I Wish I Knew THIS When Starting as a Data Analyst

7 Things I Wish I Knew When Starting as a Data Analyst

September 03, 2026

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I've been doing data analytics for 10+ years now. Here's the 7 things I wish I knew when I was first getting started.

Stop searching "data analyst" exclusively

This was my biggest mistake early on and I really regretted it.

I only looked at jobs with "data analyst" in the title. Completely ignored financial analyst, business analyst, healthcare analyst, operations analyst, data visualization specialist, business intelligence engineer — all of them.

Here's the thing: those jobs have the exact same responsibilities and requirements as data analyst roles. Same work. Different fancy title based on industry or company.

If you'd be stoked with a data analyst job, you'd be stoked with any of these. Don't limit yourself! There's lots of options.

Learn three tools and stop

There are literally thousands of data tools you could learn. Excel, SQL, Python, Power BI, Tableau, R, AWS, SAS, JMP, Qlik, Google Data Studio, Looker — I could keep going.

When I was starting out, I tried to learn a bunch at once. And that's overwhelming. You don't get good at any of them. It takes forever. And then you eventually give up.

So don't learn them all.

Learn the lowest hanging fruits. The ones that are easiest to learn and most in demand. That ends up being Excel, SQL, and a BI tool like Tableau or Power BI.

Use your past career as a weapon

Whatever you studied in college or did before data — that's not a waste. That's your superpower.

You might be thinking your background as a teacher or accountant or whatever has nothing to do with data. But you are wrong!

Your domain knowledge is really useful and really powerful. If you combine your domain plus data, you're gonna be a superhero. You're gonna be able to analyze things that most data analysts wouldn't be able to do just because you understand the business and the industry.

Your domain is your strength. Not your weakness.

Actually learn SQL (I didn't)

When I first got into data analytics I thought Python was everything. Everyone's like "Oh Python, it's so cool, Python's the new tool, everyone's using Python."

Python's great. I love Python. It's my favorite data tool.

But SQL is the most used data tool on planet Earth. Data analysts use Excel more, but data scientists and data engineers use SQL way more than Excel.

Here's the embarrassing part. I went through my whole first data job without ever using SQL. Some jobs don't require it, but I wish I would've used it at that job because it would've managed our data better and faster.

It's just the best way to organize and query your data. Worth learning it :)

Start building your network today

I know this sounds awkward and like a lot of work and putting yourself in difficult situations. But if you wait until you actually need a network, you've waited too long.

Your personal brand and networking really matter when you're trying to land a job. Especially now where applicant tracking systems have so many applicants it's really hard to stand out.

If you actually have a human-human interaction or someone knows your name and your face, you're so much more likely to get what you want.

You can start small. You don't have to start big.

Accept that you'll never know everything

The imposter syndrome you're feeling right now as an aspiring data analyst? That never goes away.

It never does.

It's so hard to feel like you know anything in data because one, it's constantly changing, and two, it's immensely vast. It's impossible to know everything.

The earlier you become comfortable living in the idea of "I don't know this, but I know I can learn this," the better.

Every job I've ever had has given me the opportunity to learn on the job and given me time to actually get paid to learn.

That's the best way to learn data analytics. Get paid to learn.

Win-win-win.

Lower your expectations about remote work

I hate to be the bearer of bad news but landing a remote job is a lot harder than you think.

Out of all the data jobs in the United States, probably about 14% are remote. That means 86% are either hybrid or in person.

Everyone wants to work remotely. But there's only 14% of opportunities to work remotely. That just makes it hard to land a remote job.

Consider hybrid instead. Often nearly as good as remote and a lot less competitive.

Breaking into data in 2026 is HARD!

The truth is, when you're getting started in data, there's just a lot you don't know. The best roadmap is unclear. You're not 100% sure what to do, who to trust, where to go, etc. I hope you found these tips helpful.

If you want even more tips, a clearer path, and more hands-on mentorship, consider joining our next cohort for The Accelerator.

Imagine this list, but way more detailed and expanded. Plus if you have any questions, we have 5 different ways to respond to you (special ai, comments, community, live events, email).

It's the program I wish I had when I was getting started. Learn more.