Book Image

Data Cleaning with Power BI

By : Gus Frazer
Book Image

Data Cleaning with Power BI

By: Gus Frazer

Overview of this book

Microsoft Power BI offers a range of powerful data cleaning and preparation options through tools such as DAX, Power Query, and the M language. However, despite its user-friendly interface, mastering it can be challenging. Whether you're a seasoned analyst or a novice exploring the potential of Power BI, this comprehensive guide equips you with techniques to transform raw data into a reliable foundation for insightful analysis and visualization. This book serves as a comprehensive guide to data cleaning, starting with data quality, common data challenges, and best practices for handling data. You’ll learn how to import and clean data with Query Editor and transform data using the M query language. As you advance, you’ll explore Power BI’s data modeling capabilities for efficient cleaning and establishing relationships. Later chapters cover best practices for using Power Automate for data cleaning and task automation. Finally, you’ll discover how OpenAI and ChatGPT can make data cleaning in Power BI easier. By the end of the book, you will have a comprehensive understanding of data cleaning concepts, techniques, and how to use Power BI and its tools for effective data preparation.
Table of Contents (23 chapters)
Free Chapter
1
Part 1 – Introduction and Fundamentals
6
Part 2 – Data Import and Query Editor
11
Part 3 – Advanced Data Cleaning and Optimizations
16
Part 4 – Paginated Reports, Automations, and OpenAI

Questions

  1. What are the two essential techniques discussed in the chapter for cleaning and preparing data using the Query Editor in Power BI?
    1. Fuzzy matching and fill up
    2. Data profiling and sorting
    3. Fuzzy matching and fill down
    4. Data imputation and statistical analysis
  2. In the context of fuzzy matching, what is the similarity score range, and what does it indicate?
    1. Range from 1 to 10, indicating similarity strength
    2. Range from 0 to 100, indicating confidence level
    3. Range from 0 to 1, indicating no to perfect similarity
    4. Range from -1 to 1, indicating negative to positive correlation
  3. When is the fill down technique in Power BI’s Query Editor particularly useful?
    1. When you want to skip data gaps
    2. When dealing with categorical data
    3. When you need to perform calculations on filled values
    4. When working with time series data and maintaining data continuity
  4. What is a crucial best practice emphasized when working with fuzzy matching and fill down in Power BI?
    1. Occasionally document the steps...