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

Paperback by Raviv, Gil

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

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£24.89

ISBN:
9781509307951
Publication Date:
19 Dec 2018
Language:
English
Publisher:
Microsoft Press,U.S.
Imprint:
Microsoft Press
Pages:
432 pages
Format:
Paperback
For delivery:
Estimated despatch 22 - 23 Sep 2025
Collect, Combine, and Transform Data Using Power Query in Excel and Power BI

Description

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.

Contents

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

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