Viewed as one of the most popular languages within data science and database design, SQL is known for its uses as a Structured Querying Language. The SQL programming language is used to communicate with a dataset through querying and other forms of searching and manipulating data. SQL is also commonly used to clean and organize a dataset, especially when working within relational database management systems (RDBMS). By using the SQL programming language within a RDBMS, any data scientist can learn how to prepare their dataset for analysis and long-term storage through the execution of a series of tasks and functions which speed up the process of data cleaning and organization.
What is Data Cleaning?
From data preparation to data wrangling, there are many names for the organization of data within a database, and data cleaning is included amongst those names. Prior to organizing a dataset, data can be seen as messy or unwieldy in some way. Within a SQL database, messy data may lack proper metadata, have missing values, or require some changes to the structure or what is included/not included in the database management system before being able to properly analyze the data.
A common occurrence when you are starting a new project or have multiple people working in a database at the same time, turning a mess of data into a clean and organized dataset is an essential data science skill. Prioritized as one of the first steps in the data science life cycle, data cleaning is considered to be a necessity before moving on to the data analysis stage. By cleaning and organizing the data that you are working with, it is much easier to perform exploratory and more complex analyses later on.
Why Use SQL for Data Cleaning and Organization
As an essential SQL skill, data cleaning and organization is a preparation process that is commonly completed within some type of SQL database. The following list includes some of the reasons why data science professionals rely on SQL for the process of cleaning and organizing a dataset.
Managing Metadata in SQL Databases
One of the key components of organizing and structuring a dataset is through managing the relevant metadata for your records. Metadata, or data about your data, is used to describe or name different parts of the dataset. Especially when working within a SQL database, which will require you to write queries in order to search through your data, you can retrieve object ids, names, and other information which is used to describe different parts of the dataset. By retrieving this information you can also gain a deeper understanding of how the database is structured and where you may need to make modifications or do some data cleaning.
Identify Missing Values
Another effective way of cleaning data within a SQL database is through identifying the missing values within the dataset. Missing values are important to find because if there is some type of data that is missing from the dataset, the accuracy of your analysis will be greatly influenced. Data scientists can identify the NULL values in a SQL database in order to determine what values are missing and how that missing data can be rectified.
Delete and Edit Records
One of the methods of fixing missing values or duplicated values within a dataset is through deleting and/or editing the records in the database. While this would be a difficult process to do in a less sophisticated storage system, the process is simplified by using a SQL database that has multiple functions and features which allow you to modify records in the database after the data has already been collected.
Top SQL Tools for Data Cleaning and Organization
There are multiple SQL database management tools that are used for data cleaning and organization, some of which are listed below. Each of these tools has its own unique features and capabilities for data preparation and automating functions.
- MySQL - Through writing queries, MySQL is a popular tool for data cleaning and organization, and MySQL Workbench in particular includes database development and data modeling features which allow users to edit tables.
- PostgreSQL - One of the most common functions for data cleaning and organization with PostgreSQL is the use of SQL String Functions. String Functions can return the length of a string of characters and are one of the ways that data scientists can manipulate data within a database.
- Microsoft SQL Server - With its own unique SQL syntax known as T-SQL, Microsoft SQL Server allows data science students and professionals to select from a variety of
- Metadata Functions that can be used to manipulate and manage data objects within the system. SQL Server can be used in conjunction with a variety of machine learning tools that automate the process of cleaning, organizing, and preparing a dataset.
Regardless of which database management system or SQL tools that you use, there are many ways to prepare data for the process of analysis by taking the time to clean and organize the dataset.
Want to learn more about Data Cleaning with SQL?
With so many methods of cleaning and organizing data, working with SQL and relational database management systems simplifies what could be a complex task. Noble Desktop offers multiple SQL courses which not only focus on learning specific SQL databases, but also working with the SQL programming language. The SQL Bootcamp includes instruction on how to use SQL to organize a database, with a focus on the PostgreSQL database management system. Through taking this course, beginner students will understand the fundamentals of SQL and other useful techniques when cleaning and organizing data.
For beginner data science students and professionals, courses like SQL Level I also give an introduction to database architecture and how to sort and organize data within a SQL database. Combining the SQL Level I-III courses, the SQL Server Bootcamp then focuses on moving from the process of data cleaning to data analysis with a curriculum focused on mathematical functions and more advanced querying methods. Whether you are interested in SQL for database design and management or to work on data science projects, Noble Desktop has multiple courses, bootcamps, and certificate bootcamps that should suit your interests!