How effective mentoring relationships are growing data science skills and capacity

The back of 2 people sat at screens with code on.

The Data Science Accelerator programme has been developing the skills of public sector analysts through project-based mentoring since 2015. It has supported over 300 participants from 100 organisations. We are now into the 20th cohort and will be accepting applications until Friday 4 February 2022.

The programme’s success is not only down to the enthusiasm of participants wanting to learn new skills and help grow their organisation’s capacity to make better use of data. It is also the willingness of experienced data scientists who take time out of their busy schedules to improve the data science skills of the public sector through coaching and mentoring.

The relationship between the mentor and mentee is vital to a successful outcome, in an environment where time is limited and there can be challenges to overcome.

In this blog we hear from Daniel O’Callaghan (Plant Health Economist, Forestry Commission) and his mentor Sam Taylor (Senior Data Scientist, Cyber Resilience Centre, Department for Work and Pensions) about their mentoring relationship and how it helped Daniel run a successful project for the Forestry Commission.

Here, Daniel summarises his Accelerator project:

“Trade is a significant factor in the introduction of pests and diseases to trees and woodlands, so import intelligence plays an important part of the Forestry Commission’s (FC’s) biosecurity measures to minimise the probability of introducing pests or diseases. The project’s objective was to automate the process of extracting information or data about firewood imports from shipping manifests that form part of this import intelligence work in my team. The project consisted of two parts:

  • creating a program that could sift through the manifests and extract data
  • developing an application that would allow for the program to be usable within the team.

I used python as the main programming language, using regular expressions (regex) as the basis for extracting data from the manifests and Flask framework for the development of the app”.

The coronavirus (COVID-19) pandemic has meant remote mentoring has taken place for the last 2 years, which can sometimes be daunting on a technical project. Daniel and Sam shared some of the tools they used to help their mentoring relationship.

Daniel: “A weekly catch-up meeting on Microsoft Teams worked quite well, as we could share our screens to discuss and work through topics and issues. We also made use of GitHub to share code easily.”

Sam: “Live coding by using screen sharing on Microsoft Teams helped us to close the gap between the virtual and real world. Executable files are blocked by corporate networks which made working together on a piece of code tricky without something like GitHub so having access to this was incredibly important for this relationship.”

Even with a well-planned project, unforeseen challenges can sometimes occur and press what is already a very short timeline. Daniel and Sam shared how they tackled the challenges they encountered.

Daniel: “On the whole the project went mostly to plan. We were quickly able to write a program that allowed us to sift through the manifests and extract the data of interest. Changes to the manifests’ format early in the project meant that most of the work we did on those was redundant. It took a while to get used to and understand the Flask framework which the app was developed on”.

Sam: “Despite the format of the data changing, we’d set up the project in such a way that the methodology that we were trying to implement still worked which certainly helped with the challenge that we were facing. Alongside this, we encountered data quality issues which are standard for a Machine Learning project!”

It’s not only the mentee that benefits from the Accelerator programme. Mentors have told us that they learn so much through the process of mentoring. So, what were the main takeaways here?

Daniel: “I learned a lot in both learning a new programming language and the process of developing an app. I also used some old skills like HTML and CSS, something that I hadn’t used since university! The weekly stand-up meetings with other Accelerator participants were really useful and insightful, as they allowed me to gain insight on other topics and issues across the public sector and data science techniques being used to tackle them.”

Sam: “One of the reasons why I volunteered for this project was to learn something new. I hadn’t done anything with PDF text extraction, so it was a good opportunity for me to expand my skillset. I truly believe that the best approach for learning is to teach something to someone else and this really pushed me to delve deeply into this topic to fully understand it”.

Both the mentor and mentee’s home organisations see great benefit from the Accelerator, from new tools to solve business problems, and staff being able to equip others with their new skills, increasing the data science capacity of the organisation. How did it benefit the Forestry Commission and the Department for Work and Pensions?

Daniel: “The programme has allowed me to use some of the skills I have learned to be used in other parts of my work at the Forestry Commission. The app will save a lot of time for my team, as the process of sifting through a manifest and extracting the data has been reduced to under 30 seconds. It has also highlighted the opportunities for using data science in tackling issues”.

Sam: “I’d say that the main benefit for the Department for Work and Pensions was the personal development of my softer skills. As a Senior Data Scientist, it’s your responsibility to develop and mentor more junior member of your team so this is a brilliant opportunity to hone skills beyond just your technical ones.”

Are you considering applying for the Accelerator programme but not sure if it’s right for you? Hopefully Daniel and Sam’s experience will help you decide.

Daniel: “I would encourage anyone thinking of applying for the programme to do it. It is a great way to learn new skills, whilst delivering something useful to your department.”

Sam: “Do It! Data Science is still very much an emerging discipline across government and as a community we have a responsibility to ensure that we’re doing it in the right way, ethically and following best practice. You can play an active role in this through this programme.”

Both Accelerator programmes are open for applications until 4 February 2022 (Data Science) and 11 February 2022 (Data Visualisation) respectively. Visit the Accelerator page for more information and to apply or contact the team.

In our next blog we will hear from a mentor and mentee on the Data Visualisation Accelerator programme, and how this newly established branch of the programme is already making an impact.