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.

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Table of Contents (11 chapters)
Preface
Preface
About the Book
Free Chapter
1
1. Understanding and Describing Data
1. Understanding and Describing Data
Introduction
The World of Data
Methods of Descriptive Statistics
Statistical Significance Testing
Summary
2
2. The Basics of SQL for Analytics
2. The Basics of SQL for Analytics
Introduction
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
3
3. SQL for Data Preparation
3. SQL for Data Preparation
Introduction
Assembling Data
Transforming Data
Summary
4
4. Aggregate Functions for Data Analysis
4. Aggregate Functions for Data Analysis
Introduction
Aggregate Functions
Aggregate Functions with GROUP BY
The HAVING Clause
Using Aggregates to Clean Data and Examine Data Quality
Summary
5
5. Window Functions for Data Analysis
5. Window Functions for Data Analysis
Introduction
Window Functions
Statistics with Window Functions
Summary
6
6. Importing and Exporting Data
6. 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
7
7. Analytics Using Complex Data Types
7. Analytics Using Complex Data Types
Introduction
Date and Time Data Types for Analysis
Performing Geospatial Analysis in Postgres
Using Array Data Types in Postgres
Using JSON Data Types in Postgres
Text Analytics Using Postgres
Summary
8
8. Performant SQL
8. Performant SQL
Introduction
Database Scanning Methods
Performant Joins
Functions and Triggers
Summary
9
9. Using SQL to Uncover the Truth – a Case Study
9. Using SQL to Uncover the Truth – a Case Study
Introduction
Case Study
Summary
Appendix
Appendix
1. Understanding and Describing Data
2. The Basics of SQL for Analytics
3. SQL for Data Preparation
4. Aggregate Functions for Data Analysis
5. Window Functions for Data Analysis
6. Importing and Exporting Data
7. Analytics Using Complex Data Types
8. Performant SQL
9. Using SQL to Uncover the Truth – a Case Study

Summary

Data is a powerful method by which to understand the world. The ultimate goal for analytics is to turn data into information and knowledge. To accomplish this goal, statistics can be used to better understand data, especially descriptive statistics, and statistical significance testing.

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

Bivariate analysis can also be used to understand the relationship between data. Using scatterplots, we can determine trends, changes in trends, periodic behavior, and anomalous points in regard to two variables. We can also use the Pearson correlation coefficient to measure the strength of a linear trend between the two variables. The Pearson correlation coefficient, however, is subject to scrutiny due to outliers or the number of data points used to calculate the coefficient. Additionally, just because two variables have a strong correlation coefficient does not mean that one variable causes the other variable.

Statistical significance testing can also provide important information about data. Statistical significance testing allows us to determine how likely certain outcomes are to occur by chance and can help us to understand whether the changes seen between groups are of consequence.

Now that we have the basic analytical tools necessary to understand data, we will now review SQL and how we can use it to manipulate a database in the next chapter.

Previous Section
End of Chapter 1
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