November 2017
Beginner
286 pages
8h 13m
English
book
Statistics for Data Science
by James C. Mott, Rajprasath Subramanian, Shaikh Salamatullah, James D. Miller, Vijayakumar Ramdoss
Content preview from Statistics for Data Science
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Common data issues
We can categorize data difficulties into several groups. The most generally accepted groupings (of data issues) include:
- Accuracy: There are many varieties of data inaccuracies and the most common examples include poor math, out-of-range values, invalid values, duplication, and more.
- Completeness: Data sources may be missing values from particular columns, missing entire columns, or even missing complete transactions.
- Update status: As part of your quality assurance, you need to establish the cadence of data refresh or update, as well as have the ability to determine when the data was last saved or updated. This is also referred to as latency.
- Relevance: It is identification and elimination of information that you don't ...
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