
So, you want to learn SQL?
Well, in this article, I will run through how I learned SQL in just 2 weeks, which helped me land my first entry-level data science role.
Let’s get into it!
My Background
When I set about learning SQL, I was midway through my journey to wanting to become a data scientist, and I was in my final year of studying for a master’s in Physics.
At this point, I had already coded for a few years in Fortran from my first couple of years at university and just finished taking a Python course and was doing some Coding problems alongside.
See [here](https://medium.com/towards-data-science/how-i-would-learn-python-in-2024-from-zero-b1c5edcdec84) and here to read about my journey of learning code in Fortran and Python!
I mention this because I am not under any illusion that I clearly had some advantages going into learning SQL that others may not have.
I was studying physics then, had previous coding experience, and knew how to approach learning technical topics. These clearly helped me learn SQL more quickly than the average person would.
However, SQL is still a programming language (although some may argue it’s not!) that I think people can learn pretty quickly, at least faster than other languages like Python or JavaScript.
For reference, it probably took me 2 to 3 months before I could say I felt proficient in Python, and even then, I was scratching the surface of its functionality.
SQL is very intuitive and reads like English. It’s also reasonably small, especially for the bits needed for entry-level Data Science positions and interviews.
It is smaller mainly because SQL is domain-specific. It’s explicitly designed to retrieve, manipulate, and work with data, while many other languages are general purpose. Take Python for example, it can be used for both machine learning and web development.
Although some may greatly disagree, in my opinion SQL is closer to Excel than low-level languages like C, C++ and Rust.
The point is that SQL is relatively straightforward to learn compared to other languages, even without a tech or STEM background.
Obviously, you can’t master it in a couple of weeks, but you can learn it to an excellent standard to pass entry-level data science interview questions and be proficient in your day job, which is what I did!
Now, let’s dive into how I learned SQL and the courses I took!
If you are interested, you can read my full journey to how I became a data scientist below:
My Roadmap

Initial Courses
I started off learning SQL by first taking the free courses on W3Schools and TutorialsPoint.
These two courses are fantastic and give you a rundown of all the need-to-know functions and syntax to become a proficient user of SQL.
I made it a point to tackle every exercise problem, as this is a crucial step in solidifying your understanding. I highly recommend you do the same to ensure you thoroughly grasp all the concepts along the way and really focus on the problems.
Also, during these courses, I did Google things quite often to help further my understanding by reading short articles and going on stack overflow. So, I didn’t purely rely on the courses and often seeked alternative explanations when I didn’t understand something properly.
It took me about a couple of days to complete each course, but I was working on them all day, which I appreciate some people may not have that luxury!
In hindsight, you don’t need to do both courses; only one is sufficient to learn the required syntax and functions. You can probably cover most of the content and exercise problems over a weekend if are not too busy.
The main thing is to take your time with the material. Don’t worry about how long it takes; focus on understanding it, as that will allow you to go faster later.
For reference, these courses teach you the general syntax of the following SQL flavours: MySQL, SQL Server, MS Access, Oracle, Sybase, Informix and Postgres. To be honest, most SQL synatx is universla across the flavours, there are just some functions that may not exist for all of them.
Second Course
After those two courses, I took The Complete SQL Bootcamp: Go from Zero to Hero by Jose Portilla on Udemy, which is a paid course that is currently priced at £60.
Udemy often runs discounts and promotions, so it may be significantly cheaper depending on when you take the course!
I took this course because it showed me how to install and run SQL on my local computer, host a server for my database, and learn more about the infrastructure side of SQL.
The previous two courses taught me only the syntax in closed online environments, not the whole ecosystem around SQL, which is incredibly useful to know if you plan to use it in your day job.
The Udemy course also delved into some other functions in SQL, which helped further solidify my understanding of what I learned in my first initial courses.
Overall, the Udemy course is ~9 hours long, but I worked through it fairly slowly, and it took me a few days to complete. If you are really quick and productive, you can probably finish it in one day.
Again, the speed is not really important; it’s all about the intuition and knowledge you develop.
Practise
After all that learning and from past experience, I knew that to become good at anything, you need to practise it. That was the exact next step I took.
As the famous entrepreneur and investor Naval Ravikant __ puts it:
It’s not 10,000 hours, it’s 10,000 iterations.
I spent another few days or so practicing SQL problems on Hacker Rank. For those of you unfamiliar, Hacker Rank is a platform that contains numerous coding challenges and problems in a wide range of programming languages.
I also used Hacker Rank to help me learn Python!
I chose Hacker Rank because I had several screening interviews that contained SQL questions specifically on Hacker Rank. So, to increase my chance of getting past this stage, I practised extensively on this platform.
I found these SQL problems are more similar to real-life queries I create in my day job as a data scientist than the equivalent Python problems on this platform.
Feel free to use other websites like LeetCode if you want; the main point is that you get experience solving problems independently to build your competency.
In total, I did probably around ~50 questions; that’s when I felt I got the hang of things and could answer most entry-level data science position problems.
This number may be different for you; it could be more or less. The point is to get reps in until you feel comfortable.
And that’s that! This was a breakdown of my SQL journey. It took me ~2 weeks to become proficient in the entry-level data science standard, although if I had been more productive, I reckon I could have done it in a week. But we can’t have it all!
Now, let’s move on to advice I wish I had for those of you who are learning SQL!
Advice

Like anything, learning SQL requires consistent effort, time and practice.
Even though I said earlier that it is seen as an "easier" programming language, not everything is straightforward and will require some deep thought to get your brain around it.
To this day, I still mess up JOINS, and it once led to me showing incorrect results in my job!
In general, these are my top bits of advice:
- Learn "just enough", then start solving problems. In reality, I probably only needed to take one course before moving on to coding challenges, and this would have expedited my learning.
- Take extra time to learn and practise topics like JOINS, COMMON EXPRESSION TABLES, and WINDOW functions. Most people find these quite challenging, but they are also very valuable in many real-life scenarios.
- Don’t spend hours on a problem if you are stuck. Give yourself a timeframe to solve it, and if you can’t find the solution within that time limit, use a hint or just look at the solution. Just remember to take your time to understand the solution, not just copy and paste it.
- Don’t rush through the course. Take your time to really digest the material, and don’t worry about how long it takes you. It’s really about going slow to go fast later.
I want to stress again that it’s virtually impossible to master anything in a couple of weeks. The point of this article is to show you how you can become proficient in SQL in a short space of time, which will help you land that initial data science or analyst role that you are after.
Hope you enjoyed this article!
If you are interested in becoming a data scientist, Python is another must-know language. Check out my previous posts below, where I explain the steps I would take if I were learning Python again from scratch!
Another Thing!
I have a free newsletter, Dishing the Data, where I share weekly tips for becoming a better Data Scientist and my general experience in the field.
Connect With Me!
- LinkedIn, X (Twitter), or Instagram.
- My YouTube Channel to learn technical data science and Machine Learning concepts!