eBook
A Data Scientist's Guide to Acquiring, Cleaning, and Managing Data in R
| Τίτλος: | A Data Scientist's Guide to Acquiring, Cleaning, and Managing Data in R |
|---|---|
| Περιγραφή: | The only how-to guide offering a unified, systemic approach to acquiring, cleaning, and managing data in R Every experienced practitioner knows that preparing data for modeling is a painstaking, time-consuming process. Adding to the difficulty is that most modelers learn the steps involved in cleaning and managing data piecemeal, often on the fly, or they develop their own ad hoc methods. This book helps simplify their task by providing a unified, systematic approach to acquiring, modeling, manipulating, cleaning, and maintaining data in R. Starting with the very basics, data scientists Samuel E. Buttrey and Lyn R. Whitaker walk readers through the entire process. From what data looks like and what it should look like, they progress through all the steps involved in getting data ready for modeling. They describe best practices for acquiring data from numerous sources; explore key issues in data handling, including text/regular expressions, big data, parallel processing, merging, matching, and checking for duplicates; and outline highly efficient and reliable techniques for documenting data and recordkeeping, including audit trails, getting data back out of R, and more. The only single-source guide to R data and its preparation, it describes best practices for acquiring, manipulating, cleaning, and maintaining data Begins with the basics and walks readers through all the steps necessary to get data ready for the modeling process Provides expert guidance on how to document the processes described so that they are reproducible Written by seasoned professionals, it provides both introductory and advanced techniques Features case studies with supporting data and R code, hosted on a companion website A Data Scientist's Guide to Acquiring, Cleaning and Managing Data in R is a valuable working resource/bench manual for practitioners who collect and analyze data, lab scientists and research associates of all levels of experience, and graduate-level data mining students. |
| Συγγραφείς: | Samuel E. Buttrey, Lyn R. Whitaker |
| Resource Type: | eBook. |
| Θέματα: | Electronic data processing--Data preparation, Database management, R (Computer program language), Database design--Computer programs, Data editing--Computer programs, Input design, Computer, Programming languages (Electronic computers) |
| Categories: | COMPUTERS / Mathematical & Statistical Software |
| Βάση Δεδομένων: | eBook Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edsebk DbLabel: eBook Index An: 1621867 RelevancyScore: 931 AccessLevel: 6 PubType: eBook PubTypeId: ebook PreciseRelevancyScore: 930.90771484375 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Data Scientist's Guide to Acquiring, Cleaning, and Managing Data in R – Name: Abstract Label: Description Group: Ab Data: The only how-to guide offering a unified, systemic approach to acquiring, cleaning, and managing data in R Every experienced practitioner knows that preparing data for modeling is a painstaking, time-consuming process. Adding to the difficulty is that most modelers learn the steps involved in cleaning and managing data piecemeal, often on the fly, or they develop their own ad hoc methods. This book helps simplify their task by providing a unified, systematic approach to acquiring, modeling, manipulating, cleaning, and maintaining data in R. Starting with the very basics, data scientists Samuel E. Buttrey and Lyn R. Whitaker walk readers through the entire process. From what data looks like and what it should look like, they progress through all the steps involved in getting data ready for modeling. They describe best practices for acquiring data from numerous sources; explore key issues in data handling, including text/regular expressions, big data, parallel processing, merging, matching, and checking for duplicates; and outline highly efficient and reliable techniques for documenting data and recordkeeping, including audit trails, getting data back out of R, and more. The only single-source guide to R data and its preparation, it describes best practices for acquiring, manipulating, cleaning, and maintaining data Begins with the basics and walks readers through all the steps necessary to get data ready for the modeling process Provides expert guidance on how to document the processes described so that they are reproducible Written by seasoned professionals, it provides both introductory and advanced techniques Features case studies with supporting data and R code, hosted on a companion website A Data Scientist's Guide to Acquiring, Cleaning and Managing Data in R is a valuable working resource/bench manual for practitioners who collect and analyze data, lab scientists and research associates of all levels of experience, and graduate-level data mining students. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Samuel+E%2E+Buttrey%22">Samuel E. Buttrey</searchLink><br /><searchLink fieldCode="AR" term="%22Lyn+R%2E+Whitaker%22">Lyn R. Whitaker</searchLink> – Name: TypePub Label: Resource Type Group: TypPub Data: eBook. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Electronic+data+processing--Data+preparation%22">Electronic data processing--Data preparation</searchLink><br /><searchLink fieldCode="DE" term="%22Database+management%22">Database management</searchLink><br /><searchLink fieldCode="DE" term="%22R+%28Computer+program+language%29%22">R (Computer program language)</searchLink><br /><searchLink fieldCode="DE" term="%22Database+design--Computer+programs%22">Database design--Computer programs</searchLink><br /><searchLink fieldCode="DE" term="%22Data+editing--Computer+programs%22">Data editing--Computer programs</searchLink><br /><searchLink fieldCode="DE" term="%22Input+design%2C+Computer%22">Input design, Computer</searchLink><br /><searchLink fieldCode="DE" term="%22Programming+languages+%28Electronic+computers%29%22">Programming languages (Electronic computers)</searchLink> – Name: SubjectBISAC Label: Categories Group: Su Data: <searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Mathematical+%26+Statistical+Software%22">COMPUTERS / Mathematical & Statistical Software</searchLink> |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1621867 |
| RecordInfo | BibRecord: BibEntity: Classifications: – Code: 005.743 Scheme: ddc Type: prePub Languages: – Code: eng Text: English Subjects: – SubjectFull: Electronic data processing--Data preparation Type: general – SubjectFull: Database management Type: general – SubjectFull: R (Computer program language) Type: general – SubjectFull: Database design--Computer programs Type: general – SubjectFull: Data editing--Computer programs Type: general – SubjectFull: Input design, Computer Type: general – SubjectFull: Programming languages (Electronic computers) Type: general Titles: – TitleFull: A Data Scientist's Guide to Acquiring, Cleaning, and Managing Data in R Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Samuel E. Buttrey – PersonEntity: Name: NameFull: Lyn R. Whitaker – PersonEntity: Name: NameFull: Samuel E. Buttrey – PersonEntity: Name: NameFull: Lyn R. Whitaker IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2017 – D: 01 M: 11 Type: profile Y: 2017 Identifiers: – Type: isbn-print Value: 9781119080022 – Type: isbn-electronic Value: 9781119080060 – Type: isbn-electronic Value: 9781119080077 Titles: – TitleFull: A Data Scientist's Guide to Acquiring, Cleaning, and Managing Data in R Type: main |
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