From Civil Engineering to Data Science: My Journey & Lessons Learned

Mrunali Jaywant Pawar
MSc Applied Data Science

If you had told me a few years ago that I’d go from pouring concrete to cleaning datasets, I probably would’ve laughed. I started out as a Civil Engineering student, focused on building roads, designing structures, and dreaming of real-world infrastructure projects. It was a solid, practical field — one that taught me discipline, logic, and how to break down big problems into manageable parts.

But somewhere along the way, I discovered something unexpected — the power of data. Today, I’m pursuing a Master’s in Applied Data Science in the UK, and I’ve found a new kind of passion: transforming raw data into insights that can drive decisions, tell stories, and make a difference. This post is about that journey the shift, the struggles, the small wins and the lessons I’ve learned along the way.

Step 1: My Civil Engineering Roots

My journey began in Civil Engineering — a field where everything is physical, measurable, and grounded in the real world. From structural design to environmental studies, I developed a mindset that was both analytical and solution-oriented.

While I enjoyed the challenges, I also began noticing something curious: decisions on-site were often driven by assumptions or outdated reports. I wondered, “What if we could use data to make smarter, faster decisions?” That question lingered in my mind.

Step 2: The Spark That Changed Everything

My first real exposure to data science came through online videos and beginner-friendly articles. I was fascinated by how data could be used to predict trends, detect patterns, and improve efficiency whether in healthcare, transportation, or even construction.

I signed up for an introductory data analytics course out of curiosity. The first few weeks were overwhelming terms like “correlation,” “pivot tables,” “data types,” and “visualizations” felt like a whole new language. But I kept going.

And soon, I was hooked.

Step 3: Learning, One Tool at a Time

Determined to grow, I enrolled in a structured 12-month Data Analytics program that covered:

– Microsoft Excel: where I learned the basics of data cleaning and formulas
– Power BI: my first experience with dashboards and data storytelling
– SQL: the magic of querying databases
– Python: where I learned how to automate and explore data deeply

I also worked on small but meaningful projects:

– A Bonus Calculator in Excel for HR teams
– A COVID-19 US Dashboard using Power BI to show trends and hotspots
– A Sales Performance Report with insights that could actually drive business actions

Each project boosted my confidence and made me realize that data is more than numbers — it’s about solving problems.

Step 4: Taking the Leap to a Master’s in the UK

I wanted to take my learning further and gain global exposure, so I applied to study Applied Data Science at the University of Essex.

Here, I’ve been diving deeper into:

– Data modeling and statistics
– Machine learning fundamentals
– Scientific communication
– Real-world datasets across domains like sustainability, health, and economics

I’ve collaborated with peers from different academic backgrounds, worked on capstone projects, and learned how to present data in a way that even non-technical people can understand. It’s been challenging, but incredibly fulfilling.

Step 5: What I’ve Learned (So Far)

This journey has taught me a lot, not just technically, but personally too.

– Your past isn’t a waste My civil engineering mindset (structured thinking, planning, problem-solving) has actually helped me in data science.
– Consistency beats perfection It’s okay to not understand everything on Day 1. What matters is showing up every day to learn a little more.
– Communication matters You can have the best analysis, but if you can’t explain it clearly, it loses value.
– Imposter syndrome is normal Everyone feels behind sometimes. The key is to keep learning and
helping others along the way.

Step 6: What’s Next?

As I continue to grow, I want to:

– Work as a Data Analyst or Business Analyst, turning messy datasets into clear, actionable insights
– Combine my civil engineering background with data science to contribute to smart cities or environmental sustainability projects
– Share what I learn through writing, mentorship, or open-source projects, especially for students
from non-traditional backgrounds who are curious about data

Final Thoughts

Switching from Civil Engineering to Data Science wasn’t easy. It meant stepping out of my comfort zone, learning a whole new set of tools, and constantly reminding myself that it’s okay to be a beginner again.

But every step was worth it.

If you’re reading this and wondering whether you’re “too late” or “too different” to explore a new path, here’s what I’ll say: You’re not starting from scratch you’re starting from experience.

And that experience can be your greatest strength.