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SQL for Data Analytics

You're reading from   SQL for Data Analytics Harness the power of SQL to extract insights from data

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Product type Paperback
Published in Aug 2022
Publisher Packt
ISBN-13 9781801812870
Length 540 pages
Edition 3rd Edition
Languages
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Authors (4):
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Benjamin Johnston Benjamin Johnston
Author Profile Icon Benjamin Johnston
Benjamin Johnston
Matt Goldwasser Matt Goldwasser
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Matt Goldwasser
Jun Shan Jun Shan
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Jun Shan
Upom Malik Upom Malik
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Upom Malik
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Toc

Table of Contents (11) Chapters Close

Preface 1. Understanding and Describing Data 2. The Basics of SQL for Analytics FREE CHAPTER 3. SQL for Data Preparation 4. Aggregate Functions for Data Analysis 5. Window Functions for Data Analysis 6. Importing and Exporting Data 7. Analytics Using Complex Data Types 8. Performant SQL 9. Using SQL to Uncover the Truth: A Case Study Appendix

Using Python with your Database

While SQL has a breadth of functionality, many data scientists and data analysts are starting to use Python too. This is because Python is a high-level language that can be easily used to process data. While the functionality of SQL covers most of the daily needs of data scientists, Python is growing fast and has generally become one of the most important data analytics tools in recent polls. A lot of Python's functionality is also fast, in part because so much of it is written in C, a low-level programming language.

The other large advantage that Python has is that it is versatile. While SQL is generally only used in the data science and statistical analysis communities, Python can be used to do anything from statistical analysis to building a web application. As a result, the developer community is much larger for Python. A larger developer community is a big advantage because there is better community support (for example, on Stack Overflow...

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