How SQL for Data Science Helps With Data Visualization

How SQL for Data Science Helps With Data Visualization | Data Science and Analytics | Emeritus

Structured Query Language (SQL) is important for businesses because it helps with data analysis and management. This is because it allows organizations to extract insights from large data sets efficiently. That is why businesses often look for professionals well versed in SQL who can work with data and provide valuable insights to their organizations. As companies collect more data than ever before, the ability to extract and analyze data using SQL has become a highly sought-after skill for data analysts, scientists, and other professionals. This blog provides an overview of SQL, its importance in data science, and other significant aspects. But first, let’s understand the importance of SQL for data science professionals.

SQL for Data Science

What is the Significance of SQL for Data Science?

SQL refers to a programming language used for managing and analyzing relational databases. According to Statista, it was among the five most-used programming languages in 2022. In data science, SQL is often used to extract data from databases to perform various data analysis tasks such as querying, aggregating, and joining data tables for workflow purposes.

How is SQL Used in Data Science?

Data Extraction

Data scientists use SQL to extract specific data from a database for analysis.  Moreover, they use SQL queries to filter and select data based on different criteria such as time, location, or other variables.

Data Transformation

SQL cleans and transforms data, creating new tables or views that are easier to analyze. It can be used to merge, join, or concatenate tables, as well as calculate new variables.

Data Aggregation

Using SQL, you can calculate summary statistics, group data into specific categories such as age and gender, and calculate averages like net national income and gross domestic income.

Data Visualization

SQL can be used to create tables and views that are easier to visualize using data visualization tools like Tableau, Power BI, or other data visualization software.

Data Exploration

In terms of data exploration, SQL for data science professionals is important to identify patterns and relationships between different variables. With SQL queries, you can filter data and calculate different metrics that help with further exploratory data analysis.

ALSO READ: What is SQL? What are its Applications and Benefits

Common SQL Commands in 2023

SQL commands refer to instructions that “command” a database to perform tasks and other data queries. These commands can be used to search databases and perform functions, such as creating tables, modifying or adding data, dropping tables, etc.

Here is a breakdown of some of the most common ones.


It is the most commonly used SQL command and specifies the columns from where one retrieves data.

Example: SELECT column1, column2 FROM table_name.


The FROM command specifies the table or tables that one wants to retrieve data from.

Example: SELECT column1, column2 FROM table_name1, table_name2.


It is used to specify a condition that must be true for a row to be included in the result set.

Example: SELECT column1, column2 FROM table_name WHERE column1 > 10.

How Can SQL be Used for Data Visualization?

Scientists use SQL  to create meaningful visualizations that can provide insights into the data. SQL is used for the following purposes as well:

Creating Summary Tables

This programming language can also be used to create summary tables that aggregate data and provide an overview of the data. Moreover, these tables further create charts, graphs, or other visualizations that can help understand the data.

Joining Tables

Another important use of the language is joining multiple tables based on common columns. Additionally, doing this can be useful for combining data from different sources and creating a unified data set.

Filtering Data

Scientists use SQL to filter data based on specific conditions. Now, this is useful for creating visualizations that focus on a specific subset of the data.

Calculating Metrics

Different metrics such as averages, sums, or counts can be calculated using SQL. They also help create visualizations that provide insight into the data.

Creating Views

SQL views can be used to create virtual tables that are based on SQL queries. Data scientists then use these views to create visualizations. Moreover, they can also update the views as new data becomes available.

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In What Ways Can SQL Help in Data Wrangling?

SQL for Data Science

SQL can help in data wrangling by providing powerful tools for filtering, transforming, and cleaning data. Here are some ways in which it helps with the process.

Filtering Data

SQL’s SELECT statement provides a powerful way to filter data based on different criteria. For example, the WHERE clause filters rows based on a specific condition, such as date range, null values, or missing values.

Joining Tables

The JOIN commands from SQL combine tables and merges data. Thus, data scientists can join tables based on a common key, merge data, and fill in missing data.

Aggregating Data

SQL’s GROUP BY statement provides a powerful way to summarize and aggregate data. For example, it helps calculate summary statistics such as mean, median, mode, min, max, and count.

Creating Views

SQL views can be used to create virtual tables based on SQL queries. In fact, views can simplify data wrangling by providing a pre-filtered and pre-joined version of the data, which can then be easily used for further analysis.

ALSO READ: What are the Most Lucrative Programming Languages to Learn in 2023

Best Practices to Adopt When Using SQL in Data Science

By following the best practices for SQL for data science professionals, they can write efficient and maintainable codes that can help improve the quality of their analysis and reduce the risk of errors or issues.

Here are some of the best practices for using SQL for data science experts.

  • Use comments to document queries and code for future reference
  • Keep queries simple to improve readability and maintainability
  • Use descriptive names for tables and columns to make queries more understandable
  • Use table aliases to simplify queries and reduce the amount of code needed
  • Always validate input data to ensure it meets the expected format and avoid potential errors or issues
  • Optimize queries by using indexes, optimizing sub-queries, and avoiding nested loops
  • Use transactions when making multiple changes to the database to ensure consistency and avoid data corruption
  • Avoid creating too many temporary tables as it can slow down queries and consume too much memory
  • Regularly backup the database to prevent data loss in case of an unexpected failure

Learn More About SQL with Emeritus

In today’s data-driven world, SQL has become an essential tool for data scientists. It helps data scientists access, manage, and analyze large data sets to derive insights that can drive better business decisions. For professionals looking to build a career in data science, learning SQL is a must-have skill. 

Emeritus offers online data science courses that can help professionals acquire these skills. The courses cover topics like data analysis, statistics, machine learning, and data visualization, and provide hands-on experience using tools like Python and SQL. 

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SQL for Data Science

About the Author

Content Writer, Emeritus Blog
Sanmit is unraveling the mysteries of Literature and Gender Studies by day and creating digital content for startups by night. With accolades and publications that span continents, he's the reliable literary guide you want on your team. When he's not weaving words, you'll find him lost in the realms of music, cinema, and the boundless world of books.
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