SQL for Data Analytics: Harness the power of SQL to extract insights from data, 3rd Edition

Take your first steps to becoming a fully qualified data analyst by learning how to explore complex datasets
Key Features

Master each concept through practical exercises and activities
Discover various statistical techniques to analyze your data
Implement everything you've learned on a real-world case study to uncover valuable insights

Book Description

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.
What you will learn

Use SQL to clean, prepare, and combine different datasets
Aggregate basic statistics using GROUP BY clauses
Perform advanced statistical calculations using a WINDOW function
Import data into a database to combine with other tables
Export SQL query results into various sources
Analyze special data types in SQL, including geospatial, date/time, and JSON data
Optimize queries and automate tasks
Think about data problems and find answers using SQL