translates raw data into information. [1]
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]
The term "schema" refers to the organization of data as a blueprint of how the database is constructed (divided into database tables in the case of relational databases). The formal definition of a database schema is a set of formulas (sentences) called integrity constraints imposed on a database.
According to the article "Prepare and Deliver an Effective Presentation", [10] effective presentations typically use serif fonts (e.g. Times New Roman, Garamond, Baskerville, etc.) for the smaller text and sans serif fonts (e.g. Helvetica, Futura, Arial, etc.) for headings and larger text. The typefaces are used along with type size to improve ...
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Data-informed decision-making (DIDM) gives reference to the collection and analysis of data to guide decisions that improve success. [1] Another form of this process is referred to as data-driven decision-making, "which is defined similarly as making decisions based on hard data as opposed to intuition, observation, or guesswork."