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  • Book Overview & Buying SQL for Data Analytics
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SQL for Data Analytics

SQL for Data Analytics

By : Upom Malik, Matt Goldwasser, Benjamin Johnston
3.5 (35)
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SQL for Data Analytics

SQL for Data Analytics

3.5 (35)
By: Upom Malik, Matt Goldwasser, Benjamin Johnston

Overview of this book

Understanding and finding patterns in data has become one of the most important ways to improve business decisions. If you know the basics of SQL, but don't know how to use it to gain the most effective business insights from data, this book is for you. SQL for Data Analytics helps you build the skills to move beyond basic SQL and instead learn to spot patterns and explain the logic hidden in data. You'll discover how to explore and understand data by identifying trends and unlocking deeper insights. You'll also gain experience working with different types of data in SQL, including time-series, geospatial, and text data. Finally, you'll learn how to increase your productivity with the help of profiling and automation. By the end of this book, you'll be able to use SQL in everyday business scenarios efficiently and look at data with the critical eye of an analytics professional. Please note: if you are having difficulty loading the sample datasets, there are new instructions uploaded to the GitHub repository. The link to the GitHub repository can be found in the book's preface.
Table of Contents (11 chapters)
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9
9. Using SQL to Uncover the Truth – a Case Study

Summary

In this chapter, we learned how to interface our database with other analytical tools for further analysis and visualization. While SQL is powerful, there are always going to be some analyses that need to be undertaken in other systems and being able to transfer data in and out of the database enables us to do just about anything we want with our data.

In the next chapter, we will examine data structures that can be used to store complex relationships in our data. We will learn how to mine insights from text data, as well as look at the JSON and ARRAY data types so that we can make full use of all of the information available to us.

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SQL for Data Analytics
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