Speaker 1: Here's how I'd become a data analyst if I had to start over. As a data analyst now with over 10 years experience, I have many lessons learned and would take a completely different path. Anyone, yes you, can be a data analyst all within six months of self-study. Here's how. By failing to prepare, you are preparing to fail, says Benjamin Franklin, and without a plan, you will get lost in the data analyst journey. So here is a six-month roadmap that I personally would use. I'd split up the roadmap into three different parts, the skills, the projects, and the job applications, and put a date on the calendar six months from now and work backwards from it. So in months one to three, I will work on skills. In month four, I would work on projects. And then month five and six is the job application process. And six months is very reasonable if you have about three to four hours to study every day. And everybody has different schedules and different commitments, but if that's something that you can commit to, this roadmap would work for you. I also have a video on how I was able to self-study for four hours every day, and if you're looking for tips there, I will link the video above. Months one through three is skills. Knowing what I know today, I would really just focus on learning three tools, and that is Excel, SQL, and Tableau. I would learn it in that order and just focus on just those three for three months. I'm not including Python, cloud technology, other BI tools, because most entry-level data analyst roles will only require you to know those three skills that I listed. And from my personal experience as well, those were the only three tools I used for years, so that is enough to get you started and you can worry about learning the other skills after you land the job. Excel is the first tool that I would learn because Excel is still the most popular BI tool used across industries, and it's highly likely that in your first role as a data analyst, you will be using a lot of Excel. You need to know how to clean, analyze, and present data using Excel. And what level do you need? About an intermediate level where you know key functions like VLOOKUP, pivot tables, how to create visualizations. And what I would do in my roadmap is break it down into smaller pieces. So spend a week learning just the formulas and getting used to them, and then spend the following week on visualizations and thinking through how you can visualize data using charts and graphs. The best way to learn is by doing. Rather than moving on to SQL as soon as you finish Excel, take the time to practice it. What I would do is take my bank statement and try to do some analysis. You can try answering questions like, what does the trend of my spending look like? And this would require taking all your spending and putting it on a line graph where you can see the trend of the increases and decreases of spending over time. And then you can try bucketing your spending into categories like entertainment, food, and chart those categories over time to see how your spending changes by category over time. And this is the type of analysis that a data analyst would do. Next is SQL. SQL is fundamental to be a data analyst. If you have a technical assessment for your data analyst job, it will 100% be in SQL. Data is stored in databases, and SQL is a language that you use to talk to databases to get the information that you need. SQL is a more powerful tool than Excel because it allows you to work with really large data sets and also be able to combine them to easily create different analysis. You only need to be an intermediate level SQL user, note I didn't say advanced, and that includes functions like select, where, group by, having joins, and window functions. Some websites I personally use to learn SQL are Datacamp, Udemy, LinkedIn Learning. Datacamp was extremely useful because it allowed me to interactively learn SQL hands-on without having to download anything, as well as LinkedIn Learning. If you are in the US, a lot of public libraries actually offer a free subscription so I was able to do a lot of my learning for free through LinkedIn Learning. And lastly, don't forget YouTube because there's a lot of really good free tutorials on YouTube that you should use as well. Next, we move on to Tableau. Now, any BI visualization tool can be used in place of Tableau, such as Power BI or Looker or QuickSight, but I personally would learn Tableau because it is the most commonly used tool that you'll see on job listings. Also, from my experience, when you start learning just one BI tool, you know about 80% of others, so it's really not as important which one you pick here. So what should you know in Tableau? You should be about an intermediate user that knows how to connect to data, add multiple data sources, as well as creating visualizations with filters will get you pretty far. There's also a free version of Tableau that you can download and start practicing, and you can even take the same bank statement data that you had earlier and use that to create some of the same visualizations to get practice. Month four is projects. This is a step that I wouldn't skip because without any data work experience, I don't have anything to show recruiters that I know how to do data analysis. So knowing that the purpose of these projects is to use on my resume and interviews, I would strategically use them to make myself stand out. Here are three tips for your projects. Tip one, I'd create three to four projects that shows a combination of my skills. So I wouldn't create a project in just Excel and a project in just SQL. I would do a combination of Excel and SQL or SQL in Tableau, and this shows the recruiter that I know which tools to use to solve which problems. Tip number two, I create projects that have an analysis that solves a problem and tells a story. Data analysts always start their process first with the problem and it's no different for a project. A common mistake is to start with the data set first with no plan and then work aimlessly without knowing what it is that you're going to do. It's like when I'm looking at the bank statement data and without a problem statement, I would just take it and create all sorts of fancy graphs that look really good, but don't mean anything. It's only when I have the problem statement of what does a trend of my spending look like that I actually have a direction and can sit down and think about, well, how would I best show this through a visualization and tell a story? Tip number three, use free resources for data such as Kaggle, Reddit, data.gov, and even Coursera does have guided projects that you can pay for. Months five through six is the job application process. I would prepare for interviews by updating my resume, my LinkedIn, and applying for data analyst jobs. And I do have a video on how to create a data analyst resume with actionable tips, which I'll leave up there for you to watch. While applying for jobs, I would start practicing technical questions. Pretty much every single data analyst job that I've applied to has required a SQL technical assessment along with the general interview. And I didn't know this and was completely unprepared, but you know better. So in month five, give yourself enough time to take some technical tests so that when it comes time for the actual interview, you'll be ready. I've personally used LeetCode, HackerRank for SQL interview questions, and they were actually pretty accurate to the ones that I've gotten in real life. So try and do the medium, easy questions. And if you can pass those, then you'll be prepared. With this roadmap, I would start and then learn, not learn and then start. I hope this is the video that starts you on your data analyst journey. And if you're interested in learning more about the soft skills a data analyst uses, watch how to get ahead of 99% of data analysts, and I will see you there.