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


Data analytics is a powerful method through which you analyze raw data to find patterns and gather predictions that help you to understand the world. The goal of analytics is to turn data into information and knowledge. To accomplish this goal, statistics, or descriptive statistics and statistical significance testing, are used to understand data.

Univariate analysis, a branch of descriptive statistics, can be utilized to understand a single variable of data. It can also be used to find outliers and the distribution of data by utilizing frequency distributions and quantiles. It is useful in finding the central tendency of a variable by calculating the mean, median, and mode of data and the dispersion of data using the range, standard deviation, and IQR.

Bivariate analysis is also used to understand the relationship between datasets. You can determine trends, changes in trends, periodic behavior, and anomalous points regarding two variables by using scatterplots. You can...