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Connecting the Dots: 5 Lessons from a Year in Data

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Connecting the Dots: 5 Lessons from a Year in Data

By Gauri Venkatachalapathi

After years working in software testing and a short stint in analytics back in India, moving to the UK for my masters felt like venturing into unfamiliar waters. Every decision - from choosing courses and adjusting to new environments, to preparing for job applications, and stepping into interviews - felt enormous and overwhelming… yet necessary.

On my first day of the Data Scientist Development Programme at Leeds Institute for Data Analytics, as I walked into a room full of talented peers, I wondered, “Do I really belong here?”.

That moment reminded me of what Martin Luther King Jr. said:

Faith is taking the first step even when you don’t see the whole staircase.

In those early weeks, I often felt like I was scrambling to keep up. I spent hours debugging code, managing new datasets, interpreting charts and figuring out unfamiliar software - sometimes feeling lost. And yet, there were moments that surprised me: presenting work that sparked a discussion, finally getting a query to work, connecting with peers who shared their own stories of struggle. Each small success reminded me that progress was not about taking giant leaps - it was about consistent steps forward. And as the year went on, I started noticing how those tiny victories stacked up, shaping not just my skills, but also my perspective.

Starting something new, especially in a field as extensive as data, forces you to confront not only what you do not know, but also what you thought you did. How do you keep going when the learning curve feels endless? How do you build confidence when the bar feels so high? These were the questions I wrestled with and yet, in looking for answers, I discovered patterns in how I learn, adapt, and grow that have shaped my career ever since.

Over the past year, I distilled these experiences into a few guiding learnings that I hope can help others navigate transitions into data roles. I don’t claim to have all the answers, but the lessons I’ve taken from this journey might resonate with anyone stepping into a new role, field, or chapter of life. Looking back, those early uncertainties weren’t just hurdles—they were the starting points for lessons that will continue to guide me through my data career.

Hands placing post it notes into groups in a relaxed work setting

1. Anchor Your Analysis - Define Clear Objectives:

There are often countless potential directions to explore when working with data and it is easy to get carried away. It is important to set clear, realistic objectives that align with the timeframes as well as the overall project objectives. In my second DSDP project, we had access to rich eye-tracking data combined with survey responses, purchase receipts and interviews. The dataset opened up a wide array of research questions, but it became critical for us to prioritise what could be meaningfully achieved within a limited 6-month timeframe. Narrowing down to achievable objectives ensured that we made tangible progress while leaving scope for future work.

Blackboard with word 'progress' written in white chalk with a progress bar drawn underneath

2. Small Steps, Big Progress:

Complex datasets and project goals can often feel overwhelming. A useful approach is to break work into smaller, digestible chunks - even sub-tasks when needed. This makes the workload feel less intimidating and the progress more visible. Every small milestone completed is worth celebrating - it not only sustains momentum, but also makes the process more enjoyable.

Looking back, I realised that what first felt like a survival strategy became a lasting habit - tackling complexity by simplifying it into achievable steps. It reminded me that progress in data projects rarely comes from giant leaps, but from consistent, cumulative effort.

View from someone standing looking down at their shoes. On the floor are arrows drawn in white pointing in different directions.

3. Embrace the Unknown:

Data projects rarely follow a straight line. There are always unknowns - from unexpected patterns in the data to unclear directions on which insights will prove most valuable. Accepting this ambiguity is part of the craft. Instead of seeing uncertainty as a problem to eliminate, it can be reframed as space for exploration, iteration, and discovery.

In both my DSDP projects, the datasets, objectives to meet, and the never-ending learning while tackling the various data processing, analysis and dissemination steps felt overwhelming as the weeks passed. Learning to sit with that uncertainty, while keeping objectives in focus, made the work more rewarding. It taught me to remain flexible and curious, rather than rushing to resolve uncertainty too quickly.

Group of four people working alongside each other at adjoining desks from a birds eye view.

4. The Power of People and Perspectives:

Working with data can sometimes feel solitary, but the most impactful insights often emerge through collaboration. Every person brings a different “knowledge toolkit” - shaped by their background, expertise, and lived experience. Drawing on these diverse perspectives helps uncover blind spots, spark new questions, and generate solutions that one person alone might miss.

During my projects, some of the most valuable breakthroughs came not from staring at my code longer, but from conversations with peers, supervisors, and stakeholders. A casual discussion could reveal a fresh angle or a practical fix to something I had been stuck on for hours. Beyond improving the analysis, these interactions made the process more engaging and less isolating. They reminded me that every individual is a powerhouse of knowledge and experience, and that data science is as much about people as it is about numbers.

A pile of notebooks sit next to a small blackboard that has "never stop learning" written in white chalk

5. Learning, Every Step of the Way:

One of the most rewarding parts of the programme was realising the tremendous amount of learning that happens in small, steady increments. Some days it was a technical skill - debugging code or refining a visualisation. Other days it was a conceptual insight, like spotting a meaningful trend in consumer behaviour. At first, these seemed small. But over time, they began to compound, adding up to a much larger “knowledge treasure” that I could draw from.

The growth wasn’t only technical. It instilled more confidence in presenting insights, asking questions, and engaging with the wider data community. These cumulative experiences showed that learning isn’t just about the end result - it’s about staying curious, open, and appreciative of the process itself.

Looking back, the moments of uncertainty, the hours spent troubleshooting, and the conversations that sparked new ideas all shaped not just my skills, but the way I approach problems and growth. I think what stood out most about the programme was how much it encouraged learning through curiosity. The mentoring, training, and structure all made it feel like a safe space to experiment and grow. It gave me confidence not just in my technical work, but in how I approach problems and collaborate with others.

If you’re stepping into something new - a role, a project, or even a whole career shift - remember: you don’t need to have it all figured out. Take the first step, stay curious, reach out to others, and trust that each small effort compounds into real progress. The journey might be unpredictable, but it’s worth it - and it’s yours to shape.

Gauri Venkatachalapathi headshot in a circle on a pink background

Gauri Venkatachalapathi is a former Data Scientist on the Data Scientist Development Programme at Leeds Institute for Data Analytics, where she worked on two data science projects for public good — one focused on public spending resilience and another on customer behaviour patterns using eye-tracking data.

 

View Gauri's data science projects

Find out more about the Data Scientist Development Programme