Cleaning data for effective data science: doing the other 80% of the work with Python, R, and command-line tools
A comprehensive guide for data scientists to master effective data cleaning tools and techniques Key Features Master data cleaning techniques in a language-agnostic manner Learn from intriguing hands-on examples from numerous domains, such as biology, weather data, demographics, physics, time series...
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
[Erscheinungsort nicht ermittelbar]
Packt Publishing Limited
2021
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| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9781801071291/?ar |
| Zusammenfassung: | A comprehensive guide for data scientists to master effective data cleaning tools and techniques Key Features Master data cleaning techniques in a language-agnostic manner Learn from intriguing hands-on examples from numerous domains, such as biology, weather data, demographics, physics, time series, and image processing Work with detailed, commented, well-tested code samples in Python and R Book Description It is something of a truism in data science, data analysis, or machine learning that most of the effort needed to achieve your actual purpose lies in cleaning your data. Written in David's signature friendly and humorous style, this book discusses in detail the essential steps performed in every production data science or data analysis pipeline and prepares you for data visualization and modeling results. The book dives into the practical application of tools and techniques needed for data ingestion, anomaly detection, value imputation, and feature engineering. It also offers long-form exercises at the end of each chapter to practice the skills acquired. You will begin by looking at data ingestion of data formats such as JSON, CSV, SQL RDBMSes, HDF5, NoSQL databases, files in image formats, and binary serialized data structures. Further, the book provides numerous example data sets and data files, which are available for download and independent exploration. Moving on from formats, you will impute missing values, detect unreliable data and statistical anomalies, and generate synthetic features that are necessary for successful data analysis and visualization goals. By the end of this book, you will have acquired a firm understanding of the data cleaning process necessary to perform real-world data science and machine learning tasks. What you will learn How to think carefully about your data and ask the right questions Identify problem data pertaining to individual data points Detect problem data in the systematic "shape" of the data Remediate data integrity and hygiene problems Prepare data for analytic and machine learning tasks Impute values into missing or unreliable data Generate synthetic features that are more amenable to data science, data analysis, or visualization goals. Who this book is for This book is designed to benefit software developers, data scientists, aspiring data scientists, and students who are interested in data analysis or scientific computing. Basic familiarity with statistics, general concepts in machine learning,... |
| Umfang: | 1 Online-Ressource |
| ISBN: | 9781801074407 1801074402 9781801071291 |
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| spelling | Mertz, David VerfasserIn aut Cleaning data for effective data science doing the other 80% of the work with Python, R, and command-line tools David Mertz [Erscheinungsort nicht ermittelbar] Packt Publishing Limited 2021 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A comprehensive guide for data scientists to master effective data cleaning tools and techniques Key Features Master data cleaning techniques in a language-agnostic manner Learn from intriguing hands-on examples from numerous domains, such as biology, weather data, demographics, physics, time series, and image processing Work with detailed, commented, well-tested code samples in Python and R Book Description It is something of a truism in data science, data analysis, or machine learning that most of the effort needed to achieve your actual purpose lies in cleaning your data. Written in David's signature friendly and humorous style, this book discusses in detail the essential steps performed in every production data science or data analysis pipeline and prepares you for data visualization and modeling results. The book dives into the practical application of tools and techniques needed for data ingestion, anomaly detection, value imputation, and feature engineering. It also offers long-form exercises at the end of each chapter to practice the skills acquired. You will begin by looking at data ingestion of data formats such as JSON, CSV, SQL RDBMSes, HDF5, NoSQL databases, files in image formats, and binary serialized data structures. Further, the book provides numerous example data sets and data files, which are available for download and independent exploration. Moving on from formats, you will impute missing values, detect unreliable data and statistical anomalies, and generate synthetic features that are necessary for successful data analysis and visualization goals. By the end of this book, you will have acquired a firm understanding of the data cleaning process necessary to perform real-world data science and machine learning tasks. What you will learn How to think carefully about your data and ask the right questions Identify problem data pertaining to individual data points Detect problem data in the systematic "shape" of the data Remediate data integrity and hygiene problems Prepare data for analytic and machine learning tasks Impute values into missing or unreliable data Generate synthetic features that are more amenable to data science, data analysis, or visualization goals. Who this book is for This book is designed to benefit software developers, data scientists, aspiring data scientists, and students who are interested in data analysis or scientific computing. Basic familiarity with statistics, general concepts in machine learning,... Computational biology Methods Database management Data integrity Python (Computer program language) R (Computer program language) Computational Biology methods Data Analysis Data Accuracy Python (Programming language) Bases de données ; Gestion Intégrité des données Python (Langage de programmation) R (Langage de programmation) Qualité des données Database design & theory Data capture & analysis Mathematical theory of computation Machine learning Information architecture Computers ; Data Processing Computers ; Machine Theory Computers ; Data Modeling & Design Computational biology Fulltext Internet Resources Methods (Music) 1801071292 Erscheint auch als Druck-Ausgabe 1801071292 |
| spellingShingle | Mertz, David Cleaning data for effective data science doing the other 80% of the work with Python, R, and command-line tools Computational biology Methods Database management Data integrity Python (Computer program language) R (Computer program language) Computational Biology methods Data Analysis Data Accuracy Python (Programming language) Bases de données ; Gestion Intégrité des données Python (Langage de programmation) R (Langage de programmation) Qualité des données Database design & theory Data capture & analysis Mathematical theory of computation Machine learning Information architecture Computers ; Data Processing Computers ; Machine Theory Computers ; Data Modeling & Design Computational biology Fulltext Internet Resources Methods (Music) |
| title | Cleaning data for effective data science doing the other 80% of the work with Python, R, and command-line tools |
| title_auth | Cleaning data for effective data science doing the other 80% of the work with Python, R, and command-line tools |
| title_exact_search | Cleaning data for effective data science doing the other 80% of the work with Python, R, and command-line tools |
| title_full | Cleaning data for effective data science doing the other 80% of the work with Python, R, and command-line tools David Mertz |
| title_fullStr | Cleaning data for effective data science doing the other 80% of the work with Python, R, and command-line tools David Mertz |
| title_full_unstemmed | Cleaning data for effective data science doing the other 80% of the work with Python, R, and command-line tools David Mertz |
| title_short | Cleaning data for effective data science |
| title_sort | cleaning data for effective data science doing the other 80 of the work with python r and command line tools |
| title_sub | doing the other 80% of the work with Python, R, and command-line tools |
| topic | Computational biology Methods Database management Data integrity Python (Computer program language) R (Computer program language) Computational Biology methods Data Analysis Data Accuracy Python (Programming language) Bases de données ; Gestion Intégrité des données Python (Langage de programmation) R (Langage de programmation) Qualité des données Database design & theory Data capture & analysis Mathematical theory of computation Machine learning Information architecture Computers ; Data Processing Computers ; Machine Theory Computers ; Data Modeling & Design Computational biology Fulltext Internet Resources Methods (Music) |
| topic_facet | Computational biology Methods Database management Data integrity Python (Computer program language) R (Computer program language) Computational Biology methods Data Analysis Data Accuracy Python (Programming language) Bases de données ; Gestion Intégrité des données Python (Langage de programmation) R (Langage de programmation) Qualité des données Database design & theory Data capture & analysis Mathematical theory of computation Machine learning Information architecture Computers ; Data Processing Computers ; Machine Theory Computers ; Data Modeling & Design Computational biology Fulltext Internet Resources Methods (Music) |
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