Book Image

SQL for Data Analytics

By : Upom Malik, Matt Goldwasser, Benjamin Johnston
3 (1)
Book Image

SQL for Data Analytics

3 (1)
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)
9
9. Using SQL to Uncover the Truth – a Case Study

Introduction

In the previous chapter, we looked at how we can import and export data into other analytical tools in order to leverage analytical tools outside of our database. It is often easiest to analyze numbers, but in the real world, data is frequently found in other formats: words, locations, dates, and sometimes complex data structures. In this chapter, we will look at these other formats, and see how we can use this data in our analysis.

First, we will look at two commonly found column types: datetime columns and latitude and longitude columns. These data types will give us a foundational understanding of how to understand our data from both a temporal and a geospatial perspective. Next, we will look at complex data types, such as arrays and JSON, and learn how to extract data points from these complex data types. These data structures are often used for alternative data, or log-level data, such as website logs. Finally, we will look at how we can extract meaning out...