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Bad data handbook / Q. Ethan McCallum.

By: Material type: TextTextPublication details: Sebastopol, CA : O'Reilly, 2013.Description: xvi, 245 p. : illISBN:
  • 9781449321888
  • 1449321887
Subject(s): LOC classification:
  • QA76.9.D3 M337 2013
Contents:
Setting the pace : what is bad data? -- Is it just me, or does this data smell funny? -- Data intended for human consumption, not machine consumption -- Bad data lurking in plain text -- (Re)organizing the web's data -- Detecting liars and the confused in contradictory online reviews -- Will the bad data please stand up? -- Blood, sweat, and urine -- When data and reality don't match -- Subtle sources of bias and error -- Don't let the perfect be the enemy of the good : is bad data really bad? -- When databases attack : a guide for when to stick to files -- Crouching table, hidden network -- Myths of cloud computing -- The dark side of data science -- How to feed and care for your machine-learning experts -- Data traceability -- Social media : erasable ink? -- Data quality analysis demystified : knowing when your data is good enough.
Item type: Books
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Item type Home library Shelving location Call number Status Barcode
Reserve Books Reserve Books Punsarn Library Circulation Counter QA76.9.D3 M337 2013 (Browse shelf(Opens below)) Available PNLIB21060075
Total holds: 0

Reprint. Originally published: 2012.

Includes bibliographical references and index.

Setting the pace : what is bad data? -- Is it just me, or does this data smell funny? -- Data intended for human consumption, not machine consumption -- Bad data lurking in plain text -- (Re)organizing the web's data -- Detecting liars and the confused in contradictory online reviews -- Will the bad data please stand up? -- Blood, sweat, and urine -- When data and reality don't match -- Subtle sources of bias and error -- Don't let the perfect be the enemy of the good : is bad data really bad? -- When databases attack : a guide for when to stick to files -- Crouching table, hidden network -- Myths of cloud computing -- The dark side of data science -- How to feed and care for your machine-learning experts -- Data traceability -- Social media : erasable ink? -- Data quality analysis demystified : knowing when your data is good enough.

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