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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.
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Holdings
Item type Home library Shelving location Call number Status Date due Barcode Item holds Course reserves
Reserve Books Reserve Books Punsarn Library Circulation Counter QA76.9.D3 M337 2013 (Browse shelf(Opens below)) Available PNLIB21060075

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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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