By Megan Squire
- Grow your facts technological know-how services through filling your toolbox with confirmed thoughts for a wide selection of cleansing challenges
- Familiarize your self with the an important information cleansing procedures, and percentage your personal fresh information units with others
- Complete real-world initiatives utilizing info from Twitter and Stack Overflow
Is a lot of it slow spent doing tedious projects resembling cleansing soiled facts, accounting for misplaced information, and getting ready info for use via others? if that is so, then having the suitable instruments makes a serious distinction, and may be a very good funding as you develop your info technology expertise.
The e-book begins via highlighting the significance of information cleansing in facts technological know-how, and should make it easier to obtain rewards from reforming your cleansing approach. subsequent, you are going to cement your wisdom of the elemental thoughts that the remainder of the booklet depends upon: dossier codecs, information forms, and personality encodings. additionally, you will the way to extract and fresh facts saved in RDBMS, net documents, and PDF files, via functional examples.
At the tip of the booklet, you may be given an opportunity to take on a few real-world projects.
What you'll learn
- Understand the position of knowledge cleansing within the total info technological know-how process
- Learn the fundamentals of dossier codecs, information varieties, and personality encodings to wash info properly
- Master severe gains of the spreadsheet and textual content editor for organizing and manipulating data
- Convert facts from one universal structure to a different, together with JSON, CSV, and a few special-purpose formats
- Implement 3 varied innovations for parsing and cleansing information present in HTML records at the Web
- Reveal the mysteries of PDF files and the right way to pull out simply the knowledge you want
- Develop more than a few recommendations for detecting and cleansing undesirable info kept in an RDBMS
- Create your personal fresh information units that may be packaged, authorized, and shared with others
- Use the instruments from this booklet to accomplish real-world tasks utilizing info from Twitter and Stack Overflow
About the Author
Megan Squire is a professor of computing sciences at Elon collage. She has been gathering and cleansing soiled facts for 2 many years. She is additionally the chief of FLOSSmole.org, a examine undertaking to assemble info and learn it for you to learn the way unfastened, libre, and open resource software program is made.
Table of Contents
- Why do you want fresh Data?
- Fundamentals codecs, forms, and Encodings
- Workhorses of fresh info Spreadsheets and textual content Editors
- Speaking the Lingua Franca info Conversions
- Collecting and cleansing information from the Web
- Cleaning information in Pdf Files
- RDBMS cleansing Techniques
- Best Practices for Sharing Your fresh Data
- Stack Overflow Project
- Twitter Project
Read or Download Clean Data - Data Science Strategies for Tackling Dirty Data PDF
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Additional resources for Clean Data - Data Science Strategies for Tackling Dirty Data
Throw out the affected e-mails: We can just make an informed decision to discard any e-mail that has a date that falls outside a predetermined window.  Why Do You Need Clean Data? In order to decide between options 2 and 3, we will need to count how many messages will be affected using only a 1999-2002 window. We can use the following SQL: SELECT count(*) FROM message WHERE year(date) < 1998 or year(date) > 2002; Result: 325 325 messages with bad dates may initially seem like a lot, but then again, they are only about 1 percent of the entire dataset.
At this point, we have three choices for what to do: • Do nothing: Maybe we can just ignore the bad data and get away with building the line graph anyway. But, as the lowest bad date was from the year 0001 and the highest was from the year 2044, we can imagine our line graph with the 1300 tick marks on the time axis, each showing a count of 1 or 2. This graph does not sound very appealing or informative, so doing nothing will not work. • Fix the data: We could try to figure out what the correct date for each bad message was and produce a corrected dataset that we can then use to build our graph.
How many times have you seen an interesting table, or list of information on a website, and wanted to save the data? Sometimes, copying and pasting works to try to create a delimited file from the web page, but most of the time, copying and pasting does not work effectively. HTML files can be terribly messy and thus, are a potentially painful way to extract data. For this reason, sometimes, we refer to web files as unstructured data. Even though web pages may have some HTML tags that could be used to attempt a delimiting type of patternbased organization of data, they don't always.