How to Become a Data Analyst in 2024 (Technical Skills)
March 2024 marked 7 years of being a Data Analyst. When I was first going down this path, I had no idea where to start. I had heard so many different tools, technologies, and languages that I spent 2 months just trying to decide what to start learning. That was a waste of my time.
Now that it’s been 7 years, this is the advice I would give my former self. This is a focus only on technical skills as that’s where most of us start our learning.
What tools does a data analyst need to know?
In short, something to pull data (SQL) something to modify data (SQL, Excel, Python, R) and something to visualize data (Excel, Tableau, Power BI, or even Python/R). There’s a lot of overlap in tools, but some work better for specific things than others. I’ll talk about how I use them and the most common uses as opposed to mentioning everything each tool can do.
So, what tool should I learn first?
The one you’re most excited about! Excitement gives you some momentum and that means you’re more likely to stick with it. I mean this in all seriousness. Learning can be tiring and it’s made easier when you want to do it.
For me, this was Excel (or Google Sheets, they’re interchangeable). Excel is also the starting point that I recommend if you have no preference. This is for the simple reason that this tool is the most commonly used in any data role, and it’s easy to use. Most people have used it before and have some familiarity, which reduces the barrier to start.
Once you know Excel, or if you just hate spreadsheets, learn SQL. It shows up in almost every data analyst role and is pretty easy to get started with. It’s also the language that we use to extract data from a database. And what’s a data analyst without data?
What data visualization tools do I need to know?
Once you know Excel and SQL, it’s time to move on to a visualization tool. Excel can be a visualization tool and for the first 4 years of my career, I used it as such, alongside a dedicated tool like Tableau. However I would still heavily recommend learning a dedicated visualization tool. They have advantages, mostly when it comes to sharing dashboards and making them useful for others. But also in how they scale across teams/organizations.
Tableau and Power BI are the two most common data visualization tools. I would suggest learning one or the other, but not both. It’s tempting to want to learn them both to feel like you’re qualified for any job, but that’s a waste of your time. Learning one means you can pick the other one up with a few hours of learning. If you really want to learn both, learn one and then spend ~10 hours learning the other.
If you’re unsure which tool to pick, here’s how I suggest deciding:
Power BI - If you want to work at a large company that likely uses other Microsoft tooling (Outlook, Teams, etc.) then it’s likely that they also use Power BI. It’s a Microsoft tool. Companies commonly stick to the Microsoft tech stack if they use it for their communication. It’s also common for Power BI to be used in government or other public institution settings, as well as more traditional industries, like construction. It’s seen as more stable due to it being owned by Microsoft, and it integrates into that ecosystem.
Tableau - Basically if the above workplaces aren’t what you want to work in, go learn Tableau. Tableau has a wonderful community built around it and you can get a version of their tool for free, called Tableau Public. This is also where you can go look at thousands of visualizations to get inspiration. I’ve spent way more time in Tableau than Power BI and I love it.
What about programming languages like Python or R?
Those are both awesome, but in most junior roles they’re used significantly less than the above tools. Like less than 2% of the time. That being said, they have a lot of power and they have their place. I like to use Python for a few things:
Joining disparate datasets together for a one-off analysis. Once in a while a stakeholder will have a good request but the data they want me to join in just happens to be in a csv. In this scenario, I can pull the rest of my data with SQL and use Python to merge datasets and get results back.
Advanced analysis. If you want to run regression, simulations, or test a whole bunch of different variables, Python and R are excellent. They allow you to do a wider variety of things and because you can loop through a function you can run 100 different versions of an analysis without it taking you 100 times as long.
Automating things that I do a lot. If I regularly modify an output for a stakeholder, say by changing column names and adding a few in, Python and R work great for this. I can write a script that I can then use over and over again. If I find that I’m doing something like this in Excel regularly then I’ll create a script once I notice the pattern.
If you’re unfamiliar with Python and R and want to pick one to learn, I suggest going with Python. It’s more common in data roles at the moment. Where this is less true is in academic settings or those with a large number of statisticians. They often prefer R over Python as it has some advantages, specifically when trying to do statistics. It doesn’t matter that much which one you learn, similar to Tableau vs Power BI.
At this point, you know how to pull your data using SQL, you may know Excel to modify data and do visualizations, or you’ve gone the Tableau/Power BI route, and you know either Python or R at an intermediate level. You’re now easily qualified for Junior Data Analyst roles and honestly know more than a lot of intermediate analysts, from a technical perspective. I’d suggest a combination of applying to jobs AND working on a personal portfolio now. The portfolio will be super useful if you’re struggling to land a role. If you land one, it’s good for continuing to improve your skills if you want.
Bonus: Are there any other things I should know?
If you want to further beef up your technical skills, I suggest learning dbt (data build tool). dbt is used to transform data. It’s written in almost all SQL, with some really simple Jinja code to help it out. Super simple. Like writing {{ref('table_name')}} instead of ‘table_name’. Though you can skip this part if you want.
As a data analyst, I use it to create tables in places where I write the same query over and over again as a CTE or subquery. Instead of doing that intermediate step in my SQL query each time, I can just turn this into a table to join into my query. It saves time overall and also adds to the base of knowledge for the rest of the team. Just ensure that you aren’t creating loads of junk tables that only work for 1 purpose. It’s more of an Analytics Engineer’s tool, but it’s super useful for all data folks.
And that’s it!
This all probably feels overwhelming. I know that’s how I felt for the first while. I bounced between so many different courses and platforms that I wasted months of my time. I’ve since become a fan of platforms with learning tracks so that you don’t need to worry about what to learn next or if courses overlap. They also usually have built-in practice tools so that you don’t need to set up an environment yourself.
Because of that reason, I looked through all the courses and platforms that I’ve used and picked just 1 to suggest to you - Data Camp. I've even gone so far as to create an affiliate link for anyone who wants to get started. Click here to try it out datacamp.pxf.io/dylan. Full disclosure, I do monetarily gain from you using this link, but it doesn’t cost you anything extra. That being said, I sought Data Camp out because I love their content. Both the overall platform and the individual courses are well thought out. I would recommend using them.
If you don’t want something with a subscription, Udemy is my second choice. Wait for a sale as many of their best courses regularly (once a month or so) go on sale for $19.99. Be careful with who the course is created by on Udemy. There’s a wide variety and some are much less comprehensive than others, so do your research!
If you found this helpful, share it with a friend! I have a few other articles on this blog and I’m continuing to add more. I also provide coaching services so feel free to take a look around the site to see if that’s a fit for you.
Thanks for reading :)
