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

About

Features

Students will learn to:

• 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
• Gain effiiciency by reducing the time it takes to prepare data for analysis, and make informed decisions more quickly. 

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.

Downloads

Downloads

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1. Click the Download button below to start the download.
2. If prompted, click Save.
3. Locate the .zip file on your computer. Right-click the file, click Extract All, and then follow the instructions.

Sample Content

Sample Pages

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Table of 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

Updates

Errata

We've made every effort to ensure the accuracy of this book and its companion content. Any errors that have been confirmed since this book was published can be downloaded below.

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