If you’re here because you want to know how to gain the skills needed for a data analyst job in the fastest possible way, and you also want to learn how to create a job application strategy that will actually lend you your next role, then you’re in the right place. I will break down this article into three sections. I’ll cover technical skills, data portfolios, and job application strategies in a way that you can implement actionable next steps.
Data analysis, just like anything else, is so much easier to learn and get really good at if you have strong foundations. And by strong foundations, I mean, please master the core technical skills.
Focus on tools and techniques that are widely used in the industry. Start with Excel and SQL. Learn how to clean, manipulate, and analyze data using Excel formulas, pivot tables, and pivot charts.
If you can create an interactive dashboard in Excel, I’d say you’re a pretty advanced user. Then move on to SQL for querying databases. Which SQL dialect you learn doesn’t matter that much in my opinion, as they are very similar. But if I really had to pick one amongst the most popular ones, like PostgreSQL, MySQL, or Microsoft SQL Server, my bet would be on PostgreSQL.
Next, learn how to create actionable insights using a data visualization tool. There is absolutely no need, in my opinion, to master both Tableau and Power BI. If I were you, I’d just learn one and learn that very well, because the two tools are fundamentally different and what you learn with one tool is not easily transferable to the other. An easy way to pick which one you should learn would be to look at what the majority of the companies where you want to work at use. If that’s Tableau, please focus on Tableau. If that’s Power BI, please learn Power BI.
Next up, learn a programming language. For data analysis, my preference has always been Python over other popular languages like R. Python gives you versatility. Libraries like Pandas, NumPy, Matplotlib, or Seaborn are great for cleaning, manipulating, analyzing, and visualizing data. If I had to learn and develop the skills again from scratch as a future data analyst, I’d go straight to the data analyst in Python career track on Datacamp. It’s packed with bite-sized hands-on learning that actually sticks, and it’s fun too.
This track has nine courses and seven hands-on projects that cover everything from the basics to advanced data analysis techniques. I’ve been a Datacamp user for many, many years now, and one of the things I love most about the platform is that the tools you need are already given to you. You don’t need to download Python or worry about setting up environments.
Everything is easily accessible from within your browser. To take it to the next level, I really recommend passing the data analyst certification by Datacamp. This is an industry-recognized certification, and it’ll get you professional credibility so you have something to prove that you can actually do what you say you can do.
And here’s how. I’ve reviewed hundreds of portfolios, and there are three main reasons why they get ignored. One, no one can tell what you built.
Two, there is no indication of what tool you used and why. And three, there are no clear next steps. Your portfolio should showcase projects that solve real-world problems using data analysis.
So if your portfolio is being ignored left, right, and centered by hiring managers and recruiters, do this. Create a walkthrough video where you walk your audience through what your portfolio contains. This is exactly what I did in my own ultimate data portfolio.
I simply used a loom recording. Keep the video short and make your messaging clear. You can also test your portfolio with the simple method.
Find a non-data friend, and by non-data I mean someone who genuinely doesn’t have a clue about data analysis. Show them your portfolio and ask them if they understood what they saw in your portfolio. If the answer is yes, great.
Job well done. Your portfolio is easy to understand. If the answer is no, please go away and work on your presentation and messaging.
Remember this, no one wants to see 300 lines of code with no context or explanation. That’s not a portfolio, that’s just code. Once you’ve acquired your skills and built your portfolio, focus on finding your next opportunity effectively.
And to do this, you’ll need to strategize your job search. Here are some actionable steps that you can take right now. Have one generic resume, but please do not use this for all applications.
Tailor this resume to the specific jobs you’re applying to. If the data analyst role you found mentions SQL or Tableau in the job description, make sure you tailor your resume to include these skills and provide relevant past experiences where you demonstrated how you use these tools successfully. Having these keywords more often in your resume, in this case SQL or Tableau, will increase your chances of your resume progressing onto the next round of the application.
The ATS will more likely pick up these keywords as you have them spread throughout your resume. And if it’s an actual human recruiter looking at your resume, you also give yourself the best chance by making it super easy for the recruiters to find those skills in your resume fast. When it comes to applications, you cannot improve what you don’t measure.
Most job seekers don’t track their application process. They apply, wait, and hope. But hope is not a strategy, is it? You could do some simple A-B testing with your resumes.
Apply to 10 to 20 jobs with resume A, then change one section, summary, skills, or work experience, and apply to another 10 to 20 jobs with resume B. Compare the results, then create your tracker in just five minutes using Google Sheets or Excel and track the roles you apply to. Log your ghosted applications, rejections, and interview calls. Find patterns.
You might say this is too granular and overwhelming, but if you don’t know your application to interview rate, maybe analytics isn’t for you. Watching another tutorial won’t get you hired. Having a clear job application strategy will.
I really hope you found this article useful. I’m a big believer in learning by doing, so I highly encourage you to go away and implement at least one action I highlighted today. Even better, make yourself publicly accountable and put that action in the comment section below.
Thanks so much for taking just a little time out of your day to read this, and I shall see you in the next one.