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Welcome to data science's dirty secret: real-world data is messy. Data scientists must spend a good deal of time playing software developer, writing code to clean up data before they can actually do anything constructive with it. It's a necessary evil, but you can still make the most of it. This practical book walks you through several real-world examples to demonstrate the theory and practice behind working with and cleaning up dirty data. No one tool solves all of the problems well. Wise data scientists learn many tools and learn where each one shines. To that end, this book takes a polyglot approach: most examples will involve R and Python, but expect the occasional smattering of Groovy and sed/awk fun.

Information systems --- Documentation and information --- Data editing --- Data protection --- Edition (Informatique) --- Protection de l'information (Informatique) --- Data editing.

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Data use in the library has specific characteristics and common problems. Data Clean-up and Management addresses these, and provides methods to clean up frequently-occurring data problems using readily-available applications. The authors highlight the importance and methods of data analysis and presentation, and offer guidelines and recommendations for a data quality policy. The book gives step-by-step how-to directions for common dirty data issues.Focused towards libraries and practicing librariansDeals with practical, real-life issues and addresses common problems th

Collection management (Libraries) -- Data processing. --- Libraries -- Circulation analysis -- Data processing. --- Library use studies. --- Social Sciences --- Library & Information Science --- Libraries --- Data editing. --- Data processing. --- Data editing --- Bibliothèques --- Edition (Informatique) --- Informatique --- Editing, Data --- Electronic data processing --- Quality control --- Statistics --- Data processing --- E-books