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Data Exploration and Preparation with BigQuery

You're reading from   Data Exploration and Preparation with BigQuery A practical guide to cleaning, transforming, and analyzing data for business insights

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Product type Paperback
Published in Nov 2023
Publisher Packt
ISBN-13 9781805125266
Length 264 pages
Edition 1st Edition
Languages
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Author (1):
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Mike Kahn Mike Kahn
Author Profile Icon Mike Kahn
Mike Kahn
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Table of Contents (21) Chapters Close

Preface 1. Part 1: Introduction to BigQuery FREE CHAPTER
2. Chapter 1: Introducing BigQuery and Its Components 3. Chapter 2: BigQuery Organization and Design 4. Part 2: Data Exploration with BigQuery
5. Chapter 3: Exploring Data in BigQuery 6. Chapter 4: Loading and Transforming Data 7. Chapter 5: Querying BigQuery Data 8. Chapter 6: Exploring Data with Notebooks 9. Chapter 7: Further Exploring and Visualizing Data 10. Part 3: Data Preparation with BigQuery
11. Chapter 8: An Overview of Data Preparation Tools 12. Chapter 9: Cleansing and Transforming Data 13. Chapter 10: Best Practices for Data Preparation, Optimization, and Cost Control 14. Part 4: Hands-On and Conclusion
15. Chapter 11: Hands-On Exercise – Analyzing Advertising Data 16. Chapter 12: Hands-On Exercise – Analyzing Transportation Data 17. Chapter 13: Hands-On Exercise – Analyzing Customer Support Data 18. Chapter 14: Summary and Future Directions 19. Index 20. Other Books You May Enjoy

Hands-on exercise – creating visualizations with Looker Studio

Visualizations enable you to uncover patterns, identify trends, and make informed decisions. For the visualizations in this section, we will be using the Los Angeles Traffic Collision Data mentioned in the Technical requirements section. You will want to select the table you used to load the collision data into BigQuery. Select your table in the BigQuery Explorer pane and, in the table query pane, click the EXPORT button, then Explore with Looker Studio:

Figure 7.5 – Explore with Looker Studio

Let’s explore some of the most common visualization techniques.

Commonly created charts

Bar charts are effective for visualizing categorical data and comparing different categories in groups. For example, with our collision dataset, we can create a bar chart that visualizes the ages of individuals who were in car collisions, with victim_age displayed on the X axis and DR_number...

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