Data Analyst: A person who scrutinises, cleans, transforms, models and presents data to uncover useful information and support decision-making. 

Over the last 18 months, I have completed my Data Analyst Level 4 apprenticeship, achieving grade distinction. I developed coding skills in Python and SQL and used these tools, amongst others, to build a portfolio of data science projects. In these projects, I tackled a range of Dunelm business problems: Store Footfall Forecasting, Basket Analytics, Space Analytics, Customer Churn Modelling… 

This post focuses on my apprenticeship story, and some advice for those considering this path of learning. If you are interested in more technical details of this work, I discuss these on my personal site, and am always happy to talk with anyone interested. For now though, there won’t be a single equation in sight – I promise. 

Graduate Beginnings 

I joined Dunelm in 2018, having graduated with a Maths degree from the University of Warwick. Within my first year at Dunelm, I had worked in our Thurmaston store, had supported our web-replatform, had overhauled our Made to Measure (M2M) curtains algorithm, and was working through a couple of grad rotations in Digital Marketing. 

In 2019, a new concentrated data capability was being built at Dunelm, and I wanted to be part of it – applying my maths to problems across the business. An opportunity to complete a Data Analyst apprenticeship was offered, and I jumped at the chance. 

12 colleagues shared my aspiration, and after a few administrative forms and interviews with the course provider (Decoded) , we were writing our first lines of code. Hello World.

Course Mechanics 

Our Data Analyst Apprenticeship enjoyed a regular cadence of workshops, online learning, and applied projects. Roughly once a month, we had a half day lecture about a machine learning algorithm, or other course content. Pre-covid, these would be held in person, at the Store Support Centre in Leicestershire. Post-covid, Microsoft Teams had our back.

Next, we would follow this up with an online learning module, completed at our own pace. The platform sign-posted us to additional content if we wanted to explore more about the topic, which I always did! 

With the content grasped, it was time to apply our new knowledge to a business dataset. I found it really valuable to jump straight in, tweaking the scenarios absorbed in the lecture, and often experimenting with a technique uncovered in my own research. Google is your friend! 

We submitted our work to our Decoded mentor. They would share written feedback for discussion on our next call. Improvements made, these projects formed our project portfolio, which would count towards our final grade. Then, it was back for the next lecture. 

Each quarter, our group would convene for an intensive ‘hackathon’. We spent the day working together on a data problem of our choice. I learnt a lot from these days, and enjoyed discussing approaches with colleagues in a ‘playground’ environment. 

Playing Tetris With Your Time 

20% of my working week was devoted to my apprenticeship. Was this exactly 8 hours each week? Not at all, the nature of project work demanded some longer periods of focus, along with some quieter weeks. I was fortunate to be able to work with some flexibility. The value our new skills were bringing to the business bought us understanding across our teams. 

In the closing stages of the Apprenticeship, once all the projects had been completed, a shift towards admin duties returned: an employer reference and portfolio cover sheet was a necessary diversion from the code, before a final 40 hour Synoptic Project on a previously unseen data set. I worked intensively on this over a 2 week period. 

With all my work submitted for assessment, I enjoyed a final interview with a representative of the British Computing Society (BCS) and I was finished. I was delighted to receive a Distinction grade in early January; a belated Christmas present. 

Closing Thoughts 

On reflection of my apprenticeship, I highlight two interlinked traits for success in this environment: Passion for the subject and curiosity to learn beyond the scope of the curriculum. Ensure that you feel this way, and the hours of learning will be rewarding and enjoyable. I found myself wanting to develop my skills outside of work too. 

From a logistics perspective, it is important to be pragmatic about what you have time for, and what you don’t with your 80:20 split and maintain good communication with your manager about your apprenticeship work, so that they can schedule and flex work effectively. Protect your time, by keeping a log of your apprenticeship hours.

Next up for me on a learning front, I’m working on my presentation skills, am reading a book about data visualisation, and am completing an online course to learn to build and optimise Neural Networks. 

Please get in touch with your comments and questions, I am always happy to talk to anyone interested. See, told you, not a single equation in sight! 

Until Next Time,

Scott