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Cracking the Data Science Interview

You're reading from  Cracking the Data Science Interview

Product type Book
Published in Feb 2024
Publisher Packt
ISBN-13 9781805120506
Pages 404 pages
Edition 1st Edition
Languages
Authors (2):
Leondra R. Gonzalez Leondra R. Gonzalez
Profile icon Leondra R. Gonzalez
Aaren Stubberfield Aaren Stubberfield
Profile icon Aaren Stubberfield
View More author details

Table of Contents (21) Chapters

Preface 1. Part 1: Breaking into the Data Science Field
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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