Collect, Combine, and Transform Data Using Power Query in Excel and Power BI

Did you know that there is a technology inside Excel, and Power BI, that allows you to create magic in your data, avoid repetitive manual work, and save you time and money?


Using Excel and Power BI, you can:



Save time by eliminating the pain of copying and pasting data into workbooks and then manually cleaning that data. Les mer
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Vår pris: 385,-

(Paperback) Fri frakt!
Leveringstid: Sendes innen 7 virkedager
På grunn av Brexit-tilpasninger og tiltak for å begrense covid-19 kan det dessverre oppstå forsinket levering.

Om boka

Did you know that there is a technology inside Excel, and Power BI, that allows you to create magic in your data, avoid repetitive manual work, and save you time and money?


Using Excel and Power BI, you can:



Save time by eliminating the pain of copying and pasting data into workbooks and then manually cleaning that data.
Gain productivity by properly preparing data yourself, rather than relying on others to do it.
Gain effiiciency by reducing the time it takes to prepare data for analysis, and make informed decisions more quickly.

With the data connectivity and transformative technology found in Excel and Power BI, users with basic Excel skills import data and then easily reshape and cleanse that data, using simple intuitive user interfaces. Known as "Get & Transform" in Excel 2016, as the "Power Query" separate add-in in Excel 2013 and 2010, and included in Power BI, you'll use this technology to tackle common data challenges, resolving them with simple mouse clicks and lightweight formula editing. With your new data transformation skills acquired through this book, you will be able to create an automated transformation of virtually any type of data set to mine its hidden insights.

Fakta

Innholdsfortegnelse

Section 1: Transforming Data
Chapter 1: Introduction to Power Query
Chapter 2: Basic Data Challenges
Chapter 3: Combining Data from Multiple Sources
Chapter 4: Unpivoting and Transforming Data
Chapter 5: Pivoting & Handling Multiline Records
Section 2: Exploring Data
Chapter 6: Ad-Hoc Analysis
Chapter 7: Using Query Editor to Further Explore Data
Section 3: Scaling Up Queries for Production or Larger Data Sets
Chapter 8: Introduction to the M Query Language
Chapter 9: Lightweight modification of M formulas to improve query robustness
Section 4: Real Life Challenges
Chapter 10: Solving Real-Life Data Challenges
Chapter 11: Social Listening
Chapter 12: Text Analytics
Chapter 13: Concluding Exercise - Hawaii Tourism Data