analytic community
Blogs home Featured Image

CIO’s globally ranked analytics and business intelligence as one of the most critical technologies to achieve the organisation’s business goals, with data and analytics skills topping the list as the most sought-after talent. As we embrace digital transformation, it’s clear that the need to upskill and resource data science teams has become far more pronounced.

Building a successful and lasting data community can often be one of the most significant hurdles to overcome, which takes time and effort. Often establishing and maintaining a thriving collaborative approach to analytics, with the right ecosystem for your community, can be a challenge.

Naturally there’s a growing need to ensure analytic teams have access to the best tools and latest methodologies to perform their analysis and find business wisdom. Alongside identifying the analytics skills already in place, a great place to start is also to identify the best tool for the job.

Nurture and aligning members of the community

Pulling together existing disparate data science resources into a single, connected community of practice, creates a secure foundation to grow analytic talent. Having such a community means the business will have a better understanding of the skill sets that exist within the organisation already, as well as best practice examples for approaching different scenarios and a better awareness of the tools and solutions that can be used.

Defining the right tools for the community

R and Python are still the two most popular and adopted programming languages. Both tools are open source, free to use and cover pretty much everything data science-related.

R was developed specifically for statistical analysis, so naturally is the popular language choice for statisticians. R has a large user community and an actively developed large library of packages which enables effective analytics. However, R can require a steeper learning curve and people who do not have prior programming experience may find it difficult to learn.

Python on the other hand, is considered the easier of the two most popular languages to learn. Its domination in machine learning is well-known. With an increasing community base, Python is commonly taught in Computer Science lessons in Schools and therefore the rated language of choice in academia. However, Python can be considered to have its limitations especially around speed and memory, so best practice use should be applied when considering Python.

It’s not a debate as such on which language to use, but more a conversation around empowering a team to become multilingual and multiskilled, so they can use the best language for the application.

Up-skilling of analytic talent

For an organisations analytics function to thrive, it’s critical to continually attract, develop, and retain key data skills & capabilities. Understanding the mix of skills within a data science team, as well as identifying gaps to unify skills & knowledge, is vital to drive analytic value. Establishing the support of a dedicated Learning and Development partner, who provides live, instructor led, data science training programmes, designed to equip and enthuse a data team with the latest approaches, can help address this challenge & unlock business gold.

Enabling training at all levels of data awareness will be critical, and this should even include training on how to use information, to guide decision-making.

Building a successful community provides a solid basis for working out where the talent pool needs to be extended, unifies talent across the business and enables quick wins towards embedding the right culture and building the required capability.

After 20 years of experience, we are a trusted data science L&D partner to leading brands worldwide. We train thousands of data science and analytical teams every year from a range of industries and backgrounds.


virtual training blog
Blogs home Featured Image

“Education is the passport to the future, for tomorrow belongs to those who prepare for it today”. Malcom X

As Nelson Mandela acknowledged, education is truly the most powerful weapon which can be used to change the world. This year especially, we’ve all experienced the need to be agile, to adapt to changing circumstances and recognise how upskilling and learning is essential to expanding our capability to meet an ever-adapting environment.

It is upon foundations such as these that Mango has built education solutions on; working with data science teams to build future proofing skills aligned to strategic objectives.

A recent survey by Udemy noted the recent and specific emphasis on upskilling and reskilling as a result of the pandemic, with 62% of organisations aspiring to close their skills gap. A key starting point for business is in assessing the skills needed to meet their strategic aims and business objectives. In addition, they need to consider the diverse capabilities across their teams, to truly embrace the right learning culture. Team building and upskilling is an integral part of embracing change for a data-driven future. According to the report by Udemy – data literacy is the new computer literacy.

Workforces with strong data skills across the organisation, not just limited to the analytics team, can help embrace these positive changes. Quite often, it’s the business stakeholders who own the targets and processes who are most empowered by engaging in the data & analytics conversation.

As educators in data science and advanced analytics, we’d like to share some of the most effective strategies when looking to upskilling teams and business stakeholders appropriately:

  • Ensure a dynamic, facilitated learning environment – This year, like no other before, saw providers going virtual. Whilst nothing replaces face to face contact, any method that brings your teams together virtually into a real classroom setting, is the next best thing. Applying learning to a workflow requires a mentoring-based approach to help build lasting and best in class capability. Self-learning or pre-recorded lectures have their place but lack the interactive ‘in person trainer’ approach, where wider questions can be answered.
  • Apply a unique learning experience so that it is tailored to the market or industry – the ability to adapt to the needs of a diverse group is a core skill as a trainer. Practice exercises tailored to help beyond the classroom will allow newly applied their skills to be incorporated into a daily workflow.
  • Ensure training partners have real world experience from industry – the ability to showcase relevant examples and not just the theory, can really help bring a programme to life. In our experience, the ability to share and provide value via real-world applications, combined with practical, proven approaches and best practice advice, is key.
  • Choose courses as an integral part of a leaning pathway – courses for individuals and teams should be chosen as part of learning pathways to fill any capability gaps. Processes which invest in capability, with ongoing development of skills are proven to help staff retention. There should also be processes in place to retest these new skills.
  • Demystifying data science – the ability to establish a common language between all functions of an organisation is essential to a collaborative partnership. This depth of understanding will then ensure a close alignment between analytics and strategy, supporting any barriers to change.

Following the delivery of a recent training programme delivered to AstraZeneca, Gabriella Rustici-Data Science Learning Director, commented:

“Having worked with Mango previously on a training project, we reached out to them as a trusted partner to assist us with a data science training initiative which involved helping cohorts from our R&D data science function embark on their R journey.  We were also looking for a workshop to help our scientists ‘demystify’ data science and understand the terminology – establishing a common language between scientists and data scientists.

Mango helped us create a remote virtual classroom R training program, which included support surgeries designed to enable participants to really absorb what they had learnt from the program. Feedback received from course participants was excellent, with comments such as: “ The instructor was great, really patient”, “The instructor was very enthusiastic, clearly knew their topic and the learning material was great”, “ I got a lot from the course” and “I’m keen to learn more R, hopefully with Mango”. The workshop was well received and has certainly given us a good start to increasing awareness of what data science can do.”

About Mango Training

Whether you’re seeking R training courses, Python training courses or more, our comprehensive training programmes are specially designed to guide practising data scientists and data engineers from breakthrough to mastery level in R programming, Python, Shiny, AI/ML and more.


weirdos wanted
Blogs home Featured Image

Dominic Cummings’ now infamous job advertisement at the beginning of this new decade asked for “data scientists, project managers, policy experts, assorted weirdos” to apply for a position in government.  Like many organisations today, Cummings is looking to put data at the heart of UK civil service decision making and is looking for the right people to drive this more scientific decision making.

But it was the ‘assorted weirdos’ mention that struck me.  Why weirdos?

Today’s workplace is changing: people are living and working for longer creating a more age-diverse workforce, and as it becomes easier to interconnect, we are increasingly working with people from across the globe, often from different cultures speaking different languages.  So, it’s little wonder that Cummings is highlighting the need for an “unusual set of people with different skills and backgrounds.”  The more diverse the workforce, the more informed decisions will become with such a melting pot of viewpoints and perspectives.

Technology is a key enabling factor in this changing workplace, and there’s no doubt that data is driving better decision making, so I applaud Cummings’ innovative job description on one level, but on another, you can’t just invest in the tech tools and hire the data scientists and expect the decisions to be better.

Being data-driven is not about the technology, but about what makes the technology work: the people, the processes, the culture and the mindset.  I cannot stress highly enough the importance of getting this right in order to achieve a data-first approach.  too many organisations fail to understand that successful data-driven transformation is about instigating a cultural change that requires organisations to truly redefine how their people operate internally and what their processes need to be to support transformation.

Part of the pleasure of my job is going to visit different organisations and learning about their individual challenges and how they propose using data to help.  Unfortunately, in too many cases, they have been dazzled by shiny new tools, products or platforms that vendors are selling, they throw money at the problem and expect things to change.  Sadly, not all that shines is gold.   People and processes will produce better results than products.

Let’s look at the people for a moment.  The role of the data scientist has changed.  No longer can the “weirdos” be brought in to sit in the corner, speak a mysterious language to one another and remain completely disconnected from the business producing occasional sparks of genius.  Success comes from communication with the rest of the business to ensure everyone understands why they are putting data at the heart of every business decision and to ensure everyone is rowing in the same direction.

It’s no coincidence that most consultants have cited Emotional Intelligence as one of the top 10 skills required for the workplace in 2020, and this is something that is particularly close to my heart.  I love helping data science teams put aside for a moment their (usually impressive) data skills and focus more instead on how they communicate and engage with the business and break down barriers across the organisation.  Understanding the needs of others and communicating what you are trying to achieve and how that can help others achieve their goals is not always easy and many individuals and teams struggle to develop the necessary skills

It’s for this reason that I’m particularly proud of the Trusted Consultant Programme that Mango has launched.  Increasingly, data scientists and analysts are being asked to run or contribute to complex, multi-departmental projects and expectations of success are high. The programme is designed to help data science and advanced analytics teams with a proven framework and tools they need to engage with stakeholders within their organisations in a positive, success-led manner.  By the end of the course attendees will have developed the essential skills required to work with business stakeholders, work as part of a team to manage the project, and present analytic results to non-technical audiences.

So, like Mr Cummings, Mango is calling for data scientists, assorted weirdos and unusual sets of people to learn how to communicate with the world the ways in which their unique skills and backgrounds can help others make better decisions.  Perhaps we could also invite a few politicians to apply as well!

If you want to find out more about our Trusted Consultant Programme contact

Blogs home Featured Image

What’s in a Data Community? One of the UK’s top retailers, Sainsbury’s, knows just how much value can be derived from a community, which, when coming together in a data science context, can add immeasurable business benefit throughout the organisation.

In fact, Sainsbury’s firm belief in the power of data science and community has led to the establishment its own Data Community, which collaborates with teams across the business to find new ways of working with data and learn what’s possible. Part of the activity includes biannual Group Data Conferences, which provide an opportunity for Sainsbury’s 800-strong community to come together to listen to inspiring ideas, connect as a community and get involved with all that the industry has to offer.

As big believers in data analysis that delivers, the Mango team was thrilled to be involved with the company’s most recent Group Data Conference, delivering two R-focused workshops; a high level Introduction to R for analysts not familiar with this popular statistical programming language, showcasing why R is one of the leading data science tools, and a Package Building in R workshop for more advanced users that focused on getting started with building packages, understanding the benefits of package building best practices and being able to implement them.

“We were delighted to have Data Science experts Mango Solutions participating at our internal LOVE Data conference – an event for the 800 data professionals across our Group,” said Naomi Sayers, Sainsbury’s Group Senior Manager of ADS Community & Capability Group Data. “Mango provided training workshops, supporting our aim of inspiring and connecting our community.”

Mango has and continues to support various large companies and organisations keen to set up or who have already set up their own internal data science communities. It’s an excellent way for companies to promote data science culture and methodologies, upskill employees and encourage collaboration across different teams, sites or departments.

If you or your company is keen to find out more about how Mango can help your data science journey, get in touch with us or take a look through more information here.

Blogs home Featured Image

As we come to the end of Shiny Appreciation month we hope that the blog posts and tweets have encouraged more of you to start using Shiny to create your own interactive web applications.

If you need some help however with getting started with Shiny, or with more advanced functionality such as understanding reactivity, making plots interactive, debugging your app or writing reusable Shiny code, then the good news is that we have three new Shiny training courses.

These one-day courses run from getting started right through to best practices for putting Shiny into production environments.

The courses are being run publically in London in July and September:

  • Introduction to Shiny:    17th July
  • Intermediate Shiny:        18th July
  • Intermediate Shiny:        5th September
  • Advanced Shiny:              6th September

Alternatively, we can deliver the courses privately to individuals or groups if preferred. We will also be offering a snapshot of the materials for intermediate Shiny users at London EARL in September.

Importantly, all of these courses are taught by data science consultants who have hands-on experience building and deploying applications for commercial use. These consultants are supported by platform experts who can advise on the best approaches for getting an application out to end users so that you can see the benefits of using Shiny as quickly as possible.

For further details please take a look at our training page or contact us at