Book Image

SQL for Data Analytics - Third Edition

By : Jun Shan, Matt Goldwasser, Upom Malik, Benjamin Johnston
Book Image

SQL for Data Analytics - Third Edition

By: Jun Shan, Matt Goldwasser, Upom Malik, Benjamin Johnston

Overview of this book

Every day, businesses operate around the clock, and a huge amount of data is generated at a rapid pace. This book helps you analyze this data and identify key patterns and behaviors that can help you and your business understand your customers at a deep, fundamental level. SQL for Data Analytics, Third Edition is a great way to get started with data analysis, showing how to effectively sort and process information from raw data, even without any prior experience. You will begin by learning how to form hypotheses and generate descriptive statistics that can provide key insights into your existing data. As you progress, you will learn how to write SQL queries to aggregate, calculate, and combine SQL data from sources outside of your current dataset. You will also discover how to work with advanced data types, like JSON. By exploring advanced techniques, such as geospatial analysis and text analysis, you will be able to understand your business at a deeper level. Finally, the book lets you in on the secret to getting information faster and more effectively by using advanced techniques like profiling and automation. By the end of this book, you will be proficient in the efficient application of SQL techniques in everyday business scenarios and looking at data with the critical eye of analytics professional.
Table of Contents (11 chapters)
9. Using SQL to Uncover the Truth: A Case Study

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...