From DNA to Databases: My Pivot Into Data Analytics

Picture this,

You’ve just graduated with a Master’s in Molecular Biology (Bioinformatics track) from one of the oldest universities in the world, (shoutout to the University of Padova).

You feel accomplished. You’ve survived exams, language barriers, deadlines, and at least three mental breakdowns.

Life is looking promising.

So you start applying for jobs. And then comes the familiar line we all know too well, “Sorry, we’re looking for someone with more experience.”

Ah yes. The classic entry-level role that requires experience from your previous life as a senior data scientist in 2012. Yep, that makes total sense! But how do you get experience if no-one is willing to give you a chance to begin with?

I’m Zimbabwean, which also means this journey has never just been about me. It’s about being thousands of miles away from my family, working toward something bigger, stability, opportunity, and the ability to support the people who’ve supported me my entire life. So when the traditional route started feeling closed off, I had a choice to make.

Break down dramatically or pivot aggressively.

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I chose both actually, but ultimately I chose pivoting. With feelings. Because I have to make it into the job world come hail come thunder!

Data Analysis?

I’ve always liked working with data. During my studies I actually enjoyed the analysis side of things but I never really saw myself stepping into data analytics properly. It always felt like there was a checklist of “must-have” tools I didn’t fully own yet: Power BI, SQL, Python, Excel, you name it, the usual suspects you see everywhere on YouTube job guides. Not to mention, the list of requirements kept on updating every two weeks!

So naturally, I assumed I was disqualified from it already. But then I did what every confused graduate does,I Googled. And I discovered that these skills aren’t out of reach, they’re learnable and my background does not have to limit me to a role in the lab (which I’m not particularly fond of anyways). So I started thinking, maybe I could potentially use my background to my advantage. I’ve always been close to the life sciences, so it feels like a natural direction, working with data from hospitals, patient records, and health systems. There’s something exciting about the idea of working with data that actually comes from real people and real systems.

So where do you even start when you’ve decided to pivot into data? For me, it started with YouTube tutorials, (Alex the Analyst deserves an honourable mention), the Google Data Analytics Professional Certificate on Coursera, getting onto Databricks to actually start practising.

Enter: Databricks (and immediate confusion)

My first look at Databricks, clean interface, big possibilities, and a quiet sense that I might be slightly out of my depth.

Databricks is a platform for data analytics, big data, and machine learning, where you can store and work with large datasetss, write queries using SQL and use Python for analysis and machine learning all in one place. It sounds amazing in theory, but in practice, it kind of feels like being given a spaceship dashboard and being told “just explore.”

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I chose it because I didn’t just want to learn theory, I wanted to actually practise on real data and start building practical skills. Modern platforms like Databricks are changing the way that data is handled, making it possible to analyse it at scale.

This week marks the beginning of my Databricks SQL journey. I set up my Community Edition account and spent time exploring the workspace clicking through catalogs, schemas, and the SQL editor like I absolutely knew what I was doing (I did not). But honestly, I’ve already learned something important, understanding the environment matters just as much as writing the query. It’s a bit like arriving in a new city, first you figure out where you are, how things work, and how to get around. Then you start moving with confidence.

The workspace, where everything lives. This is where I created my first notebook and started exploring datasets. (Bakehouse Sales Starter Space is a fictional dataset I found already created by Databricks).

The part I’m actually excited about

What I’m really looking forward to is working with real datasets especially (but not limited to) healthcare and biology, where the data is large, complex, meaningful, and occasionally humbling. Coming from bioinformatics, I already know how powerful data can be when it’s used well. But now I want to go a step further; not just processing data, but extracting insights that matter.

The SQL editor where you write and run queries. My home for the next few weeks.

Over the next few weeks, I’ll be documenting my progress as I:

• Load and explore datasets (without breaking anything hopefully)
• Write SQL queries that actually return results
• Build small data analysis projects

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My goal isn’t just to learn SQL but to actually use it and build practical, job-ready skills while sharing the process along the way and ultimately land a job.

If you’re also starting out in data analytics or transitioning from another field, I’d genuinely love to connect and share the journey.

We’re all just trying to make sense of data one query at a time.