Principles of data wrangling : practical techniques for data preparation /

A key task that any aspiring data-driven organization needs to learn is data wrangling, the process of converting raw data into something truly useful. This practical guide provides business analysts with an overview of various data wrangling techniques and tools, and puts the practice of data wrang...

Ausführliche Beschreibung

Gespeichert in:
Weitere Titel:
Practical techniques for data preparation
Hauptverfasser:
Rattenbury, Ty, Rattenbury, Tye
Format:
Elektronisch E-Book
Sprache:
Englisch
Veröffentlicht:
Beijing, [China] : O'Reilly, 2017.
Ausgabe:
First edtion.
Zusammenfassung:
A key task that any aspiring data-driven organization needs to learn is data wrangling, the process of converting raw data into something truly useful. This practical guide provides business analysts with an overview of various data wrangling techniques and tools, and puts the practice of data wrangling into context by asking, "What are you trying to do and why?" Wrangling data consumes roughly 50-80% of an analyst’s time before any kind of analysis is possible. Written by key executives at Trifacta, this book walks you through the wrangling process by exploring several factors—time, granularity, scope, and structure—that you need to consider as you begin to work with data. You’ll learn a shared language and a comprehensive understanding of data wrangling, with an emphasis on recent agile analytic processes used by many of today’s data-driven organizations. Appreciate the importance—and the satisfaction—of wrangling data the right way. Understand what kind of data is available Choose which data to use and at what level of detail Meaningfully combine multiple sources of data Decide how to distill the results to a size and shape that can drive downstream analysis
Umfang:
1 online resource (84 pages) : illustrations, tables
text file
ISBN:
9781491938874
1491938870
9781491938911
1491938919
9781491938898
1491938897
Schlagworte:
Data mining.
Electronic data processing > Data preparation.
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