Bad Data Handbook: Cleaning Up the Data So You Can Get Back by Q. Ethan McCallum

By Q. Ethan McCallum

What's undesirable facts? a few humans think about it a technical phenomenon, like lacking values or malformed documents, yet undesirable facts encompasses a lot extra. during this guide, information specialist Q. Ethan McCallum has accrued 19 colleagues from each nook of the knowledge enviornment to bare how they've recovered from nasty facts problems.

From cranky garage to negative illustration to erroneous coverage, there are numerous paths to undesirable info. base line? undesirable facts is info that will get within the method. This e-book explains potent how you can get round it.

one of many issues coated, you'll realize how to:
attempt force your facts to determine if it's prepared for analysis
paintings spreadsheet information right into a usable form
deal with encoding difficulties that lurk in textual content data
increase a profitable web-scraping effort
Use NLP instruments to bare the genuine sentiment of on-line reviews
handle cloud computing matters that could effect your research effort
steer clear of regulations that create info research roadblocks
Take a scientific method of information caliber analysis

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Extra info for Bad Data Handbook: Cleaning Up the Data So You Can Get Back to Work

Sample text

When schools start to let out in the spring, how much will traffic drop? Sites with large amounts of historical traffic data can build models (explicitly or implicitly via “tribal knowledge”), but what about a new site or a translation of an existing site? In order to address this, I tried using the publicly available Wikipedia logs to build a long-term, large-scale seasonality model for traffic in various languages. Wikipedia serves close to 500M pages per day, a bit more than half of them in English.

Figure 3-2. A plot of the data from Figure 3-1 The Problem: Data Formatted for Human Consumption | 33 Example 3-2. The data from Figure 3-1 rearranged so that all values for the same gender are next to each other Gender Female Female Female Male Male Male NCEA NCEA NCEA NCEA NCEA NCEA Level Level 1 Level 2 Level 3 Level 1 Level 2 Level 3 Year 11 0 0 0 5929 194 2 Year 12 60 58 0 6427 5395 128 Year 13 38 38 36 5170 5027 3276 Unfortunately, this sort of reuse of the data is severely hampered by the layout of the data within the spreadsheet.

Some of the labels that correspond to this number, “Male” and “Year 11”, are on the same row and column as the number, but even those are neither immediately adjacent to the number nor at some obvious limit location as the first row or first column in the spreadsheet. Figure 3-4. The data that corresponds to the top-left count in Figure 3-1 Given a data value, it is difficult to describe to software the location of the labels that are associated with that data value. To drive the point home, Example 3-3 shows how the data could be arranged so that the association between labels and data values is very straightforward.

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