This practical handbook takes the reader through several real-world examples to demonstrate the theory and practice behind working with and cleaning up dirty data. As no single tool solves all of the problems well a polyglot approach is taken, with most examples involving R and Python, but sed/awk utilities also appearing.

"Mapping the world of data problems"--Cover.

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.