Data Science Degree Apprentice

Abstract

In September 2019, 27 apprentices enrolled in the Level 6 Applied Data Science Degree Apprenticeship program, the first of its type in Wales. This article details the experiences of an apprentice on the course who works in the Data Science Campus at the Office for National Statistics. It summarizes how they have found the apprenticeship so far, the sort of data science projects they have been involved in, and the experience of studying for the degree at the same time.

In September 2019, 27 apprentices enrolled in the Level 6 Applied Data Science Degree Apprenticeship program, the first of its type in Wales. This article details the experiences of an apprentice on the course who works in the Data Science Campus at the Office for National Statistics. It summarizes how they have found the apprenticeship so far, the sort of data science projects they have been involved in, and the experience of studying for the degree at the same time.

Main Text

My name is Evie, and I have been a degree apprentice at the Office for National Statistics (ONS) since March 2019. I combine studying for a BSc in applied data science (delivered by Cardiff Metropolitan University) and working at the ONS Data Science Campus alongside experienced data scientists, applying my learning to real-world projects.

The Data Science Campus uses new data sources, including administrative data and big data, for public good. Formed in 2017, the Campus has worked on projects to provide insight into key policy areas, as well as supporting a variety of learning and development pathways into data science at a range of different levels, including the Level 6 Data Science Degree Apprenticeship, launched in late 2018, and the first of its kind in Wales.¹

I joined the apprenticeship program with little experience in computer programming. After completing A Levels, I went to university to study chemistry. While I enjoyed looking at data to help me understand different concepts and ideas, I wanted to study this in a real human context, so I left the course after a year to pursue a career in a different field. I was eager to work in an environment where I could make a positive impact from the outset, and I feel the apprenticeship program allowed me to do this, while studying a novel and unique degree.

Since arriving at the Campus, I have been involved in a variety of different projects, learning different data science techniques and programming languages. One highlight for me was a project investigating the differences in management practices and business characteristics between high growth and regular businesses.² Using the Organisation for Economic Co-operation and Development (OECD) standard definition, a business is considered high growth if its turnover or staff number increases by 20% over a 3-year period. We investigated whether there was a relation between engaging in positive management practices and the growth of the business using the Management and Expectations Survey, which asked a series of questions about the management and characteristics of a business—for example, whether or not staff members were rewarded with performance bonuses. We found that there were slight differences in the management style of high-growth businesses, and these findings were shared with the Department for Business, Energy and Industrial Strategy (BEIS) as part of its wider work investigating alternative indicators of growth.

As well as experiencing different projects at the Campus, I’ve had the opportunity to engage with the local community³ by becoming a STEM Ambassador. I have delivered workshops to both primary and secondary school children, introducing them to artificial intelligence and the basics of coding through interactive presentations. I thoroughly enjoy taking part in STEM events, as I learn how to communicate data science to different audiences. The enthusiasm and enjoyment for mathematics and programming in young people is very rewarding!

As an apprentice, I spend one day a week at Cardiff Metropolitan University attending workshops and lectures as part of the degree. The modules are a mix of programming, computational mathematics, and specific data science modules, building up towards being assessed on our own data science project in the final year. I’ve particularly enjoyed learning more about the different ways to analyze and visualize geospatial data. One assignment was to create an engaging presentation and supplementary infographic to demonstrate how data can tell a story and challenge perceptions of a certain topic. In my group, we used traffic data to look at the effect a car-free day has on different areas of a city and, hence, whether this strategy could be usefully implemented to lower vehicle air pollution in the long term. It used disaggregated traffic data to perform a detailed analysis on the flow of traffic over time. This is one example of how the university course and workplace overlap, with access to such data sources being a great benefit of the degree program.

I am very excited about my future in data science! Over the next 2 years, I will learn about more complex techniques, including more advanced machine learning, leading towards working independently on projects and becoming a fully qualified data scientist. I hope to use the skills I learn through the degree and in the workplace to make a positive difference in a variety of sectors.

Biography

About the Author

Evie Brown is a data science apprentice at the Office for National Statistics Data Science Campus. As part of the apprenticeship program, she studies part time for a degree in applied data science while working on data science projects at the Campus. She is particularly interested in learning how to visualize and communicate geospatial data and using machine learning on large datasets. Outside project work, she enjoys communicating data science to young audiences at STEM events.