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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 FeaturesMaster each concept through practical exercises and activitiesDiscover various statistical techniques to analyze your dataImplement everything you've learned on a real-world case study to uncover valuable insightsBook DescriptionEvery 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 learnUse SQL to clean, prepare, and combine different datasetsAggregate basic statistics using GROUP BY clausesPerform advanced statistical calculations using a WINDOW functionImport data into a database to combine with other tablesExport SQL query results into various sourcesAnalyze special data types in SQL, including geospatial, date/time, and JSON dataOptimize queries and automate tasksThink about data problems and find answers using SQLWho this book is forIf you're a database engineer looking to transition into analytics or a backend engineer who wants to develop a deeper understanding of production data and gain practical SQL knowledge, you will find this book useful. This book is also ideal for data scientists or business analysts who want to improve their data analytics skills using SQL.
Basic familiarity with SQL (such as basic SELECT, WHERE, and GROUP BY clauses) as well as a good understanding of linear algebra, statistics, and PostgreSQL 14 are necessary to make the most of this SQL data analytics book.
Table of ContentsUnderstanding and Describing DataThe Basics of SQL for AnalyticsSQL for Data PreparationAggregate Functions for Data AnalysisWindow Functions for Data AnalysisImporting and Exporting DataAnalytics Using Complex Data TypesPerformant SQLUsing SQL to Uncover the Truth – a Case Study
Key FeaturesMaster each concept through practical exercises and activitiesDiscover various statistical techniques to analyze your dataImplement everything you've learned on a real-world case study to uncover valuable insightsBook DescriptionEvery 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 learnUse SQL to clean, prepare, and combine different datasetsAggregate basic statistics using GROUP BY clausesPerform advanced statistical calculations using a WINDOW functionImport data into a database to combine with other tablesExport SQL query results into various sourcesAnalyze special data types in SQL, including geospatial, date/time, and JSON dataOptimize queries and automate tasksThink about data problems and find answers using SQLWho this book is forIf you're a database engineer looking to transition into analytics or a backend engineer who wants to develop a deeper understanding of production data and gain practical SQL knowledge, you will find this book useful. This book is also ideal for data scientists or business analysts who want to improve their data analytics skills using SQL.
Basic familiarity with SQL (such as basic SELECT, WHERE, and GROUP BY clauses) as well as a good understanding of linear algebra, statistics, and PostgreSQL 14 are necessary to make the most of this SQL data analytics book.
Table of ContentsUnderstanding and Describing DataThe Basics of SQL for AnalyticsSQL for Data PreparationAggregate Functions for Data AnalysisWindow Functions for Data AnalysisImporting and Exporting DataAnalytics Using Complex Data TypesPerformant SQLUsing SQL to Uncover the Truth – a Case Study
540 pages, Kindle Edition
Published August 29, 2022
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Displaying 1 of 1 review
January 19, 2023
Actually way more helpful than I expected it to be, particularly if you use the database seed to run your own local instance and follow along by doing your own queries.
Displaying 1 of 1 review