With so many programming languages gaining widespread popularity within the world of data science, it can be difficult to determine the right data science tools that you should learn or develop to boost your career. In comparison to other programming languages, Structured Query Language, otherwise known as SQL, is one of the most commonly used tools within the field. In an analysis of 32,000 data science jobs on LinkedIn, researchers at Dataquest found that SQL was the most in-demand skill in data science with this number being expected to increase over time. Students and professionals that are interested in pursuing a career in data science should also consider learning more about SQL, its history, and why it has become such an important tool to know within the industry.
History and Background of SQL
Created in the 1970s by E. F. Codd, SQL was initially developed to allow individuals to work with relational databases. A database is a collection of data that is stored within a computer or server, so working with relational databases is focused on how the different types of data within a database relate to each other. The concept of relational databases was created by Codd, so SQL is used to create and analyze these relational models within a database. Similar to the spreadsheets of data that we have become used to through programs such as Microsoft Excel, Codd’s model of relational databases was also based on the organization of data in rows and columns with descriptors and designations that could be used to compare and contrast the data holdings through indexing and various search functions.
Flash forward a few decades, and now SQL is being used for multiple purposes within data science due to its origination as a database management tool. In this current moment in time, data science has moved from the study of small data sets to massive big data holdings, making any software and programming language which focuses on making the management of data easier a hot commodity for students and professionals alike. Although SQL is not as user-friendly as languages like R and Python, it is still widely used within the world of data science because there are several unique attributes that make this programming language especially suited to the organization and analysis of data.
How Data Scientists Use SQL
SQL’s role as one of the most popular and commonly used data science programming languages can be attributed to the development of SQL as a data science tool. Due to the fact that SQL was created to manage databases, this programming language is well suited for working with big data and relational databases in a variety of ways. Primarily, there are four key ways that data scientists use SQL within their projects: data cleaning, database design, and querying, data organization, data visualization, as well as data modeling.
- Data Cleaning - Cleaning data is one of the first steps that a data scientist completes after data collection. Specifically, data cleaning is the process of sorting through data to remove any outliers or data which is not relevant to the project, or that skews the data in some way. When working with a small dataset cleaning data can be completed using a spreadsheet or any editing software, however big data requires programming languages like SQL in order to effectively clean the data due to SQL’s ability to search the data-set for specific keywords, phrases, or markers.
- Database Design and Queries - Building on the data cleaning function, whenever SQL is mentioned, it is most commonly described as a programming language that can be used to search databases through querying. Querying is a specific type of programming code that can be used to find a specific type of data within a large database or dataset. Through learning how to query, data science students and professionals can better manage the data they have not only during the cleaning process but also during the steps of data organization and analysis. Queries can also be used to identify the missing data in a dataset, which allows data scientists to determine important next steps in the data collection process.
- Data Organization -SQL is very popular when it comes to the organization of data through its metadata functions. Within a database, metadata can be simply described as data about data, and metadata serves as the organizational structure for categorizing different types of data or different aspects of data in the same data set or database. Therefore, one of the database management techniques of SQL allows users to easily organize data into new categories through creating metadata and other forms of labeling and categorization.
- Data Visualization - After the data has been organized and labeled in a way that makes it easier to sort and analyze, like many programming languages that are commonly used within data science, SQL can also be used to visualize data through its Dashboard tool. The SQL dashboard creates charts and graphics based on the data that is held within your database, and these visualizations can be used to communicate key insights about the data on hand and how you can use it to solve problems through creative solutions.
- Data Modeling - Similar to data visualization, data modeling is the creation of either visual or statistical representations of a dataset. Data modeling is most common within engineering, as it is generally used within machine learning and prototyping, but data scientists can also use multiple free and open-source tools that are compatible with SQL in order to create models of their data, such as Oracle SQL Developer and the SQL Database Modeler.
Why SQL Is Important to Data Science
Due in part to its age and longevity, SQL continues to be an important programming language within the field of data science. Through its unique abilities as a language that was created to work with relational databases and as a database management system, SQL has become ubiquitous within the world of data science due to its function as a multipurpose tool that can be used at every stage of the process of working with data. From housing data that is collected into a secure database to the cleaning and organization of data, SQL can even be used to visualize and model data after it has been analyzed. In short, SQL is important to data science because of the way that it is used by data scientists and its persistence as one of the most popular programming languages in the industry.
Ready to start learning SQL?
You can learn all of the ways that data scientists use SQL by taking one of Noble Desktop’s SQL classes which focus on how to use this programming language for data science and database design. In addition to classes that focus on SQL, there are also a variety of data science classes and certificate programs that focus on how to use programming languages and querying to further develop your skills. Whether you are interested in live online SQL classes or in-person SQL classes in your area there are many ways that you can begin learning this important programming language!