Book Image

The Applied SQL Data Analytics Workshop - Second Edition

By : Matt Goldwasser, Upom Malik, Benjamin Johnston
3.5 (2)
Book Image

The Applied SQL Data Analytics Workshop - Second Edition

3.5 (2)
By: 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. Hidden in this data are key patterns and behaviors that can help you and your business understand your customers at a deep, fundamental level. Are you ready to enter the exciting world of data analytics and unlock these useful insights? Written by a team of expert data scientists who have used their data analytics skills to transform businesses of all shapes and sizes, The Applied SQL Data Analytics Workshop is a great way to get started with data analysis, showing you how to effectively sieve and process information from raw data, even without any prior experience. The book begins by showing you how to form hypotheses and generate descriptive statistics that can provide key insights into your existing data. As you progress, you'll learn how to write SQL queries to aggregate, calculate and combine SQL data from sources outside of your current dataset. You'll also discover how to work with different data types, like JSON. By exploring advanced techniques, such as geospatial analysis and text analysis, you'll finally 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 The Applied SQL Data Analytics Workshop, you'll have the skills you need to start identifying patterns and unlocking insights in your own data. You will be capable of looking and assessing data with the critical eye of a skilled data analyst.

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Table of Contents (9 chapters)
Preface
About the Book
1
1. Introduction to SQL for Analytics
1. Introduction to SQL for Analytics
Introduction
The World of Data
Methods of Descriptive Statistics
Statistical Significance Testing
Relational Databases and SQL
Basic Data Types of SQL
Reading Tables: The SELECT Query
Creating Tables
Updating Tables
Deleting Data and Tables
SQL and Analytics
Summary
Free Chapter
2
2. SQL for Data Preparation
2. SQL for Data Preparation
Introduction
Assembling Data
Transforming Data
Summary
3
3. Aggregate and Window Functions
3. Aggregate and Window Functions
Introduction
Aggregate Functions
Aggregate Functions with GROUP BY
The HAVING Clause
Using Aggregates to Clean Data and Examine Data Quality
Window Functions
Statistics with Window Functions
Summary
4
4. Importing and Exporting Data
4. Importing and Exporting Data
Introduction
The COPY Command
Using R with Our Database
Using Python with Our Database
Best Practices for Importing and Exporting Data
Summary
5
5. Analytics Using Complex Data Types
5. Analytics Using Complex Data Types
Introduction
Date and Time Data Types for Analysis
Performing Geospatial Analysis in PostgreSQL
Using Array Data Types in PostgreSQL
Using JSON Data Types in PostgreSQL
Text Analytics Using PostgreSQL
Summary
6
6. Performant SQL
6. Performant SQL
Introduction
Database Scanning Methods
Performant Joins
Functions and Triggers
Summary
7
7. The Scientific Method and Applied Problem Solving
7. The Scientific Method and Applied Problem Solving
Introduction
Case Study
Summary
Appendix
1. Introduction to SQL for Analytics
2. SQL for Data Preparation
3. Aggregate and Window Functions
4. Importing and Exporting Data
5. Analytics Using Complex Data Types
6. Performant SQL
7. The Scientific Method and Applied Problem Solving

7. The Scientific Method and Applied Problem Solving

Activity 7.01: Quantifying the Sales Drop

Solution

  1. Load the sqlda database:
    $ psql sqlda
  2. Compute the daily cumulative sum of sales using the OVER and ORDER BY statements. Insert the results into a new table called bat_sales_growth:
    sqlda=# SELECT *, sum(count) OVER (ORDER BY sales_transaction_date) INTO bat_sales_growth FROM bat_sales_daily;

    The following output should be produced:

    SELECT 964

    Compute a 7-day lag function of the sum column and insert all the columns of bat_sales_daily and the new lag column into a new table, called bat_sales_daily_delay. This lag column indicates what the sales were 1 week before the given record:

    sqlda=# SELECT *, lag(sum, 7) OVER (ORDER BY sales_transaction_date) INTO bat_sales_daily_delay FROM bat_sales_growth;
  3. Inspect the first 15 rows of bat_sales_growth:
    sqlda=# SELECT * FROM bat_sales_daily_delay LIMIT 15;

    The following is the output of the preceding code:

    Figure 7.27: Daily sales...

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