If I Had to Start Over in Data Analytics: What I'd Do Differently

I've been an analyst since 2004. If I had to restart my analytics career from scratch today, here's exactly what I'd do differently.

Not what I wish I'd done—what I'd actually do based on what I now know matters most.

1. Focus on SQL First (Not Excel)

Don't get me wrong—Excel is valuable. But if I could only learn one technical skill first, it would be SQL.

Why SQL matters more:

Most data lives in databases, not spreadsheets
Every analytics job requires it
Interview technical screens heavily focus on it
It scales better than Excel for real-world work

I'd spend my first 3 months getting genuinely strong at SQL before touching anything else.

Strong means: comfortable with joins, aggregations, window functions, CTEs, and subqueries.

2. Build Real Projects, Not Tutorial Projects

Tutorial projects are fine for learning syntax. But they don't impress hiring managers because everyone following the same course has the same project.

Instead, I'd find datasets that genuinely interest me:

Love sports? Analyze team performance data
Interested in housing? Look at real estate trends
Care about environment? Explore climate datasets

The passion shows through in interviews. And you'll remember more because you actually care about the answers.

One authentic project beats ten cookie-cutter tutorial follow-alongs.

3. Learn Business Before Mastering Tools

Technical skills get you interviews. Business understanding gets you hired and promoted.

I'd spend time understanding how businesses actually work:

How do companies make money?
What metrics actually matter and why?
How do sales, marketing, product, and finance connect?
What questions keep executives up at night?

You can always learn a new tool. Understanding business context takes years. Start early.

Read business books. Follow earnings calls. Ask "why does this metric matter?" until you really understand.

4. Network From Day One (Not When I Need a Job)

I was too focused on building skills and not enough on building relationships.

The reality: The best opportunities come through people, not job boards.

Three of my roles came through personal connections. Zero came from cold applications.

If I started over, I'd:

Attend local analytics meetups from month one
Engage genuinely on LinkedIn (comment on posts, share learnings)
Have coffee chats with working analysts
Join online analytics communities

Networking isn't something you start when you need a job. It's something you maintain always.

5. Get Comfortable With Imperfect Data

Courses give you clean, polished datasets. Real work gives you chaos.

I'd practice with raw, messy data:

Kaggle has thousands of real-world datasets
Government open data portals (data.gov, etc.)
Company financial filings from EDGAR

Learn to wrangle missing values, inconsistent formats, and conflicting sources. The ability to turn chaos into insight is what separates good analysts from great ones.

6. Start Applying at 70%, Not 100%

I waited too long to apply for jobs. I thought I needed to be "ready."

Here's the truth: You'll never feel ready.

When you're 70% of the way through learning the fundamentals, start applying. Job searches take time anyway. Plus, interviews will reveal what skills actually matter—often different from what you thought.

Don't wait for perfection. Get in the game.

The Biggest Lesson

If I could go back and tell my younger self one thing, it would be this:

The learning accelerates when you're doing the work, not preparing to do the work.

Stop waiting until you feel ready. Build projects. Apply for roles. Network with practitioners. Get uncomfortable.

That's where the real growth happens.

Common Questions About Starting a Data Analytics Career

Q: How long does it realistically take to get job-ready?

6-12 months of focused, deliberate practice if you're starting from zero. Could be faster if you have transferable skills (Excel experience, business knowledge, etc.). Don't trust anyone who promises 8 weeks.

Q: Do I need a degree to break into analytics?

Not necessarily, but it helps. What you absolutely need: demonstrable skills (portfolio projects), ability to communicate findings, and enough business sense to be useful. A degree is one path—not the only path.

Q: Should I learn Python or R first?

Python. More job postings require it, and it's more versatile beyond analytics. R is great for statistics, but Python opens more doors.