The Data Science Accelerator programme is a great opportunity for public sector analysts seeking to develop their data science or data visualisation skills. Since its launch in 2015, over 350 analysts from more than 100 organisations have benefitted from this skills-building mentoring programme.
Complementing the Data Science Accelerator, the Data Visualisation Accelerator was introduced in June 2021 to meet the growing demand for data visualisation mentoring. These programmes are open to all UK public sector employees, including those in central and local government. The Autumn 2023 cohort is currently accepting applications, providing an opportunity for aspiring analysts to propel their careers forward.
In this guest blog post, Helen Lankester, a previous participant in the Data Science Accelerator programme, offers valuable insights into her journey, highlighting the real-world impact and career growth she experienced through her involvement in the programme.
I am Helen, a graduate of the 2022 Data Science Accelerator Programme. In this blog, I want to share my perspective and experience of being on the Accelerator programme and the progression I have made in my early career since.
I joined the UK Hydrographic Office (UKHO) after graduating with my MSc in Glaciology, which was my first introduction to coding using MATLAB to analyse the ice motion of the Greenland ice sheet. Since commencing my role at the UKHO in 2020, I was eager to enhance my coding proficiency.
A colleague in our Data Science team encouraged me to enrol on an online Python coding course to improve my skills before applying for the Accelerator programme. The UKHO’s Data Science team are very supportive of colleagues in non-data science teams gaining coding skills. A colleague in this team made me aware of the programme and helped support my application.
Project focus: automating Arctic iceberg detection
My Accelerator project focused on automating the detection of Arctic icebergs using synthetic aperture radar (SAR) imagery to enable safe Arctic navigation. Trans-Arctic Ocean navigation is hazardous, and vessels are exposed to risks such as sea ice and icebergs. Currently, the only dataset produced by the UKHO regarding iceberg concentration provides monthly iceberg positions and is therefore unsuitable for navigation. Developing a near real-time iceberg detection data product to enable safe trans-Arctic maritime navigation was required.
I began by researching what current ice products the UKHO offered to identify any areas for improvement. I wanted to work on a project that combined my previous glaciology knowledge, as I would understand the potential problems I could encounter while working with the SAR satellite imagery.
The approach I took to find the icebergs was using an edge detection algorithm in Python, which works by detecting changes in brightness. In this case, ice is very bright compared with the dark water in the satellite images, and the algorithm detects these brightness changes to outline the icebergs.
Support throughout the programme
Throughout the Accelerator programme, I felt supported by the Accelerator team at the Office for National Statistics (ONS), from the initial cohort induction to the graduation presentations. The Accelerator team match you with a mentor who is an experienced data scientist.
I organised weekly Teams meetings with my mentor, and we would virtually fix any bugs in my code. I found having a mentor very helpful to discuss ideas and help fix any project blockers.
Our cohort had weekly Teams stand-ups delivered by the ONS, which were designed to help people experiencing issues with their projects. These weekly stand-ups were an excellent way to network with people across government. I enjoyed the weekly stand-ups with my Accelerator cohort, as I could learn about other projects across government, from organ donation tracking to improving the energy efficiency of historic buildings.
Project success and my career progression
My project was initially a proof of concept which proved successful. Therefore, we are continuing to work on the project at the UKHO. This is a great achievement, as I only had one day a week over three months to work on the project during the Accelerator programme. I highly recommend that you apply to the Accelerator to not only develop valuable data science skills, but to also gain an insight into what other government departments are working on.
Before the Accelerator programme, I had limited coding experience and was finding it difficult to break into the world of data science. However, after completing the programme, I had the skills I needed to successfully gain a new role in the Bathymetry team in the Scientific Analysis Group at the UKHO. Within this new role, I have had the opportunity to use AI and machine-learning tools to identify sonar noise in bathymetric data.
The advice I would give to potential applicants would be to attend Accelerator clinics and thoroughly research your project. Before applying, I researched different methodologies for iceberg monitoring by reading scientific articles and meeting with different teams at the UKHO to understand the product I was trying to improve. I hope this blog has given you an insight into why you should apply for the Accelerator programme, as it is an excellent opportunity to meet people across government and develop valuable data science skills.