Published by Microsoft Press (October 8, 2018) © 2019
Gil RavivUsing Power Query, you can import, reshape, and cleanse any data from a simple interface, so you can mine that data for all of its hidden insights. Power Query is embedded in Excel, Power BI, and other Microsoft products, and leading Power Query expert Gil Raviv will help you make the most of it. Discover how to eliminate time-consuming manual data preparation, solve common problems, avoid pitfalls, and more. Then, walk through several complete analytics challenges, and integrate all your skills in a realistic chapter-length final project. By the time you’re finished, you’ll be ready to wrangle any data–and transform it into actionable knowledge.
Prepare and analyze your data the easy way, with Power Query
· Quickly prepare data for analysis with Power Query in Excel (also known as Get & Transform) and in Power BI
· Solve common data preparation problems with a few mouse clicks and simple formula edits
· Combine data from multiple sources, multiple queries, and mismatched tables
· Master basic and advanced techniques for unpivoting tables
· Customize transformations and build flexible data mashups with the M formula language
· Address collaboration challenges with Power Query
· Gain crucial insights into text feeds
· Streamline complex social network analytics so you can do it yourself
For all information workers, analysts, and any Excel user who wants to solve their own business intelligence problems.
- 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