129: Best Data Skills to Learn (And EXACTLY When to Learn Them)

It's easy to be intimidated by the insane amount of data tools out there, especially when you're starting from scratch and so many different resources tell you so many different tools to learn. In fact, a recent survey shows that there's over 2000 different data tools that you could be learning or using. But here's the truth. You don't have time to learn 2000 tools. Heck, you don't even have time to learn 20 tools. And luckily for you, you don't need to. That begs the question, what tools should you start out with if you're just starting as an aspiring data analyst? Well, if you're just starting out, you definitely don't want to waste time. That is like the number one thing. So you don't want to waste time learning skills that aren't useful. You also want to get your foot in the door as soon as possible and land that first data job. So this gives us two different levers or two different variables to play with. Number one, how popular a data tool is. And number two, How difficult a tool is to learn. We want to start with the popular and useful data programs that are easy to learn as well. You can sort of think of this like a 2D matrix with the x axis measuring difficulty and the y axis measuring how often a tool is required. So it makes sense to start with the quadrant where tools are high in demand and easy to learn. This is the lowest hanging fruit and the place where most people should start. But that still means that we have to determine what skills are required the most and which ones are the easiest to learn. Now, what skills are in demand the most? It's a difficult question to know. One way is to trust experts in the field like Ivy League legend Columbia University. They're smart, right? They're super trustworthy. Ivy League, Columbia. Uh, and if you go to their website, you'll actually see that they state that MATLAB is the. Third most popular data tool that might not mean anything to you right now, but I'll show you in a few minutes how you can actually prove data wise. This isn't true at all. That MATLAB isn't even in the top 10 of data skills to learn, or even the top 25 for that matter. As a data nerd, we should try to use data when answering these types of questions. It's actually a hard data to get, unfortunately, but luckily for us, my friend Luke Bruce. Has already been doing it. He created something called Data Nerd Tech, which is web scraped and analyzed, 2.5 million different data, job listings, especially the requirements, and then reports. What percentage of job descriptions mention skills as requirements? The data is constantly being updated, but as of the creation of this video, here is the top 10. sql, Excel, Python, Tableau, power bi R, SaaS, PowerPoint, word, Azure. But honestly, I think only skills that are required over 10 percent of the time should be the real focus points. So that leaves us with the big six. SQL at 47 percent Excel at 33% Python at 31%, Tableau at 24%, Power BI 20%, and R 17%. These are what I call the big six, and they're the six most important things to learn when you're trying to land your first data job. It's also important to note that these results are for all levels of data analyst roles, both junior and senior. Senior and intermediate. So just for the junior roles, there might be some slight differences. Now we know which data skills are important and required often in job descriptions, but which ones are easy to learn? Of course, learning data skills is a bit subjective, depending on your previous experience and honestly, your intelligence level, but there are some universal guidelines when it comes to the ease of learning data tools. Like you're probably already familiar with Excel. Am I wrong? You've used it before. Like in school or at another job, you've analyzed some sort of data at some point in your life, probably in Excel, whether it's school or work, you've probably opened up Excel. Correct me if I'm wrong, go in the comments and tell me if I'm wrong, but you're probably okay at Excel right now. And that's awesome because Excel is really used quite often in the data fields. That's why I think Excel is one of the things that you should start with when you're starting your data career journey is because it's easy. It's one of the easiest things that you can learn because you're already familiar with it. And to be honest, there's not even that much more to add to it. Of course, there's different techniques and things that you can do in Excel, but chances are you're already familiar with 50 percent of it. Next there's BI tools, BI standing for business intelligence, and honestly, they're like the, PowerPoint. If you haven't used Power BI or Tableau much, they can sound quite intimidating, but don't let it be. Both are pretty easy. They're just drag and drop analysis programs. It honestly feels a lot like PowerPoint where you click on something and you drag it to different places. And based on where you drag it, different things happen. It's all point and click drag and drop. You'll be able to figure it out. I promise. SQL or SQL stands for Structured Query Language. And it is a language, so it is a little bit harder to learn, but there's not really all that much, honestly, when you're first trying to land your first day at a job, there's probably about 20 different commands that you should be using in SQL. And so while it takes time to learn those commands, there's only really about 20 of them. And it's not that bad when you contrast that to another programming language like Python or R and I won't sugarcoat it. Learning to program is hard. It's like a new language. That's literally why they call them programming languages. And it takes time to even know the terms to start to program. Concepts like loops, functions, variables, these are very difficult to comprehend and they take a while to figure out. When I was first learning loops, It did not come easy and it took me a couple of weeks, but once you figure those out, then you can actually start learning Python or R that's the problem with these two. They're really in demand, but they're quite difficult to learn. Now, of course, there are easy data programs that we could be learning out there, some that are maybe even easier than Excel or easier than Tableau, but they're not part of the big six. So we're going to ignore them. So in my opinion, these are the easiest data skills to learn from easiest to hardest. Number one, Excel, two, Tableau, three, Power BI, four, SQL, five, R, and six, Python. Great! So now we know the most required data skills and we know the easiest data skills. So we can combine these two lists together and create our ultimate list or what I call the order of operations. To learning data skills. Now you might remember this idea of order of operations from when you were learning math in grade school. It's a concept to determine the sequence in which mathematical operations should be executed. Basically it's what you do first in a math equation. You may even remember PEMDAS, or if I like to remember it, please excuse my dear aunt Sally. And this is kind of a mnemonic to help you remember. The correct mathematical order of operations. So let's break down this mnemonic one by one. The P, or please, this stands for parentheses, which basically means you should perform the calculations inside of parentheses first. Next, there's the E, or excuse, which is calculating the powers and roots inside of the mathematical equation. Next, there's the M, or the D. Which is the my and the dear, which is saying that you should do multiplication and division from left to right. Next, next is the A and the S or the Aunt Sally, which means that you should be performing the addition and subtraction also from left to right. Please excuse my dear Aunt Sally. It's an easy way to remember where to start and in what order to proceed. It's basically the top right quadrant of the data skill matrix or the section with the Easy to learn skills with the most in demand skills. So here is my official data learning order of operations, or simply said the data learning ladder. It's number one to learn Excel. Number two, learn Tableau. Number three, move on to SQL. And four, finally finish with Python. We're starting with Excel because it is by far the easiest to learn and the second most popular tool out there. Then we're moving to Tableau because although less popular than SQL or Python, it's much easier to learn them both. Like I said, drag and drop, click. It's easy. Then move to the most popular data tool, SQL, which is a little bit harder to learn than Excel and Tableau, but still not nearly as hard as something like Python or R. And then we're going to finish with Python. Now you might be wondering, well what happened to R? Or what happened to Power BI? And honestly, Power BI and Tableau are similar enough that if you learn one, learning the other is not going to take you very long. You'll be able to figure it out and a lot of the concepts are quite the same. That's also true with Python and R. They are somewhat similar. Once you learn one of these languages, learning the other language won't be nearly as bad. My suggestion is just to learn one for right now and then pick up the other later. Excel, Tableau, SQL, Python. This gives you the data learning ladder. And to make it easier to remember, I created a phrase or mnemonic that you can repeat in your mind to remind you that this is the fastest way of landing a data job. It is every turtle sprints past. Excel, Tableau, Python. Or if you wish for the more thorough version with Power BI and R included, it is every turtle powerfully sprints past rapidly. When you're not sure what step to take next on your data journey, simply refer to this ladder. I hope this helps, and if this video did help, I'm sure you're going to find this video super helpful as well.