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.
Table of Contents (9 chapters)
Preface
7
7. The Scientific Method and Applied Problem Solving

Basic Data Types of SQL

As previously mentioned, each column in a table has a data type. We review the major data types here.

Numeric

Numeric data types are data types that represent numbers. The following figure provides an overview of some of the major types:

Figure 1.33: Major numeric data types

Figure 1.33: Major numeric data types

Character

Character data types store text information. The following figure summarizes character data types:

Figure 1.34: Major character data types

Figure 1.34: Major character data types

Under the hood, all of the character data types use the same underlying data structure in PostgreSQL and many other SQL databases, and most modern developers do not use char(n).

Boolean

Booleans are a data type used to represent True or False. The following table summarizes values that are represented as Boolean when used in a query with a data column type of Boolean:

Figure 1.35: Accepted Boolean values

Figure 1.35: Accepted Boolean values

While all of these values are accepted, the values of True and False are considered to be compliant with best practice. Booleans can also take on NULL values.

Datetime

The datetime data type is used to store time-based information such as dates and times. The following are some examples of datetime data types:

Figure 1.36: Popular datetime data types

Figure 1.36: Popular datetime data types

We will discuss this data type more in Chapter 5, Analytics Using Complex Data Types.

Data Structures: JSON and Arrays

Many versions of modern SQL also support data structures such as JavaScript Object Notation (JSON) and arrays. Arrays are simply lists of data usually written as members enclosed in square brackets. For example, ['cat', 'dog', 'horse'] is an array. A JSON object is a series of key-value pairs that are separated by commas and enclosed in curly braces. For example, {'name': 'Bob', 'age': 27, 'city': 'New York'} is a valid JSON object. These data structures show up consistently in technology applications and being able to use them in a database makes it easier to do many kinds of analysis work.

We will discuss data structures in more detail in Chapter 5, Analytics Using Complex Data Types.

We will now look at the basic operations in an RDBMS using SQL.