With multiple data science tools and software to choose from, SQL continues to be one the most popular programming languages within the realm of data science and database design. Standing for Structured Query Language, SQL is one of the oldest programming languages geared towards working with data, relational databases, and their corresponding management systems. Through creating queries and structuring data through organizational functions, SQL is an essential programming language for data scientists across fields and industries. If you would like to learn more, keep reading about the decades-long history and uses of SQL for Data Scientists,
History of SQL for Data Science
Created in the 1970s, SQL is a programming language based on the concept of relational databases. While working in the San Jose Research Lab at IBM, E. F. Codd conceptualized relational databases, as a way to retrieve data in a database which have a set of criteria or pre-defined relationships between datasets. This relationality is based on how the data is organized and within SQL data is structured in rows and columns making it easier to organize data once it has been collected and stored within a database. Similar to the categorical systems that are found in libraries and archives, each piece of data within the database is given a record or code making it easily retrievable to both users and programmers. Most relational database management systems (RDBMS) operate using SQL because each piece of data can be given multiple descriptors and identifiers within the system.
Moving forward in time, while SQL has always been known as a programming language that is used for managing relational databases, within the 21st century this language has also become popular within other aspects of data science. SQL is not only used to organize and retrieve data within a database but also to make the process of analyzing and understanding a dataset more efficient and streamlined. Through aggregating large stores of data, SQL allows data scientists to group data in ways that make it easier to recognize patterns and codes within the database. Like a more advanced spreadsheet, SQL coding is focused on creating and manipulating metadata in order to return information that you want to know. In addition, as a free open-source programming language, users are able to constantly iterate and update the ways to use SQL within and outside of online communities.
The Most Popular Uses of SQL
As a programming language that has been commonly used for the organization of data, SQL is primarily used by students, practitioners, and data science professionals that need to keep data separate from the analysis. And while SQL is often described as a programming language, SQL is actually much more of a querying language. Querying is one of the most popular uses of SQL and it allows data science students and professionals to search a database for a particular type of data. It can be used to select and retrieve data from one part of the database to place it in another part of the database or to filter data based on a specific search protocol. When using SQL, querying also ensures that there is no risk of accidentally changing data when you are analysing it and if the data is changed, a saved query can also be re-run to analyze the new data.
With the popularity of big data, SQL is also commonly used for handling large stores of data and designing databases. Through the creation of different data types, SQL focuses on the quality control of data entries that are made within a database. For example, you will receive an error if you try to enter a word into a field that should contain a number. SQL keeps data science students and professionals accountable for the structure and syntax of the system. Understanding the nature of relational databases, and using SQL will also assist you in using databases in programming languages such as R or Python. As a programming language that is highly compatible with other languages, SQL is just one of many data science tools to have in your toolkit.
How Data Scientists Use SQL
Due to the popularity of big data and analytics, most companies and institutions store their data in some form of database. In contrast to saving data within discrete files or folders, storing data within a relational database offers data scientists the unique opportunity to compare and contrast multiple datasets at the same time. These comparisons can then be used to generate insights about how specific data types work with each other within a database. As an open-source programming language, SQL also works well with other programming languages and data visualization software, making it a perfect complement to other data science tools.
Similar to other programming languages, Data Scientists primarily use SQL for data storage, organization, analysis, and modeling. More than any other function, SQL is the go-to programming language for data organization, and the language can be used to dive deeper into a dataset, by identifying missing datasets and incorrect formatting, as well as re-organizing the data in ways that suit your needs. Due to the fact that SQL is a more direct way of accessing and understanding data through utilizing queries, this programming language is especially useful for data science professionals working in any field and it is common for employers to require students and professionals working in data science to have some knowledge of how to use SQL.
Want to learn more about using SQL for Data Science?
As an essential data science tool, SQL is a versatile programming language which can be used to work with relational databases by effectively organizing and analyzing large stores of data. Offering several live online courses, Noble Desktop’s data science classes include several SQL bootcamps which introduce the basics of querying and database design. You can also find SQL classes in your area which include multiple bootcamps and workshops for students across experience levels. Noble Desktop offers a free on-demand Intro to SQL seminar for students and professionals who want a quick overview of the language and how it can be used in data science.