This comprehensive guide, featuring SQL tutorials, a cheat sheet, and real-world SQL projects, offers everything you need to get started with SQL. The included FAQs address common challenges, making it an essential resource for SQL beginners.
The six SQL tutorials summarized below will help support you on your journey to learning SQL. Check out the associated full SQL tutorials for more details. If you're just starting out and want to actively learn SQL directly in your browser, enroll in Dataquest's SQL Fundamentals skill path for free.
Introduction to SQL and Databases - here's a breakdown of what this SQL tutorial teaches:
SELECT statements.PRAGMA commands to examine table structuresTEXT, INTEGER, REAL)<, >, =) to filter numeric dataBETWEEN for range queries and IN for multiple value checksAND and OR operatorsLIKE with wildcards (% and_) for pattern matchingSELECT DISTINCTORDER BYASC and DESC keywords to control sort directionCASE expressions for conditional logic in queriesJOIN operationsSummarizing Data in SQL - here's a breakdown of what this SQL tutorial teaches:
SUM, AVG, COUNT, MIN, and MAX functions to summarize dataGROUP BY clause to categorize data for analysisGROUP BY with aggregate functions to summarize data by categoryHAVING clause to filter grouped data based on aggregate conditionsBy picking up these SQL data summarization techniques, you'll be able to extract valuable insights from large datasets, identify trends, and make informed decisions. These skills are essential for anyone working with data, from business analysts to data scientists, enabling you to turn raw information into actionable knowledge that drives success in your projects and organization.
Combining Tables in SQL - here's a breakdown of what this SQL tutorial teaches:
WHERE, GROUP BY, and ORDER BY clauses for complex analysesUNION, INTERSECT, and EXCEPT to combine entire result setsSQL Subqueries - here's a breakdown of what this SQL tutorial teaches:
SELECT, WHERE, and HAVING clausesIN, ANY, and ALL operators with multi-row subqueriesEXISTS operator to check for related recordsWindow Functions in SQL - here's a breakdown of what this SQL tutorial teaches:
OVER clause to define the window of rows for calculationGROUP BY queriesROWS or RANGEPRECEDING, FOLLOWING, and CURRENT ROWSUM, AVG, COUNT) across windowsPARTITION BY to group calculationsROW_NUMBER(), RANK(), and DENSE_RANK() for ordering and rankingNTILE() to divide results into groupsLAG() and LEAD() to access data from previous or future rowsFIRST_VALUE() and LAST_VALUE() for comparisons within a partitionPERCENTILE_CONT() and PERCENTILE_DISC() for percentile calculationsCUME_DIST() and PERCENT_RANK() for relative rankingsQuerying Databases with SQL and Python - here's a breakdown of what this SQL tutorial teaches:
Test your knowledge with the SQL exercises below. For additional practice problems and real-time feedback, try our interactive coding environment, great for SQL practice online.
For this SQL exercise, we'll explore beer recipe data from Punk API. This data is stored in a table called beers. The following table shows the first 3 rows of the database table. We've omitted some of the columns for display purposes.
| id | name | tagline | first_brewed_year | abv | contributed_by |
|---|---|---|---|---|---|
| 1 | Buzz | A Real Bitter Experience. | 2007 | 4.5 | Sam Mason |
| 2 | Trashy Blonde | You Know You Shouldn't | 2008 | 4.1 | Sam Mason |
| 3 | Berliner Weisse With Yuzu - B-Sides | Japanese Citrus Berliner Weisse. | 2015 | 4.2 | Sam Mason |
beers table.
name, tagline, and contributed_by columns (in that order).LIMIT to select only the first ten rows.SELECT name, tagline, contributed_by
FROM beers
LIMIT 10;Practice solving this SQL exercise using our interactive coding environment designed for SQL practice online with real-time feedback.
A data analyst has some data on test scores and demographics of random children. The data is in a table named performance.
They would like to know how many children are in the table.
Write a query that counts the rows in the performance table.
num_children.COUNT function. SELECT COUNT(*) AS num_children
FROM performance;Practice solving this SQL exercise using our interactive coding environment designed for SQL practice online with real-time feedback.
A data analyst has some data on test scores and demographics of 1000 random children. The data is in a table named performance. The analyst also has an updated version of the dataset with more scores and students, stored in a table called performance_updated. Finally, some of the students have been surveyed about their school life. This data is in a table called survey.
The analyst would like to combine the information from the performance table and the survey table.
performance table and the survey table.
student_id column.JOIN keyword is by itself.SELECT *
FROM performance
JOIN survey
ON performance.student_id = survey.student_id;Practice solving this SQL exercise using our interactive coding environment designed for SQL practice online with real-time feedback.
SELECT StatementsA data analyst has some data on test scores and demographics of 1000 random children. The data is in a table named performance. Additionally, some of the students have been surveyed about their school life. This data is in a table called survey.
The analyst would like to calculate the proportion of students in each race_group category.
race_group category. Incorporate a subquery into the answer.race_group category, a GROUP BY clause is necessary. In order to divide this number by the total to get the proportion, we need a scalar subquery to calculate it.SELECT race_group,
COUNT(*) * 100.0 /
(SELECT COUNT(*) AS overall
FROM performance) AS proportion
FROM performance
GROUP BY race_group;
Practice solving this SQL exercise using our interactive coding environment designed for SQL practice online with real-time feedback.
OVER() ClauseLet's use the OVER() clause to write a query that finds the difference between employee's salaries and the company's average salary.
employee Table| employee_id | last_name | first_name | department | title | hire_date | salary |
|---|---|---|---|---|---|---|
| 1 | Adams | Andrew | Management | General Manager | 2002-08-13 | 108000 |
| 2 | Edwards | Nancy | Sales | Sales Manager | 2002-04-30 | 98900 |
| 3 | Peacock | Jane | Sales | Sales Support Agent | 2002-03-31 | 87000 |
| 4 | Park | Margaret | Sales | Sales Support Agent | 2003-05-02 | 69800 |
| 5 | Johnson | Steve | Sales | Sales Support Agent | 2003-10-16 | 76500 |
| 6 | Mitchell | Michael | IT | IT Manager | 2003-10-16 | 89900 |
| 7 | King | Robert | IT | IT Staff | 2004-01-01 | 67800 |
| 8 | Callahan | Laura | IT | IT Staff | 2004-03-03 | 78000 |
| 9 | Edward | John | IT | IT Staff | 2004-09-18 | 75900 |
| 10 | Megan | Hanks | Sales | Sales Support Agent | 2005-10-16 | 71500 |
employee table:
first_namelast_namesalarydifferenceTo calculate the average salary of all employees, you need to:
OVER() clause with no argument, which creates a window containing all of the table's rows.AVG() function to the window.
SELECT first_name, last_name, salary,
salary - AVG(salary) OVER() AS difference
FROM employee;
Practice solving this SQL exercise using our interactive coding environment designed for SQL practice online with real-time feedback.
To query an SQLite database from Python, we first need to establish a connection to the database using the sqlite3 library. The sqlite3 library provides a convenient way to interact with SQLite databases in Python, allowing us to execute SQL queries, retrieve data, and manage transactions.
Let's start by connecting to the database.
sqlite3 library.connect() method to establish a connection to the database. The database file name is world_population.db.
world_population.db when connecting to the SQLite databasesqlite3). This is the first line of the expected answer.
import sqlite3
# Connect to the SQLite database
conn = sqlite3.connect('world_population.db')
# Close the connection
conn.close()
Practice solving this SQL exercise using our interactive coding environment designed for SQL practice online with real-time feedback.
Check out our comprehensive SQL Cheat Sheet that provides a quick reference for essential SQL commands.
You can also download the SQL Cheat Sheet as a PDF.
The best way to get SQL practice is to work on a real world challenge in the form of projects. Use these Dataquest guided projects to test your skills and show off your knowledge to potential employeers by including them in your portfolio.
Difficulty Level: Beginner
In this beginner-level guided project, you'll step into the role of a data analyst to explore and analyze Kickstarter project data using SQL. You'll start by importing and exploring the dataset, followed by cleaning the data to ensure accuracy. Then, you'll write SQL queries to uncover trends and insights within the data, such as success rates by category, funding goals, and more. By the end of this project, you'll be able to use SQL to derive meaningful insights from real-world datasets.
To successfully complete this project, you should be comfortable working with SQL and databases, such as:
Upon completing this project, you'll have gained valuable skills and experience, including:
Difficulty Level: Intermediate
In this intermediate-level guided project, you'll step into the role of a data analyst at a scale model car company. You'll use SQL skills like joins, subqueries, and common table expressions (CTEs) to explore the company's sales database and provide data-driven answers to key business questions about inventory, customers, and marketing. By analyzing product, order, and customer data, you'll identify which products to prioritize, how to segment customers, and how much to spend acquiring new customers. Through hands-on practice with a real database, you'll showcase your ability to use SQL to solve actual business problems.
To successfully complete this SQL project, you should be comfortable with the following SQL skills:
Upon completing this project, you'll have gained valuable SQL skills and experience, including:
Difficulty Level: Advanced
In this advanced guided project, you'll step into the role of a data analyst at Northwind Traders, a global gourmet food distributor. Using advanced SQL techniques like window functions and common table expressions (CTEs) on the company's database, you'll analyze sales data to provide actionable insights that drive strategic business decisions. This hands-on project allows you to apply your SQL skills to a real-world scenario, strengthening your ability to construct complex queries, interpret results, and make data-driven recommendations.
To successfully complete this SQL project, you should have the following skills:
Upon completing this project, you'll have gained valuable skills and experience, including:
The answer to this question really depends on your goals. They're very different things.
SQL is a query language. It's really only useful for interacting with, filtering, and lightly analyzing data from databases. It offers a lot of power for working with data in those contexts, but it can't do all the things a full programming language like Python can do.
Python is a programming language. That makes it a bit more complex to learn, but it also means it can do a lot more. You can analyze data in Python, but you can also use it to build machine learning models, make video games, program a robot, or design art.
If you work with data often — if you're opening spreadsheets every day and you know what VLOOKUP is — there's a good chance you'd benefit from learning both languages.
At Dataquest, we teach both Python and SQL as part of our Data Analyst and Data Scientist career paths. Both skills are required for full-time data jobs, although R can be substituted for Python, learning SQL is non-negotiable.
SQL (Structured Query Language) is a powerful tool for managing and analyzing relational databases. It's essential for data analysis, allowing you to efficiently extract, manipulate, and analyze large amounts of structured data.
In the data industry, SQL is widely used because of its ability to handle massive datasets and perform complex operations quickly. As we like to say, "SQL is the foundation that all data work is built on." This is because SQL enables you to retrieve specific information from databases, making it an indispensable skill for anyone working with data.
For instance, imagine you have a massive spreadsheet with millions of rows of sales data. With SQL, you can quickly find exactly what you need, such as the top 5 sales from last month, without manually searching through all that data.
Let's take a look at a simple SQL query:
SELECT *
FROM orders
LIMIT 5;
This query essentially says, "Show me all columns from the orders table, but only give me the first 5 rows." It's a quick way to get a snapshot of your data without being overwhelmed by thousands of results.
Learning SQL is a valuable investment for anyone interested in working with data. With many SQL courses available, you can gain this essential skill and open up numerous opportunities in data analysis and related fields.
Whether you're an aspiring data analyst, data scientist, or simply want to be more proficient with data in your current role, SQL is a skill that will serve you well. It's the universal language of data professionals, connecting you to a world of insights hidden within databases.
That's a very personal question — what's very easy to one person may seem very difficult to the next, and vice versa. However, most people find SQL pretty easy to learn, especially when compared to full-on programming languages like Python or R.
That's because unlike a "full" programming language, SQL is a query language. It's built specifically for interacting with relational database management systems such as Microsoft SQL Server, Oracle, SQLite, MySQL, etc. For that reason, there's not as much to learn, and some of the more complex concepts that exist by necessity in more holistic programming languages aren't a factor in SQL.
That said, the fact that most people find SQL relatively easy to learn does not mean that you will, or that you should feel ashamed if you find it challenging! Particularly if this is your first foray into the world of programming, you should be ready for a challenge.
But don't worry. No matter what your background is, you can totally learn SQL. Our Community is here to help you anytime you need it!
If you work with data, learning SQL is an essential skill that can open doors to new career opportunities. To get started, follow these practical strategies to become proficient in SQL:
PRAGMA table_info(orders) to get familiar with table structures.SELECT statements and gradually increasing complexity.Remember, learning SQL is a journey. Start with a structured course, like the one offered by Dataquest, to build a solid foundation. Then, continue to challenge yourself with increasingly complex queries and real-world projects. With persistence and practice, you'll be surprised at how quickly your SQL skills improve and the impact they can have on your work.
When choosing an SQL course for beginners, look for one that lays a solid groundwork while preparing you for real-world data challenges. A well-structured course should guide you through a logical progression of topics, starting with basic concepts like database schemas and simple queries, and then gradually introducing more complex ideas.
One of the most important aspects of an SQL course is hands-on practice. The best courses offer plenty of opportunities to write queries and work with real datasets. For example, the guided project analyzing Kickstarter data can help you apply SQL skills to answer practical questions.
Let's take a closer look at an example query:
SELECT main_category, backers, pledged, goal,
pledged / goal AS pct_pledged,
CASE
WHEN pledged / goal >= 1 THEN "Fully funded"
WHEN pledged / goal BETWEEN .75 AND 1 THEN "Nearly funded"
ELSE "Not nearly funded"
END AS funding_status
FROM ksprojects
WHERE state IN ('failed')
AND backers >= 100
AND pledged >= 20000
ORDER BY main_category, pct_pledged DESC
LIMIT 10;
This query demonstrates how a good SQL course should teach you to combine various concepts like filtering, sorting, and conditional logic to extract meaningful insights from data.
When selecting a course, look for one that breaks down complex topics into understandable pieces and provides clear explanations of database concepts. An interactive learning environment can also enhance your understanding and keep you engaged.
Ultimately, the goal is not just to learn syntax, but to understand how to think about and work with data effectively. Choosing the right SQL course can set you up for success in your data career, whether you're aiming to become an analyst, scientist, or simply want to be more proficient with data in your current role.
Learning SQL can be a game-changer for your career in the data industry and beyond. SQL is the quiet workhorse that runs everything in the data world, from small business databases to massive big tech applications.
One of the primary career benefits of mastering SQL is its universal applicability. The post highlights that SQL is the common language among analysts, scientists, and engineers across various domains. This widespread use means that SQL skills can open doors to numerous job opportunities in different industries, making it a valuable addition to your professional toolkit.
SQL proficiency enables you to work effectively with large datasets, extract valuable insights, and drive data-informed decisions. For instance, at Dataquest, SQL is used daily to monitor course quality and quickly diagnose and fix issues. This real-world application demonstrates how SQL skills can directly contribute to improving products and services in any organization.
Moreover, learning SQL prepares you to tackle data challenges of any scale. Knowing SQL has helped make a real difference in my work by enabling data-driven decisions that keep my team focused. This ability to derive actionable insights from data is highly valued in today's business environment.
A SQL course provides a solid understanding of databases, tables, queries, and joins—concepts you'll use repeatedly as you progress in your data career. This foundational knowledge serves as a stepping stone for more advanced data analysis and manipulation techniques.
What's more, SQL is generally considered easier to learn compared to full programming languages like Python or R, making it an accessible starting point for those new to data analysis. You can also obtain a SQL certification to demonstrate your proficiency.
Investing time in a SQL course can significantly enhance your career prospects. The fundamental nature of SQL in the data world, combined with its wide-ranging applications, makes it a valuable skill that will serve you well throughout your professional journey. Whether you're aiming to become an analyst, scientist, or simply want to be more proficient with data in your current role, SQL is a skill that will continue to pay dividends.
SQL's strength lies in its ability to perform complex data operations with remarkable efficiency. Consider this simple yet powerful query:
SELECT order_id, product_name, sales, discount
FROM orders
WHERE discount IN (0.15, 0.32, 0.45);
This query quickly filters and retrieves specific order information from potentially millions of records, showcasing SQL's effectiveness in data analysis tasks.
While SQL is powerful on its own, it often complements other data analysis tools. Many professionals use SQL for initial data extraction and processing before moving to Python or R for advanced analytics or visualization. This combination allows analysts to leverage the strengths of each tool in their data workflow.
Learning SQL through a comprehensive SQL course can significantly enhance your data analysis capabilities. It provides a solid foundation for working with databases, a skill that's essential in today's data-driven industries. Whether you're aspiring to become a data analyst, data scientist, or simply want to be more proficient with data in your current role, SQL is a valuable skill that will serve you well throughout your career.