How Long Does It Take to Learn SQL? (Updated in 2024)

When handling relational databases, there are a couple of ways to facilitate data querying. While methods like ORMs can be beneficial for abstracting database interactions, SQL remains the best language for direct data manipulation and analysis. Having a strong grasp of SQL foundations can provide a crucial edge in an industry where data-driven decisions are paramount.

How long does it take to learn SQL starting from scratch? In this article, we’ll discuss some of the mathematical background behind the language, how to measure your skill level, and more SQL resources to check out.

The learning curve for SQL is highly dependent on your background. For non-programmers learning SQL as their first language, it may be easier to understand since it’s rather declarative: you describe what you want, not the steps to achieve it.

However, if you come from a programming background that typically introduces a procedural paradigm, getting accustomed to how SQL manages and operates on data can be more difficult. Because the language is based on relational algebra, SQL operations typically work on a set rather than a specific entity.

SQL basics are relatively intuitive, but you may wrestle a bit with the syntax. For example, when I first learned SQL, some things I found myself confused with include:

• Syntax ordering: SQL clauses can get confusing as they’re not executed sequentially, instead requiring a specific order.

SELECT → FROM → WHERE → GROUP BY → HAVING → ORDER BY → LIMIT.

• Execution ordering: SQL operations are executed in a different order from how they’re written.

FROM → WHERE → GROUP BY → HAVING → SELECT → ORDER BY → LIMIT.

• JOINS: Understanding the subtle distinctions between various JOIN operations (INNER, LEFT, RIGHT, and FULL) can be daunting. It’s important to be able to visualize how these operations merge tables and filter records.

I was also initially confused about how GROUP BY interacts with aggregation functions like COUNT(), SUM(), and RANK(). There are also compound clauses with SQL, like INSERT INTO, that function as one operation but are made of two words. Not that big of a deal, but it can make things confusing anyway.

SQL, the ubiquitous language of relational databases, is not arbitrarily constructed. Its design reflects principles derived from relational algebra– a rigorous mathematical framework. To truly grasp the nuances of SQL, it’s important to understand some of the underlying foundational operations:

• Selection (σ) is similar to a gatekeeper, sifting through the vast data rows. It filters out specific rows based on certain conditions, much like the WHERE clause we use in SQL. This operation helps narrow down the data to match our needs.
• When we talk about picking and choosing in SQL, we’re referring to Projection (π). This operation selects distinct columns from a table. In practice, it’s executed by listing the desired columns right after the SELECT keyword, so we only see the relevant data points.
• The Cartesian Product (×) pairs rows from two distinct tables without any particular linking condition, essentially producing every combination of rows from both tables. This is similar to if you were to do a JOIN in SQL without setting any criteria.
• Finally, the Join (⨝) operation acts as a bridge, linking two tables based on a shared attribute or condition. In SQL, this method exists as various JOIN types, like the INNER JOIN. This operation ensures that data from different tables can be analyzed cohesively.

These definitions may seem intimidating, but, in practice, these concepts are relatively easy to understand. While JOINs are a bit harder, with enough hands-on practice, they become more intuitive to use in a relational context.

For SQL, like many disciplines, mastery isn’t about completion– it’s about depth, adaptability, and continuous engagement. While some may see it as an endpoint, true SQL mastery adapts to the language’s evolving landscape.

Diverse Paths to Proficiency

SQL learning journeys can be as diverse as the language. Here are a few perspectives:

25-year MS SQL Server User: “I’ve always leaned into the ‘learn as you go’ philosophy. Even after 20 years with MS SQL Server and five years with Access before that, I find myself revisiting books and searching on Google. Commands change, new versions emerge, and continuous learning becomes paramount.”

10-Year SQL Enthusiast: “By the fifth year, I felt I had reached a notable level of proficiency. Yet, the journey of discovery and learning in SQL is unending.”

Jay from Interview Query: “My personal mastery moment in SQL was when I grasped self-joins and could effectively manage 90% of my analytical queries. When faced with challenges like funnel analysis or statistics, I could quickly decipher the necessary data and promptly formulate a SQL query to get my answer.”

Measuring Your SQL Journey

Assessing your proficiency in SQL is both a reflection and a guidepost for future learning. Here are some ways to approach this:

• Objective-Driven Approach: Begin with your goals. Consider the milestones in your SQL projects. How effectively and efficiently can you achieve them?
• Credential-Driven Approach: Stack up your certifications and qualifications. While these credentials may not be the entirety of your expertise, they can provide a structured benchmark for your skills.
• Competency-Driven Approach: Dive into hands-on assessments like SQL interview questions. Reflect on your problem-solving speed, familiarity with functions, and the diversity of queries you can craft.

While there are no definitive “levels” for measuring SQL proficiency, there are benchmarks that can help you determine your SQL knowledge.

Here, we’ll describe different levels of SQL proficiency under three main categories: beginner, intermediate, and advanced. To measure your level of understanding, try out the questions we’ve created below.

Beginner SQL Proficiency

At the beginner level, you should be able to identify basic SQL terminologies and analyze how they work together to create a database architecture.

These questions are mostly theory-based and are designed to test your familiarity with basic SQL concepts. While these are “easy” questions that a beginner should be able to answer, it’ll be more difficult if you’re missing any of the fundamentals, including:

• Data filtering (SELECT and WHERE)
• Basic aggregation
• Temp tables
• Cursors
• Basic concepts regarding databases
• Basics of pivots

To test your mastery of these topics, try the following questions:

1. Define a database. Where do SQL databases fit in?

A database is a structured collection of data. While databases exist in various forms, SQL databases, in particular, are digital and are often relational. This means they store data in tables that can relate to one another, which allows for both design simplicity and more complex functions, depending on the use.

2. Describe the differences between a DBMS and a RDBMS.

A DBMS (Database Management System) is a software system that facilitates the creation, management, and use of databases. A RDBMS (Relational Database Management System) is a type of DBMS that uses a relational model. This means the data is organized into tables with defined relationships, allowing for a more connected view of the data. Unlike a traditional DBMS that stores data in file formats, an RDBMS leverages tabular structures.

3. What’s a Primary Key in SQL?

A primary key in SQL is a type of self-induced constraint that limits the occurrence of a value within a tablet to only once, with NULL values not allowed. The primary key constraint combines the NOT NULL and UNIQUE constraints.

You can create a table with a single field as a primary key using the following code:

CREATE TABLE Employees (
	ID int NOT NULL,
	FirstName VARCHAR(255) NOT NULL,
	LastName VARCHAR(255),
      PRIMARY KEY (ID)
);

To enforce a primary key on two fields, do the following:

CREATE TABLE Employees (
	ID int NOT NULL,
	FirstName VARCHAR(255) NOT NULL,
	LastName VARCHAR(255),
	CONSTRAINT PK_Employees PRIMARY KEY (ID, FirstName)
);

Intermediate SQL Proficiency

At an intermediate level of SQL proficiency, it’s important to be familiar with certain concepts that help create insightful queries, although not necessarily the most in-depth functions and concepts.

These topics serve as building blocks for creating more complicated queries. At this level, you should be able to solve most problems ranging from surface-level to relatively complex issues, even if they’re not necessarily the best and most efficient solution available.

Some intermediate-level concepts include:

• NULL handling
• Types of joins
• Subqueries
• Constraints
• Indexes
• GROUP BYs and aggregation

To test yourself on these concepts, try the following questions:

4. Given a table of product purchases, determine the customers who were upsold.

Write a query to get the number of customers that were upsold by purchasing additional products.

Note: If the customer purchased two things on the same day, that doesn’t count as an upsell since they were purchased within a similar timeframe.

Input:

transactions table

Column	Type
id	INTEGER
user_id	INTEGER
created_at	DATETIME
product_id	INTEGER
quantity	INTEGER

Output:

Column	Type
num_of_upsold_customers	INTEGER

5.What’s UNION and How Is It Different From UNION ALL?

At its core, UNION is simply UNION ALL with more parameters. For example, let’s say you’re given a dataset with two tables with employee information:

id table

Column	Type
name	VARCHAR
id	INTEGER

salary table

Column	Type
name	VARCHAR
salary	INTEGER

To merge the two tables and remove duplicate records, you could use UNION.

Example:

SELECT name, id
from demo
UNION
SELECT name, id
from demo

The result will create a table that merges duplicate records (i.e., ‘name’) and generates a table with only one name column. If you use UNION ALL, the resulting table would have duplicate records, so ‘name’ would occur twice.

Ultimately, UNION ALL is faster than UNION but concatenates all the values, while UNION can detect relations and duplicates.

6. Describe what PIVOTS are and how they’re used in SQL.

A PIVOT operation allows you to rotate data from a row-centric to a column-centric model. This transformation often involves data aggregation, allowing for more compact and insightful data presentation.

For a practical pivot operation, you might use a structure like:

SELECT column1, column2
FROM table
PIVOT
 (
   AggregateFunction(column_to_aggregate)
   FOR column_to_be_pivoted IN (list_of_values)
 ) AS AliasName

7. What are some common SQL constraints?

The family of SQL constraints is extensive, each serving a specific purpose.

• INDEX: Indexes (sets a numerical value) to a database, allowing for easy data retrieval
• NOT NULL: Disallows NULL values into a column
• UNIQUE: Disallows duplicate values into a column
• DEFAULT: Assigns a predefined value when a value has not been specified
• CHECK: Checks if values satisfy a condition
• FOREIGN KEY: Prevents actions that destroy relations or links between tables
• PRIMARY KEY: Ensures that the values are unique and not null

8. How Do You Handle Missing or NULL Values in SQL?

Handling missing values in SQL is always a challenge. Improper handling can result in accidental biases that shift business decisions in the wrong direction.

Before mastering the coding methods to handle missing data, it’s important to understand the following concepts.

• Imputation: Using algorithms (i.e., machine learning models) to fill in missing values
• Casewise Detection: Removing a variable when it has too many factors missing
• Listwise Detection: Removing a variable if it has too many missing values
• Dummy Variable Adjustment: Replacing a missing variable using the measures of central tendency

To find NULL values in your tables, you can use the following code:

SELECT column1
FROM table1
WHERE column1 IS NULL;

Use NOT NULL to utilize reverse logic in handling null values:

SELECT column1
FROM table1
WHERE column1 IS NOT NULL;

Making imputation models for handling NULL values is notoriously tricky. If you can create custom models for managing NULL values, then you’ve probably reached an advanced level of SQL understanding.

Advanced SQL Proficiency

It takes more than constant theoretical learning and practical application to attain SQL mastery. You also need to be familiar with specific nuances of SQL, which will allow you to create queries that are efficient, easy to read, and, most importantly, work without compromising data integrity.

Below is a list of concepts that those who’ve reached advanced levels of SQL proficiency should know:

• User-defined functions
• Execution plans
• Triggers (safe executions)
• How to design and construct OLAPs
• How to identify and implement data structures
• The advantages and disadvantages of data structures and what use cases highlight their strengths most
• Hints

To test your level of understanding of these topics, try the following questions:

9. Find the first touch attribution for each customer from an online retailer.

The schema below is for a retail online shopping company consisting of two tables, attribution and user_sessions.

• The attribution table logs a session visit for each row.
• If conversion is true, then the user converted to buying on that session.
• The channel column represents which advertising platform the user was attributed to for that specific session.
• Lastly, the user_sessions table maps many-to-one session visits back to one user.

First touch attribution is defined as the channel with which the converted user was associated when they first discovered the website.

Calculate the first touch attribution for each user_id that is converted.

Example:

Input:

attribution table

Column	Type
session_id	INTEGER
channel	VARCHAR
conversion	BOOLEAN

user_sessions table

column	type
session_id	INTEGER
created_at	DATETIME
user_id	INTEGER

Example output:

user_id	channel
123	facebook
145	google
153	facebook
172	organic
173	email

10. What’s the difference between OLAP and OLTP?

Given their similar names, there’s a tendency to use OLAP and OLTP interchangeably, but they are systems that can be identified as infrastructures with opposite philosophies. OLAP and OLTP are not mutually exclusive but rather work together to create an efficient database architecture.

OLAP stands for OnLine Analytical Processing, while OLTP represents OnLine Transaction Processing.

OLTP involves processing transactions and recording each detail, with built-in data guards to ensure data integrity. Because OLTP works in a fast-paced system, it needs to process and store information quickly, so heavy processing is discouraged.

An example of a real-life application of OLTP is online shopping, where a database records the user ID, order information, and other relevant data. OLTP quickly generates accurate records and avoids colliding data (i.e., time-sensitive information like in-store item inventory).

On the other hand, OLAP utilizes the information from the OLTP system to generate insights for business decisions. While OLTP is fast, OLAP needs more time to create accurate insights from the data warehouse with its analytics-oriented approach.

11. Given a table of product subscriptions with a subscription start date and end date for each user, write a query that returns true or false whether or not each user has a subscription date range that overlaps with any other completed subscription.

Let’s examine each of the conditions first and see how they could be triggered. Given two date ranges, what determines if the subscriptions would overlap?

Let’s set an example with two date ranges: A and B.

Let ConditionA>B demonstrate that DateRange A is completely after DateRange B.

|—- DateRange A ——| |—Date Range B —–| _

ConditionA>B is true if StartA > EndB.

Let ConditionB>A demonstrate that DateRange B is completely after DateRange A.

|—- DateRange A —–| _ _ |—Date Range B —-|

Condition B>A is true if EndA < StartB.

Overlap then exists if neither condition is held. In that, if one range is neither completely after the other nor completely before the other, then they must overlap.

12. Write a SQL query to output the average number of right swipes for two different variants of a feed ranking algorithm by comparing users that have swiped 10, 50, and 100 swipes in a feed_change experiment.

If you’re a data scientist in charge of improving recommendations at a company and you develop an algorithm, how do you know if it performs better than the existing one?

One metric to measure performance is called precision (also called positive predictive value), which has applications in machine learning as well as information retrieval. It is defined as the fraction of relevant instances among the retrieved instances.

In a fast-paced environment where insights can have rippling side effects on business decisions, it’s tempting to time yourself on how fast you can generate queries. While this can be a benchmark for skill, most often, it’s the accuracy that matters, not the speed.

Generally speaking, simple queries (like joining basic tables or pivoting a table) can be generated in around five minutes or less, given that the database is quite limited in size.

However, as a data scientist, it’s common to encounter huge 1000-line queries that take considerable effort to generate. The longer your code is, the higher the risk of generating logical errors within your query. As such, it’s more important to prioritize having a logically sound query over speed to a certain extent.

A lot of SQL material is static and generally hard to follow. For those without programming experience, creating commands instead of using a mouse and a pointer can be challenging to conceptualize.

For easy-to-understand SQL material that adapts to your learning style, Interview Query provides SQL courses that are supported by a huge community. If you’re looking for additional interview questions, check out our extensive question bank that covers SQL and other important data science topics.