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Cracking the Data Science Interview

You're reading from   Cracking the Data Science Interview Unlock insider tips from industry experts to master the data science field

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Product type Paperback
Published in Feb 2024
Publisher Packt
ISBN-13 9781805120506
Length 404 pages
Edition 1st Edition
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Authors (2):
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Leondra R. Gonzalez Leondra R. Gonzalez
Author Profile Icon Leondra R. Gonzalez
Leondra R. Gonzalez
Aaren Stubberfield Aaren Stubberfield
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Aaren Stubberfield
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Table of Contents (21) Chapters Close

Preface 1. Part 1: Breaking into the Data Science Field FREE CHAPTER
2. Chapter 1: Exploring Today’s Modern Data Science Landscape 3. Chapter 2: Finding a Job in Data Science 4. Part 2: Manipulating and Managing Data
5. Chapter 3: Programming with Python 6. Chapter 4: Visualizing Data and Data Storytelling 7. Chapter 5: Querying Databases with SQL 8. Chapter 6: Scripting with Shell and Bash Commands in Linux 9. Chapter 7: Using Git for Version Control 10. Part 3: Exploring Artificial Intelligence
11. Chapter 8: Mining Data with Probability and Statistics 12. Chapter 9: Understanding Feature Engineering and Preparing Data for Modeling 13. Chapter 10: Mastering Machine Learning Concepts 14. Chapter 11: Building Networks with Deep Learning 15. Chapter 12: Implementing Machine Learning Solutions with MLOps 16. Part 4: Getting the Job
17. Chapter 13: Mastering the Interview Rounds 18. Chapter 14: Negotiating Compensation 19. Index 20. Other Books You May Enjoy

Approaching complex queries

Writing complex SQL queries can be a challenging task, especially when dealing with multiple tables, complex filtering conditions, and intricate calculations. However, by following a step-by-step approach, you can break down the problem into smaller, manageable parts and gradually build up to the final query.

Here are some systematic guidelines on how to approach complex queries:

  • Step 1 – define the objective: Begin by clearly defining the objective of your query. What specific information are you trying to retrieve or calculate? What is the desired output?
  • Step 2 – identify the tables: Determine which tables contain the necessary data for your objective and identify their respective keys. This helps us identify our starting point. If multiple tables are involved, consider the relationships between them and how they should be joined. Determine the key(s) in each table.
  • Step 3 – determine the filtering criteria: Identify...
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