Principles data wrangling practical von rattenbury tye (3 Ergebnisse)

Principles of Data Wrangling: Practical Techniques for Data Preparation
Rattenbury, Tye/ Hellerstein, Joseph M./ Heer, Jeffrey/ Kandel, Sean/ Carreras, Connor
- Softcover
Anbieter: Revaluation Books, Exeter, Vereinigtes KönigreichRevaluation Books
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 44,73
EUR 11,69 VersandVersand von Vereinigtes Königreich nach USAAnzahl: 2 verfügbar
Paperback. Zustand: Brand New. 82 pages. 8.75x6.75x0.25 inches. In Stock.

- Softcover
Anbieter: Kennys Bookstore, Olney, MD, USAKennys Bookstore
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 48,18
EUR 9,00 VersandVersand innerhalb von USAAnzahl: 1 verfügbar
Zustand: New. 2017. 1st Edition. Paperback. . . . . . Books ship from the US and Ireland.

- Softcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 53,00
EUR 60,84 VersandVersand von Deutschland nach USAAnzahl: 2 verfügbar
Taschenbuch. Zustand: Neu. Neuware - 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 dat…a 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 factorstime, granularity, scope, and structurethat 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 importanceand the satisfactionof 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.