New “Analysis Function Learning Pathways” have been developed to bring together analytical courses from a range of providers, supporting the Function’s aim to develop analytical capability across government.

Following the suggested sequence of complementary courses will help you to see where your learning could take you and provides suggestions on follow-up courses if you are interested in developing your learning further. The pathways will help you develop a stronger holistic understanding of analytical topics and be supported in continuing your progression more so than by taking a stand-alone course.

There are a variety of pathways available, with one to suit you whether you are a coding whizz or have done no analysis before. Each pathway has an attached persona, helping you understand which pathway is the right one for you.

To give you an idea of what you can expect from each pathway, why not explore these examples:

Communicating Insights

Communicating insight is extracting insights and information from data and communicating them to decision-makers in a way they’ll easily understand. It is as important as researching or analysing data. This pathway will provide you with a basic understanding of risk and issues surrounding statistical disclosure, communicating quality, change and uncertainty as well as best practices of conveying your message using visual aids.

After completing the three courses in this pathway you should be able to:

• Understand the key risk of statistical disclosure while communicating your results.
• Describe the principles and issues of communicating quality, uncertainty, and change for effective communication.
• Understand the existing guidance to improve publications.
• Communicate the results using visual aids by following best practices.

Reproducible Analytical Pipelines (RAP)

Reproducible Analytical Pipelines (RAP) focus on the use of open-source analytical tools and a variety of techniques from various fields such as software development, software engineering, analytics and collaboration, in order to deliver reproducible, testable and auditable analysis pipelines. This pathway aims to provide a high-level understanding and hands on practice with open-source tools and approaches to develop automated high-quality reproducible research practices while removing any manual/semi-manual processes.

After completing the eight courses in this pathway, you should be able to:

• Demonstrate a hands-on understanding of open-source analytical tools for developing reusable automated data pipelines.
• Use GitHub to track changes made to the code in a collaborative development environment.
• Test, document and package code in R and Python using reproducible programming techniques.
• Understand the role of a continuous integration pipeline towards development, testing and integration automation in a collaborative environment.

Deciding which pathway to start with can sometimes feel confusing as trying to gauge if a course is pitched at the right level for you to build on your existing knowledge can be tricky.

To help you decide which pathway is right for you we have developed some useful personas.

Learning personas are designed to create a realistic representation of the intended learning audience which will help you identify what learning is best suited to you.

Personas are created based on a variety of information from pre and post course surveys, learning needs analysis as well as one to one feedback from course participants and information/observations from learners looking for a course.

The personas are designed to be relatable to you and will give you an idea of whether a pathway will not only be at the right level but will also help you achieve your learning goals.

Communicating Insights Persona

General background

Alex works in policy and uses data to inform his communications to stakeholders. He uses data insights to put forward proposals that will increase efficiency.

Starting point

Alex’s preferred method of communications is business papers, however, more recently there has been a push to make these shorter and move towards a more visual means of communication.

Perceived needs

Alex would like to learn more about statistical disclosure control as this is something he has never really considered in his communications and how to create an impactful message visually.

Special considerations

Alex works part-time due to caring responsibilities so would prefer online self-study courses to allow more flexibility.

Benefits of this course

This pathway will provide you with a basic understanding of risk and issues surrounding statistical disclosure, communicating quality, change and uncertainty as well as best practices of conveying your message using visual aids. 

RAP Pathway persona

General Background: Sarah is an analyst who joined the analysis team a year ago.

Starting Point

Sarah attended an introduction to R course a year ago, and has been using R in her work ever since. Sarah is able to code, but she finds it hard to repeat and remember parts of her analysis months later. 

Perceived Needs

Sarah produces the same quarterly report and would like to automate this and make quality assurance easier. She would like her colleagues to be able to use her code.

Special Considerations

Sarah doesn’t have a lot of time to invest in learning and would prefer not to read large amounts of text.

Benefits of this Course

This learning journey will help participants gain the technical tools and familiarity with best practice necessary to transform their work into Reproducible Analytical Pipelines (RAP)

To access the learning pathways, you will need a Learning Hub account. Email the team at gss.capability@statistics.gov.uk if you don’t already have an account.

Jane Crowe, National Programme Manager, Data Science Campus in the Office for National Statistics (ONS) explores how becoming a mentor can help to boost your own learning, as well as that of your mentor:

“While we teach, we learn” is a quote attributed to the Roman philosopher Seneca and this age-old truth is echoed by colleagues who mentor on government data science mentoring programmes.

Data science mentors provide advice and guidance to help colleagues develop technical skills while working on an analytical project. From a knotty technical question or new insight from their mentees’ data to a different approach to project management or simply a new way of using GitHub, mentors report that they gain new skills and knowledge through mentoring others. It could also provide opportunities your current role may not, such as demonstrating and developing skills like coaching, communication and leadership. Mike Hudgell of the UK Hydrographic Office says “Simple questions can also sometimes test the depth of your own knowledge and understanding, there is always something new to learn!”

Data science mentors also value the sense of reward that comes with helping someone grow, develop and achieve their goals. And let’s not forget the bigger picture, that they’ve contributed to increasing the skills available to ensure government and the public sector can make better decisions and deliver better services for citizens.

And why else do people choose to mentor? Well, simply because they enjoy it! They make new friends, become part of a community of esteemed experts, gain fresh perspectives and grow their professional network. Mentor Sonia Williams of the Department for Environment, Food and Rural Affairs said: “I enjoy getting to know someone from another organisation and learning more about their work. The variety it adds to my job – you can end up mentoring a project completely different to your day job.”

If you would like to share your data science skills and knowledge, learning while helping others grow, there is a range of mentoring opportunities available at various skill levels. This includes the Data Science Accelerator, incorporating the new data visualisation category*, the ONS Data Science Campus international mentoring programmes and the Scottish Government Data Science Accelerator. Feeling unsure? We understand that and that’s why we’re setting up a programme of support and coaching for data science mentors.

If you would like to find out more about the programmes available and how to get involved, why not attend one of the data science mentoring clinics. This will also be an opportunity to put your questions to a panel of experienced data science mentors. The clinics will be held be on 1 and 14 July.

So, whether you’re thinking ‘Sign me up please, I want to join the Movement!’ or ‘I think I’m interested but I’d like to find out more’, email the team at data.science.accelerator@ons.gov.uk.

*The Data Science Accelerator is delivered by the Office for National Statistics Data Science Campus in partnership with the Analysis Function, on behalf of the Government Data Science Partnership.

The fifth round of Free Trade Agreement Negotiations with New Zealand took place between 8-16 June 2021. New Zealand’s Trade and Export Growth Minister Damien O’Connor also visited the UK from 16-18 June for face-to-face talks. Both countries have agreed to accelerate negotiations to finalise the details of the deal with the aim of reaching agreement in principle.

Both countries are committed to agreeing a high quality, comprehensive free trade agreement that supports jobs, broadens consumer choice, and provides more opportunities in key industries such as services, digital trade, and the green economy.

Strong progress was made in agreeing key issues across the deal including provisionally closing a further four chapters:

• Government Procurement chapter, which improves Small Medium-sized Enterprises’ (SMEs’) access to procurement and the integrity of supply chains.
• Disputes Chapter, which establishes mechanisms to promote and enforce compliance with the agreement and ensures that state-to-state disputes are dealt with consistently, fairly and in a cost-effective, transparent, and timely manner. It gives businesses and stakeholders certainty that the obligations under this agreement will be upheld.
• Transparency chapter, which underscores the rule of law as the major cornerstone of good governance, outlining agreed expectations for the UK and New Zealand to be transparent, open, and accessible to UK businesses, with respect to this trade agreement and their respective regulatory environments.
• Trade and Gender Equality chapter, which recognises that women are underrepresented in international trade, and aims to support women exporters, business owners, and entrepreneurs to participate in global trade.

Excellent progress was also made during Round 5 on the following chapters, which the UK and New Zealand agree now have a clear path to closure:

• Rules of Origin
• Goods
• Cross-Border Trade in Services
• Customs
• Digital
• Telecoms
• State Owned Enterprises
• Consumer Protection
• Good Regulatory Practice
• Labour
• Development
• Anti-Corruption
• Initial & Final Provisions
• General Exceptions
• Institutional Provisions

In previous rounds, chapters on SMEs, Competition, Remedies were provisionally closed.

The Government has been clear that any future deal with New Zealand must work for UK consumers, producers, and companies. Throughout the process the UK will continue to engage stakeholders to ensure their views inform our approach to negotiations.

Any deal the UK agrees will be fair and balanced and in the best interests of the whole of the country. We remain committed to upholding our high environmental, labour, food safety and animal welfare standards in the deal, as well as protecting the National Health Service (NHS).

The UK and New Zealand both remain eager to make further progress, with the UK clear that momentum needs to be maintained across the whole agreement. Ahead of the next round, negotiating teams will share further proposals and discuss a range of issues.

The next round of negotiations, Round 6, is scheduled to take place in July, with a series of intersessional discussions across the FTA planned for the next month.