With so many data science tools to choose from, it can be difficult to determine which one makes the most sense to use. Even once you learn more about a particular tool, it can still be hard to determine when and why you would use it. As one of the most popular specializations for Data Scientists, SQL is an essential data science tool and programming language. Created in the 1970s, SQL has a significant amount of longevity in the data science industry, and most companies store their data within a database. Primarily used within the world of database design and management, SQL is the go-to querying language for understanding how to organize and search through large stores of data. The following guide focuses on everything that you need to know in order to get started working with SQL.
Getting Started with SQL
Pronounced “sequel,” SQL (short for Structured Query Language) is a programming language primarily used to search and manage relational databases. The first step in working with SQL is downloading a database management system that is compatible with this programming language. While there are several relational databases that you can use, some of the most common are MySQL, SQLite, and SQL Server. Generally, it is recommended that you begin with an open-source and freely available database before upgrading to more expensive or sophisticated systems.
After downloading one of these relational databases, you can then upload a dataset into your database and practice different commands to gain familiarity with the system. Most relational databases import CSV files, so you should ensure that any data that you want to use is saved in that type of file, then you can simply import your data into the database from a library like GitHub, programs like Microsoft Excel, or web-based applications like Google Sheets. Once you have imported your data, there are multiple ways that you can use SQL for data science projects and analyses.
What You Can Do With SQL
Although SQL is primarily used for database management and design, there are several ways that you can use SQL within and outside of the data science industry. Data scientists use SQL for all of the following:
- Creating tables and metadata within relational databases.
- Joining tables together to compare or determine correlations.
- Querying a database, or creating search functions for a dataset.
- Writing commands like subqueries, which can be used to perform a multi-step operation.
- Identifying missing datasets within a database or handling null values.
- Finding and removing data within a database or dataset.
- Creating and updating records, or data types within a dataset.
- Securing a database through passwords to protect private data or sensitive information i.e. for cybersecurity.
Important SQL Concepts and Terms
After learning what you can do with SQL, it is important to understand the fundamentals of SQL and how you can use these concepts in practical ways. The following list includes some of the most important SQL concepts and terms that you should know, as well as links and instructions for how you can use these concepts to work with relational databases:
- Tables are rows and columns that are used to organize a database; objects that contain identifying data and can be used to organize data or examine the relationship between types of data within a database (CREATE TABLE)
- Joins combine columns from one or more tables in the database; acts as another method of creating relational comparisons (INNER JOIN, LEFT JOIN, FULL JOIN, CROSS JOIN, UNION)
- Data Types are categories used in the creation of metadata; they usually fall under the data categories of numeric, or numbers, characters, or letters, and temporal, date or time (CHAR, VARCHAR, NCHAR, NVARCHAR, DATE, TIME, TIMESTAMP, etc)
- Queries are a search function that can be used to select particular types of data from the database; queries are also considered to be the primary function of using SQL (SELECT, FROM)
- Filtering is a sorting function that allows a data science professional to choose which data that they want to see or select, and which data that they do not want to see or select at a particular time (DISTINCT, WHERE, HAVING, AND, OR, IN, NOT IN)
- Data Aggregation is the arrangement of data into identical groupings; it is commonly used for data that needs to be organized around a particular category or value (GROUP BY with COUNT, SUM, AVERAGE)
- Conditional Statements are expressions based on the statement, “If . . . Then” which are used to perform a certain command that requires some condition to be met before performing a certain task (WHERE; IF; CASE-WHEN-THEN-ELSE-FIND)
- Alias assigns a new name to a table or column; an alias can also be used to create a pseudonym for sensitive data (SELECT . . . FROM . . . AS)
Once you know what you want to do with SQL and some of the fundamentals of working with the language, begin practicing with relational databases and further developing your skills. There are several online tutorials and seminars that offer brief overviews and introductions to this querying language. In addition you can download SQL handbooks and work through various data science exercises with real-world data. Through practicing writing queries, creating tables, and aggregating data you will quickly move from the ranks of SQL beginner to more advanced and intermediate skills like using views and virtual tables or writing subqueries.
Need more guidance to begin using SQL? Noble Desktop offers several data science classes and a data science certificate which include bootcamps and courses on how to use SQL for beginner students. One course is SQL Level I, which teaches students the fundamentals of SQL queries, servers, and relational database management systems. You can also find SQL classes in your area, including multiple bootcamps and workshops for students across experience levels.