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Principles of data wrangling : practical techniques for data preparation

Author: Tye Rattenbury
Publisher: SEBASTOPOL : O'REILLY MEDIA.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Database:WorldCat
Summary:

Written by key executives at Trifacta, this book teaches you a shared language and a comprehensive understanding of data wrangling, with an emphasis on recent agile analytic processes used by many of  Read more...

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Genre/Form: Electronic books
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Tye Rattenbury
ISBN: 1491938897 9781491938898 9781491938874 1491938870
OCLC Number: 992787355
Description: 1 online resource.
Contents: Cover; Copyright; Table of Contents; Foreword; Chapter 1. Introduction; Magic Thresholds, PYMK, and User Growth at Facebook; Chapter 2. A Data Workflow Framework; How Data Flows During and Across Projects; Connecting Analytic Actions to Data Movement: A Holistic Workflow Framework for Data Projects; Raw Data Stage Actions: Ingest Data and Create Metadata; Ingesting Known and Unknown Data; Creating Metadata; Refined Data Stage Actions: Create Canonical Data and Conduct Ad Hoc Analyses; Designing Refined Data; Refined Stage Analytical Actions Production Data Stage Actions: Create Production Data and Build Automated SystemsCreating Optimized Data; Designing Regular Reports and Automated Products/Services; Data Wrangling within the Workflow Framework; Chapter 3. The Dynamics of Data Wrangling; Data Wrangling Dynamics; Additional Aspects: Subsetting and Sampling; Core Transformation and Profiling Actions; Data Wrangling in the Workflow Framework; Ingesting Data; Describing Data; Assessing Data Utility; Designing and Building Refined Data; Ad Hoc Reporting; Exploratory Modeling and Forecasting; Building an Optimized Dataset Regular Reporting and Building Data-Driven Products and ServicesChapter 4. Profiling; Overview of Profiling; Individual Value Profiling: Syntactic Profiling; Individual Value Profiling: Semantic Profiling; Set-Based Profiling; Profiling Individual Values in the Candidate Master File; Syntactic Profiling in the Candidate Master File; Set-Based Profiling in the Candidate Master File; Chapter 5. Transformation: Structuring; Overview of Structuring; Intrarecord Structuring: Extracting Values; Positional Extraction; Pattern Extraction; Complex Structure Extraction Intrarecord Structuring: Combining Multiple Record FieldsInterrecord Structuring: Filtering Records and Fields; Interrecord Structuring: Aggregations and Pivots; Simple Aggregations; Column-to-Row Pivots; Row-to-Column Pivots; Chapter 6. Transformation: Enriching; Unions; Joins; Inserting Metadata; Derivation of Values; Generic; Proprietary; Chapter 7. Using Transformation to Clean Data; Addressing Missing/NULL Values; Addressing Invalid Values; Chapter 8. Roles and Responsibilities; Skills and Responsibilities; Data Engineer; Data Architect; Data Scientist; Analyst Roles Across the Data Workflow FrameworkOrganizational Best Practices; Chapter 9. Data Wrangling Tools; Data Size and Infrastructure; Data Structures; Excel; SQL; Trifacta Wrangler; Transformation Paradigms; Excel; SQL; Trifacta Wrangler; Choosing a Data Wrangling Tool; About the Authors; Colophon
Responsibility: Tye Rattenbury, Joseph M. Hellerstein, Jeffrey Heer, Sean Kandel and Connor Carreras.

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