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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

Calculating window functions

SQL window functions are an additional tool in your toolkit. Unlike aggregate functions, which return a single result per group of rows, window functions return a single result for each row, based on the context of that row within a window of related rows.

OVER, ORDER BY, PARTITION, and SET

Window functions have the following basic syntax:

<function> (<expression>)
OVER (
[PARTITION BY <expression_list>]
[ORDER BY <expression_list>] [ROWS|RANGE <frame specification>])

There are a few key concepts to understand here, so let’s break them down:

  • The OVER keyword is what differentiates a window function from a regular function; once you see it, you know you’re in window function land. The OVER clause defines the window or subset of rows within a query result set that the window function operates on. In short, it provides a way to partition the result set into logical groups and allows the window...
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