Why and How to Learn SQL

SQL, or structured query language, is a common programming language used in many industries and contexts to manage and manipulate relational databases to achieve an end goal. SQL allows you to query, update, and delete data from a relational database. It is easy to use and understand, making it approachable to beginners with limited prior experience in programming.

With dedication and practice, anyone can learn SQL and become proficient in working with relational databases. Follow the steps outlined here to learn SQL from scratch and begin your journey as a professional who utilizes SQL.

What is SQL?

Standard query language (SQL) is a popular programming language designed to interact with databases. IBM researchers developed SQL (originally coined SEQUEL) in the 1970s to handle the large amounts of data stored in their mainframe computers properly. Since then, SQL has become a widely used language for managing relational databases. Many data-centric careers now require SQL skills, including data analysts, database administrators, and software developers.

What does SQL do?

With SQL, you can conduct several database operations, such as building new tables, adding and updating records, deleting records, and writing queries to pull important data. SQL provides standardized syntax and commands for working with relational databases, making it easy to communicate with various database management systems (DBMSs) like MySQL, Oracle, MariaDB, and PostgreSQL.

Querying large amounts of data stored in relational databases is efficient and powerful with SQL. It is widely used in various companies and industries today. Some examples include finance, health care, marketing, web development, information technology, retail, education, and telecommunications.

Is it better to learn SQL or Python first?

SQL and Python are valuable skills for working with data, but they serve different purposes and apply in various contexts, so deciding which one to learn first will depend on your overall goals. SQL is a specialized language that works with relational databases. Conversely, Python is impactful with multiple tasks, including analytics, web development, and automation.

Learning SQL is an excellent starting point if your primary goal is to work with databases and manipulate data stored in relational databases. SQL is essential for tasks like creating tables, adding and modifying data, and writing queries to pull sets of data

On the other hand, if you want to work with data more flexibly and customize it, Python might be a better choice. NumPy, sci-kit-learn, and Pandas are all examples of tools available in Python that interact with datasets and run analyses. Overall, Python has a rich ecosystem of unique and valuable tools and libraries.

Ultimately, the choice between learning SQL or Python first depends on the tasks you want to perform and accomplish. If you're interested in working with data stored in relational databases, then SQL is a good place to start. If you want to work with data more generally and have a wider range of applications and options for manipulating data, then Python may be a better choice.

How to learn SQL

Learning SQL involves a few key steps. Below are some tips on how to begin your journey of learning SQL:

1. Start with the basics

Before you begin learning SQL, you should understand its basic applications, what it can accomplish, and its limitations. Next, you can start with the fundamentals of SQL, such as creating and modifying tables, inserting data, and querying data. 

2. Learn advanced topics

As you become more proficient in SQL, you can learn more advanced topics, such as common table expressions, temporary functions, and self-joins. Many online courses and resources exist to help you develop further with SQL to become an expert, such as the Data Cleaning in SQL: Prepare Data for Analysis Guided Project offered by the Coursera Project Network.

3. Practice regularly

Like any new skill, learning SQL requires regular practice. Block off weekly time to focus on SQL projects and practice writing queries. With more practice, you will notice your efficiency and proficiency increase.

Who needs to learn SQL?

Learning SQL benefits many professionals who need to work with data stored in relational databases. If you are interested in a career with more direct SQL applications,  you might consider the following choices:

Data analyst

Average annual salary in the US: $83,848 [1]

Job outlook (projected growth from 2022 to 2032): 35 percent [2]

Education requirements: To become a data analyst, you typically need to earn a bachelor’s degree, commonly in mathematics, computer science, or a related field. 

SQL is essential for working with large data sets stored in relational databases. Data analysts and developers use SQL to extract, transform, and analyze data, often integrating it with other programming languages. In this role, you can quickly pull data from your databases and present insights that assess current practices and inform future decision-making.

Database administrators

Average annual salary in the US: $103,931 [3]

Job outlook (projected growth from 2022 to 2032): 8 percent [4]

Education requirements: To become a database administrator, you will typically need to complete a bachelor’s degree in computer science or a related field. 

Database administrators work with large databases that store critical business data. In this role, you will use SQL to create and manage database structures, back up and restore data, and troubleshoot database issues.

Software developer

Average annual salary in the US: $101,003 [5]

Job outlook (projected growth from 2022 to 2032): 25 percent [6]

Education requirements: To become a software developer, you typically need to earn a bachelor’s degree in computer science or a related field. 

Developers often build software applications on top of relational databases, so software developers need to know SQL to work with these databases. They usually use SQL to design and store procedures that they can repeatedly use to reference and analyze data within the database.

Other database languages

While SQL is one of the most widely used database languages, other languages offer unique capabilities and are well-suited for specific use cases. Explore two other database languages besides SQL.

• NoSQL: NoSQL is a non-relational database language that stores and manages unstructured or semi-structured data. Unlike SQL, which uses a structured query language, NoSQL does not have one structure. Rather, it uses various data models and query languages. Practical contexts for  NoSQL include big data applications, real-time analytics, and web applications that require scalability and high performance.

• MongoDB query language (MQL): MQL is a database language used with MongoDB, a popular NoSQL document database. MQL is a flexible, JSON-like query language that allows developers to retrieve and manipulate data stored in MongoDB databases easily. It will enable them to store their data in the same format as the application code, leading to faster development times with fewer errors.

NoSQL is a common alternative to SQL that allows users to work with unstructured data. This type of database management system comes in several formats, including document databases such as MQL. While SQL is a widely used database language, depending on your data structure, you may benefit from the unique capabilities of NoSQL systems.

Getting started with Coursera

SQL, or structured query language, is an important tool for working with databases in many different contexts and industries. SQL is designed explicitly for relational databases, making it easy for data analysts and software developers to create, modify, and reference tabular data. 

To learn SQL, you can start with online resources like the courses or certificates available on Coursera. On Coursera, you can enroll in some of the top analytics courses in the world. Check out the Google Data Analytics Professional Certificate. This Professional Certificate allows you to learn key analytical skills that top professionals use today, such as data visualization and cleaning. This course also features tools such as SQL and R programming. You don’t need previous experience for this beginner-friendly program, which takes roughly six months to complete, working 10 hours per week.