Speaker 1: From the day I started messing around with some data in Excel, it took me about three years to land a data analyst job at Heineken and another two years to land a job as a freelance data analyst at a big bank in Europe. And another two years to quit that job and travel the world full time, but more on that later. Can you become a data analyst way faster? 100%. I've wasted too many hours watching YouTube tutorials and I've spent money on Udemy courses that didn't add any value. So I often ask myself, if I could start over with data analysis, knowing what I know today, how would I go about it? And that's exactly what this video is about. I'll remove all the fluff and give you the fastest path to go from zero to a full-time data analyst. And throughout this video, I will share the three major mistakes almost every data analyst makes. So stay tuned for those. The first thing every beginner data analyst needs to pick is a tool or programming language for data analysis. I personally started off with Excel. Is that the best tool there is for data analysis? No. If I could go back in time, would I have started with something else? Also no. Because apparently getting good at Excel was enough for my first data analysis job. And I personally recommend every single data analyst to start off with Excel. It's easy to learn, used in almost every company and it has a wide range of applications for data analysis. But this is also its pitfall. Although you can do a lot of different things in Excel, it's not designed specifically for data analysis. Meaning it has its limitations. You want to work with a very large data set? Well, good luck not punching through your computer screen after Excel crashes for the 27th time. Excel is perfect for starting out. But once you've mastered Excel, it's time to move on to the more serious tools. And the most logical tool to learn next is SQL. Next to Excel, it is apparently the second most requested skill in data analyst job openings. Huge thanks to Luke Burroughs for actually scraping a lot of LinkedIn job openings to come up with these facts. You're a legend man. Thank you. What up there nerds. And the reason SQL is so in demand is because it doesn't have the same limitations as Excel. Why is my accent suddenly changing to silent? I don't know what. With SQL you can extract, transform and load very large data sets. But the best thing about it is that SQL has its own very easy to use programming language. It's a great tool to add to your skill set as a data analyst while also being a great stepping stone to some more serious programming. But before we get to that, every data analyst needs a visualization tool. There are dozens of BI tools out there. But having worked for multiple companies and projects and having looked at probably hundreds of data analyst job openings, I've come to realize that the majority of companies are looking for people with experience with Tableau, Power BI and ClickView. Here's the pros and cons of each so you can decide which suits you best. Power BI is part of the Microsoft stack so it works smoothly with Excel and SharePoint. Besides that, it has a free version and even the paid versions are relatively budget friendly compared to the other BI tools. Next up is Tableau. Tableau is a BI tool with more extensive data visualization capabilities than Power BI. It seems to be just a bit more in demand in the job market than Power BI. It comes with a much higher price tag though, making it harder to learn if you're starting out by yourself. And then there's ClickView. Using in-memory technology, making it a super fast and responsive way of doing business intelligence. But also a pretty high price tag and also it's less in demand than Tableau and Power BI. I personally pick Power BI as my BI tool. Although it might not be the fastest BI tool out there or the one with the most extensive capabilities, in my opinion it can give you the most bang for buck when it comes to becoming a full-time data analyst. Before we move on to how to actually get good at data analysis and land that first job, there's one more thing we need to do as a data analyst. After you've mastered a BI tool, it's time to get into the more advanced analytics. I'm talking about which programming language to use when it comes to data analysis or data science. Software developers and programmers get to choose between Java, JavaScript, Ruby, C Sharp, Python, R, C, C++ and many more. Luckily for us data analysts, we only have to choose between two programming languages, R and Python. So which one should you pick? I chose Python, but if I could go back in time, I would choose Python. Why? Well, even though they're actually very similar, R is a programming language focused more on statistical analysis, while Python is a more general purpose programming language that also happens to be very good for data analysis. And although R might be a little bit easier to learn, I would still go for Python as it's probably the number one programming language in the world. Meaning, if you get good at Python for data analysis, it might also open the door to a lot of different job opportunities. So the path I took is Excel, SQL, Power BI and Python. This is the path I would recommend as it worked out great for me. But you can start off with any language or tool that suits you best, because whatever you pick, your first language or tool will definitely not be your last. So now that you know which tools to learn, let's talk about how to actually learn it. And this is where beginners make their first major mistake. The mistake most beginners make is that they try to learn by watching others. This is how most people learn data analysis. Some people go to Udemy and watch multiple 20 plus hour courses and probably not even finish all of them. Or they watch YouTube videos watching other people analyze data. But without actually writing code or analyzing data themselves, they give themselves a false sense of progress. Because analyzing data in your head is very different from actually doing it. Data analysis in your head is very different from actually analyzing data, stumbling upon faulty data and debugging for hours. So what is the right way to do it? The answer is very simple. Learn by doing. You just need to get the reps in and start coding and analyzing data by yourself. For Excel, I would recommend a website called ExcelPracticeOnline.com, a free website where you learn to use Excel by doing. From the basics to the most advanced functionalities. For SQL, I would recommend the website W3Schools.com slash SQL. For Power BI, I would recommend Data Camp. There's free courses and also paid ones. For Python, you can go to this free website called LearnPython.org. Whether you're practicing every day at your current job so you can make that switch to a data analysis career or whether you're still studying, you need to practice every single day. Only this way will you experience what it's actually like working as a data analyst. Analysis? Working as a data analyst. Only then will you learn what it's like to debug your own code or formulas. Because to be honest, that's what you will spend the most time on. Debugging faulty code or correcting faulty data. Being a data analyst is awesome. But this is also the second major mistake most beginners make. They try to solve every problem themselves. The fact is that error you just got in Power BI, there's a very big chance someone already has experienced exactly the same error as you. And that's good news for you because now you don't have to look for the cause of the error. You just have to copy part of the error message in Google and you'll probably end up on some Stack Overflow site where someone explains exactly how to solve this error. Let's be honest, copying code from Stack Overflow is like 80% of any developer, programmer or data analyst's job. Data analyst is awesome. Okay, now that you know what tool and programming language to pick and how to actually learn it, it's time to build your portfolio. And this will help you build out an attractive CV for recruiters on LinkedIn. Recruiters? I always have talked a little bit like this. I'm a programmer. So programming language and recruiters talk to me every day. Now if you have a job where you can apply data analytics already, then do so right away. If you don't really do anything data analytics related, like sales for example, then try to build a report or a dashboard showing some key metrics or KPIs that you can show to your manager. He will appreciate it and you will get to learn your data analysis skills. And if you can't apply to your current job or if you're still studying, then start a project on your own at home. Because in the end it's about getting that data analyst job. And the best way to do that is as follows. Pimp up your LinkedIn profile. I've made a video before where I gave my top five LinkedIn profile tips to attract recruiters on LinkedIn. And I'll summarize it here for you. Make sure you put data analyst in as many places as you can in your profile. LinkedIn is a search engine and if recruiters search for data analyst, you want to be found. You want to be on top of that list. So make sure your header contains the word data analyst. In your work experience, if you've done something data analyst related, put in the keyword data analyst. Once your LinkedIn profile is set up, there's two things you can do. Start applying to every single data analyst job you can find on LinkedIn or any other job platform. Or the second way, which I prefer, is if you've set up your LinkedIn profile correctly, then recruiters will soon start hunting you. They will invite you to job interviews for data analyst positions. Which brings me to major mistake number three. Don't quit. Well, do quit. Let me explain. You see, the second best decision I have ever made in my life was becoming a data analyst. And to become one yourself, the only thing you have to do is don't quit. Whether it will take you one week, one month, or one year, if you keep applying, if you keep improving your skills, updating your CV, you will land that job eventually. And trust me, once you do land that first data analyst job, a world of opportunities will open up for you. You can grow your skills into a senior data analyst or become a freelance data analyst. Or what I eventually did, quit my job as a data analyst. The single best decision in my life was quitting my job as a data analyst. You see, becoming a freelance data analyst gave me the resources to quit my job and pursue a completely different career, creating my own brand while traveling the world. Do not subscribe to my YouTube channel. I'm 100% serious. If you're looking for advice and tips on how to become a data analyst and how to build a data analyst career for the rest of your life, then I would recommend subscribing to Luke Perus, as he makes way better videos than me. But if you view being a data analyst as a stepping stone to creating a life you want, your dream life, whatever that might be for you, then you might want to subscribe as I share my personal lessons on this journey from a 9 to 5 to a dream life. Watch this video next to learn what made me quit my six-figure job to start YouTube. Cheers.