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Hey! Today I'm going to put myself in your shoes and show you what I'd do differently if I was becoming a data analyst like you in 2022. Hi, my name is Tom. I've been working in the data industry for over five years and I'm currently working as a senior data scientist at CareerFoundry. Over the years, I've gained a lot of knowledge and a lot of experience, and I've made my fair share of mistakes. But I also took the journey that you're taking a long time ago. So the journey that I would take now is completely different from the journey that I did take all those years ago. Caveat - there is no one successful way to learn data analytics. Everyone has their own style. Everyone can find their own path to success. I'm just going to be sharing my own personal insights. Hopefully these tips will be useful, but stay tuned to the end of the video. I'll be sharing some free resources that I would find useful if I was starting in a career in data analytics today. Spoiler alert - the whole industry is changing rapidly and evolving day-to-day. Here are some of the ways. Lots more online content. There is so many more people working in data analytics than there was, which means if you go online for support, you're going to find lots of people, like-minded people, interested in studying and learning about the same things as you. And while we're on the subject, don't forget to subscribe to our YouTube channel at CareerFoundry, where you'll receive regular updates on everything related to online data analytics learning. More people working in the field. That means not only more job opportunities, but more people to network and learn with. More sectors using data analytics techniques. Back in the day, data analytics might have been a practice that was limited to certain fields. For example, finance or pharma. These days, more and more sectors are using data analytics techniques, which means you can work in basically any part of the economy you're interested in. Cool, right? More cloud resources to help you manage your data analytics infrastructure, whether it's databases you're talking about or visualization tools.

These days, you don't have to download software locally to your machine. Instead, you can go to the cloud and play around with these tools online. More free machine learning techniques. Although you're not training to be a data scientist, there are still a bunch of machine learning applications online that will help you do your job as a data analyst better. For example, object recognition, computer vision, or natural language processing. And don't forget about the online community. There are sandboxes where you can learn about data analytics and practice data analytics with other like-minded people. So let's expand on this a little and let me share how I became an expert in the field. I took a Master's in computer science and then went on to study data science explicitly. A large part of data science involves the study of data analytics, and I became interested in data analytics as a result of that. Thereafter, I worked on data related projects in a variety of fields, including e-commerce, finance and education. So what would I do differently if I was going to start learning data analytics in 2022? The first thing I would say is less upfront learning and more on the fly learning. There are three main ways to become a data analyst in 2022. You could go to university, you can try and teach yourself, or you can go to an online school. Going to university is great. You're surrounded by lots of people. There's a lot of motivation from your teachers as well as from the other classmates to drive yourself forward. However, it does take a lot of time to get a degree and also it costs a lot of money. Trying to teach yourself is amazing. You have total freedom to decide what you want to learn, what projects you'd like to work on. And also, you're exposed to the latest cutting edge trends in data analytics, machine learning that you might not necessarily get from university.

However, it takes a lot of motivation and dedication. It's easy to get lost in the endless fields of study around data analytics, and as a result, it can be really hard to find your own learning path through this maze of content. And the approach I like the most, the one that I would take if I was starting data analytics in 2022, would be to try an online school. There are a lot of them out there. Some of them are cheap, some of them are indeed free, and some of them are more expensive. Each course will take a different amount of time, and each course will dive to a different level of depth into the field of data analytics. And indeed, each course gives you a different roadmap highlighting some of the skills and techniques that you're going to be learning along the way. And finally, each course has a different amount of human interaction that you get. Some are fully online and automated, and some involve more support from human beings. So make sure you find the right course for you in terms of how much you're able to spend, how much time you have, what sort of things you'd like to learn about, and how much human interaction you need. And of course, how do you find a job after you've learned data analytics? Well, that's one of the big differentiators. If you're self-taught, you're going to have to work it out by yourself. But online schools and universities have career specialist teams. Career specialists normally help you prepare your CV, look at your portfolio of projects and take you through interview tips and tricks so that you are in a great position to pass your interview and get that first important job in data analytics. So what would I do differently if I had my time again to study data analytics in 2022? Well, first things first. I don't think honestly, I would go to university, although I loved the experience. I made lots of great contacts and I loved learning in a physical space together with other people. But on balance, the amount of time and money that it takes to get that degree didn't pay off, with regards to how quickly I could have got a job in the industry if I'd just taken a quick online school course and then got an internship quite quickly.

I've seen a lot of my friends and colleagues go through this route and quite quickly they progressed through the field. I feel like there's nothing better than just getting actual work experience in a company. It's probably the best thing you can have on your CV. So don't waste any time getting that. No matter which approach you take, you're going to want a portfolio of projects at your fingertips for job applications. Most university degrees tend to culminate in a thesis. You can think of this like a very in-depth, very detailed central project for your portfolio. If you're in an online school, many of them help you to create a portfolio of several projects that will be useful in job applications. But if you're self-taught, you're going to have to do the research and build it all by yourself. How many projects should your portfolio have? Well, there's no one right answer, but somewhere between one and three is probably fine. If you have about five that can already be a lot and ten is too many. My recommendation would be start with a core project. Find something that you're passionate about. Spend a lot of time building a really great project around that one passionate theme, and then maybe build a couple of side projects related to that, or indeed look at other topics that you're interested in. If you're struggling for ideas, don't forget the Internet can help you. As the industry is changing from day to day, there are some soft and hard skills that you're going to need to learn in order to start your career in data analytics. Well, I can provide you with a full roadmap of how you can become an expert in data analytics. But I think that's a mistake. In fact, the mistake I made when starting my career was thinking I needed a full understanding of the entire domain before starting to work.

Here's why that's a bad idea. A., It's nearly impossible to retain all that information in your brain. And B., by the time you get round to applying some of the information you've learned, it's probably changed given how quickly the industry is evolving. So instead, I'd like to present a lean, minimal roadmap in order to get you started on the road towards becoming a data analyst. Don't forget, this might not be perfect for you. Everybody's learning path is different. My lean roadmap has these five components. Number one, working with data. In addition to understanding what the main data types that you'll be working with are, you're going to need to learn some basic tasks for trying to extract information and derive conclusions from that data. Basic tasks might include grouping your data, summarizing your data, or cleaning your data. These might sound confusing right now. With a little bit of practice, you'll find that they're not as hot as they initially appear. You can learn all these skills and more by taking CareerFoundry's Free Data Analytics Short Course. Click on the link in the description to sign up. Number two, learning a tool so you can work with all this data. My personal preference is to start with Excel. It's a great basis and allows you to move more easily into SQL and ultimately Python afterwards. Number three, statistics, specifically descriptive statistics. This means learning just enough math to be able to describe your data not just with words, but also mathematically. So things like what's the mean or median of your dataset? What's the distribution of the dataset? Is it skewed in any particular direction? And is this distribution to be expected or is it strange? Number four, visualizing your results and telling a story about your results. So how can you create things like charts and presentations to best communicate your results and also, how do you tell a good story? How do you capture people's interests in order to communicate your findings well? And the last one.

Number five, find an interesting area to perform data analytics on. I suggest you find an area of industry or economy that you're passionate about and try and solve a problem in that area. Trust me, you're going to enjoy working on this a lot more than working on some dry financial or pharma data, and your passion for the project will shine through and the end result is going to be amazing. Well, now you've got an idea of this basic road map. Let's think about how long all of these skills are going to take to acquire. It doesn't make sense for me to break these down into subcomponents, but look at it like this. It's going to take you anywhere from a few months to up to one year in order to really start to feel comfortable with all of the skills in this roadmap. Another thing I would do differently is trying to find my own personal road map. A really helpful part of that process is knowing where you want to be having an objective. So take some time early on in your research into data analytics to think about things like what sort of business do I want to be working in? What sort of sector do I want to be working in? What sort of problems do I want to solve? What skills would I like to have in order to be able to solve those problems? And what sort of people do I want to work around? Define your end objective first and then try and work backwards from that towards a roadmap of what skills you're going to need to learn in order to be able to achieve those objectives. This should naturally lend itself towards the drafting of an initial roadmap. But don't forget, I've also given you a cheat sheet roadmap if you don't want to do that work. Something else I would do is try and have a lot more confidence in myself. In reflection, I was quite scared about starting my career in data, so I did a lot of courses, an awful lot of preparation before I began, but really nothing can prepare you for working in the field of data other than actually starting to work in that field.

So have confidence in yourself, believe in your ability, don't be afraid to make mistakes and just jump into the pool, as they say. When talking about skills, a question I get very often is how much math is actually needed in data analytics? The answer is just enough math to get by. You don't need to be an expert at math to be good at data analytics. You do, however, probably need to be interested in math. Okay. But math doesn't equal algebra. Math doesn't equal formulas. Math is about problem solving, math about finding beauty and generalizability and simplicity. So if you like puzzles, if you like philosophy, if you like problem solving, you're probably going to like math. And that'll be enough for you to get into data analytics. Another extremely important part about being a data analyst is getting comfortable with the tools you're going to be using on the job. As a junior, if you want to start learning in the field, you should look at code to get the data. Start with Excel, maybe move on to SQL and Python. Ways to explain the data. Look at visualization tools like Looker, Tableau Metabase or Power BI. Ways to communicate your findings, get comfortable with storytelling. However, I advocate small learning goals. If you're trying to learn a new code language or a new visualization tool, don't try and learn all at once. Break it down into small steps, learn a step, and then practice it first before moving on. One thing I would do differently if I started data analytics in 2022 is take advantage of all of the free online tools out there. For example, if you're trying to come to terms with SQL, you don't need to download and install SQL locally on your machine. You can play in a sandbox online. Or visualization tools - if you're trying out a new visualization tool, don't forget to take advantage of the freemium versions out there. So try something like Tableau for free.

One piece of advice I could give you is don't forget to study other people's work. When I was starting out in the field, there wasn't the same breath of content online that there is now. So don't forget to check out other data analysts and what they're doing on platforms like YouTube. Really easy for an intro into the field. Don't forget to check out GitHub if you're already comfortable with code. There's so much great content on there or go to hackathons and meetups and speak to people face to face. Another thing I do differently if I was starting again would be network. One of the best things I enjoy about my work in data is having a great network of people who work in the same field as me. So when I started, again, I was a little bit apprehensive. I wasn't sure that my skills were strong enough, and I was sometimes afraid to reach out to other people in the data world. But these days, there's nothing I love more than getting a connection from someone who's just starting out in the field. I encourage you to do the same thing. Find people you respect on LinkedIn, reach out to them and connect with them. Or check out some of the online communities. There are some great subreddits out there and there's also Discord. As promised, during the video, here are some resources where you can find out more about getting to grips with data analytics. Why don't you look at Medium.com? This is a website that I use all the time to find out the latest and greatest in machine learning data science and data analytics. They have lots of great articles for you to look at. If you want to take some actual real world data sets - try kaggle.com. It's packed with open source datasets where you can try your data analytics techniques out on real world data. If you're interested in coding related challenges around data, try a platform like HackerRank. They are packed full of SQL coding challenges and general coding challenges to really ramp up your coding skills. And finally, the platform we all love - YouTube.

Filled with great content about data analytics, data science, machine learning and everything else you want to learn about. Some of the content is even created by our own in-house CareerFoundry team. And of course, as mentioned before, we have our own online free short course on data analytics and you can find the details in the description below. Well, by the time you've done all the things I've mentioned in this video, and if you spend 6 to 12 months working hard at it, you should be able to find a junior data analyst role with some determination and patience. Good luck! Hope this video was helpful. And so if you like what you see, don't forget to subscribe to our channel for more great data related content. I've actually made another video recently where I went over a typical day in the life of a data analyst. We talk about some of the responsibilities you'll face and some of the skills you'll need and just generally what your day is going to look like. So if you'd like to see what the job entails in more detail, I'm being told that there's a thumbnail that you should click on right here. I hope it's there... Alright, I'm going to be back soon with another great video about data, but that's everything for me for today. See ya!.