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Since we first demoed it at our really successful trip to Strata London last year, a few people have asked us how we made the awesome looking Data Science Radar app that we were running on the tablets we had with us. In this post we’ll take a look at how we did it, and hopefully show you how easy it is to do yourself.

Mango is primarily known for its work with the R language, so it should come as no surprise that this is the secret sauce used in the creation of the app. More specifically, we used a Shiny app written by one of our Senior Data Scientists, Aimee Gott. The app uses the radarchart package which you can find on github.

I think the fact that it was written with Shiny has actually surprised a few people, largely because of how it looks and the way that we run it.

The tablets in question are cheap Windows 10 devices, nothing special, but we had to come up with a way of running the application that would be simple enough for the non-R-using members of the team. This meant that anything from the everyday world of R had to be abstracted away or made as simple to use as possible. In turn this means, not starting RStudio, or having to type anything in to start the app.

R and the required packages are installed on the tablets, ready to start the configuration that would allow the whole Mango team to use them in the high pressure, high visibility setting of a stand at an extremely busy conference.

We wrote a simple batch file that would start the app. This only got us part of the way though, because the default browser on Windows 10, Microsoft’s Edge, doesn’t have a full screen mode, which makes the app look less slick. We therefore changed the default browser to Microsoft’s IE, and put it in full screen mode (with F11) when it first opened. The good news here is that IE, remembers that it was in full screen mode when you close and re-open it, so that’s another problem solved. The app now opens automatically and covers the full screen.

The code for the batch file is a simple one-liner and looks like this:

"C:\Program Files\R\R-3.3.0\bin\Rscript.exe" -e "shiny::runApp('/Users/training2/Documents/dsRadar/app.R', launch.browser = TRUE)"

Next, it was necessary to set the rotation lock on the tablets, to avoid the display flipping round to portrait mode while in use on the stand. This is more cosmetic than anything else, and we did find that the Win10 rotation lock is slightly buggy in that it doesn’t always seem to remember which way round the lock is, so that it occasionally needs to be reset between uses. Remember, our app was written specifically for this device, so the layout is optimised correctly for the resolution and landscape orientation, you may want to approach that differently if you try this yourself.

We also found that the on-screen keyboard wasn’t enabled by default with our devices (which have a detachable keyboard), so we had to turn that on in the settings as well.

Having the application start via a Windows batch file, isn’t the prettiest way of starting an app as it starts the windows command prompt before launching the application itself. This is hidden behind the application when it’s fully started, but it just doesn’t look good enough. This problem can be avoided with a small amount of VBScript, which runs the contents of the batch file without displaying the command prompt. Unfortunately the VBScript icon you end up with is pretty horrid. The easiest way to change it is to create a shortcut to the VBScript file and then change the icon of the shortcut, which is much easier.

Here’s the VBScript:

Set objShell = WScript.CreateObject("WScript.Shell")

objShell.Run("C:\Users\training2\Desktop\dsRadar.bat"), 0, True

Check out the video below to see it in action, we hope you agree that it looks really good and we hope you find this simple method of turning a shiny application into a tablet or desktop app as useful as we do!


Author: Mark Sellors


Beth Ashlee Senior Data Scientist
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Spotlight on Beth Ashlee – Senior Data Scientist


Name: Beth Ashlee

Job title: Data Science Consultant

Qualification(s): BSc Biomedical Science

Time in current role:  4 years

Beth Ashlee joined Mango initially as an intern whilst studying Biomedical Science. 4 years on and she’s recently been promoted to a position of Senior Data Scientist. During this time, she has experienced many diverse opportunities and pathways that have accelerated her analytical competency.

In addition to having been exposed to a myriad of technical-based scenarios through her delivery of client training in R and Python, Beth spends much of her time collaborating on a variety of projects such as Shiny app development, data exploration or productionising models. One of Beth’s passions is her team lead responsibility for Mango’s graduate recruitment programme where she actively trains and mentors her team on both professional and personal development.

Beth is a master communicator which is reflected in the shape of her Data Science Radar – a tool used to assess core Data Science competencies. Soft skills in data science are essential to establishing meaningful relationships alongside the ability to translate business value across an organisation, an area where Beth certainly excels. Outside of work, Beth enjoys travelling to new places and attending music festivals.


Beth’s Top 3 traits: 

  • Programmer 
  • Communicator 
  • Data Wrangler

Beth scores high in both Visualisation and Programming which ties in with the types of projects she has been working on most recently. 

As would be expected given her role as a Consultant and Trainer, Beth scores strongly as a Communicator. During a recent Government project, which required significant stakeholder engagement, these skills proved essential for helping to mobilise teams around the possibilities of advanced analytics.

Beth has identified that modelling is something she needs to work on to become a more well-rounded data scientist. To support this development, she has recently been doing more self-learning and is now working on a client facing modelling project.

Having a thorough understanding of capabilities and skill levels mapped against core competencies like these for the team, can help guide and shape the data science project team best suited to the task. The result is a significantly more engaged workforce with a set of skills that the business understands and needs, to deliver data-driven value. For more information on Data Science Radar, check out our Building a Winning Data Science Team page.

Would you like to join our award-winning 2020 Data IQ Best Data and Analytics Team? Mango are currently recruiting.


Related blogs:

Spotlight on a Data Consultant: Karina Marks

Spotlight on a Junior Data Scientist: Joe Russell

presenting online
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Virtual Classrooms – bringing data science teams together

Throughout lockdown, we’ve all been encouraged to tap into virtual training opportunities – either through free online learning or professional online training courses. What isn’t being talked about is the reduction in group training – and the positive effects virtual training can have on individuals when they are able to share the experience and interact with each other as a group.

Joint training sessions have very quickly been pushed to the wayside as focus is switched to the needs of individual stuck at home.  But is this the right way to go? Surely, training as a team is an integral part of enabling individuals to feel part of a team again, bringing likeminded colleagues together to interact and socially engage for a common goal – to complete the training.

It’s all very well us undertaking personal training while at home, but it’s the exchange of knowledge and sharing of frustrations that naturally come as part of the training process that is currently being missed – if we’re stuck on a subject or training module, then we want the opportunity to voice this with our peers rather than struggle on.

Virtual training courses and workshops that are delivered live to a group by a trainer are the most impactful. Chrissy Halliday, Mango’s Customer Success Manager said:

I’m frequently engaged with our customers about education and training programs and I’ve found a common theme emerging during this period of lockdown; keeping remote data science teams connected is vital whilst striving to provide a sense of “business as usual” during these challenging times. Everyone has quickly adapted to the new virtual world we find ourselves in, and training is a wonderful way to empower teams with the latest tools & methodologies to continue to deliver value.”

Virtual team training enables individuals to talk and discuss amongst the group, allowing the trainer to advise and demonstrate in real time to their audience, ultimately providing the whole team with a platform to question, absorb and understand the subject matter.

At Mango, we believe that face-to-face training is key to delivering content flexibly to our clients. Our ‘virtual classrooms’ provide attendees with valuable access to an experienced senior Data Scientist, who is on hand to answer any queries that inevitably crop up throughout the training session – just as they would do in ‘normal times’ when delivering face-to-face training on premise to our customers.

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So what does a typical data science consultant look like I hear you say? Well, we have assessed the skills and competencies of one of own data science consultants using Data Science Radar.

Mango’s Data Science Radar maps the many and varied competencies in the field of data science against six core data science traits. These traits quantify relative strengths and areas for improvement  for individual s as well as at a team level  – ultimately enabling users and team leads to make decisions based on evidence rather than intuition.

Here’s what we discovered when we analysed one of our consultant’s persobal radars:

Name: Karina Marks

Job title: Data Science Consultant

Qualification(s): MMath

Number years in current role: 3 years

Karina’s expertise in her role demonstrates strong programming skills in both R and python and shiny app development. As one of Mango’s lead trainers, she supports building team capability and demonstrates superb communication skills, with the ability to explain complex concepts to both technical and non-technical audiences. Much of Karina’s work is centred around her knowledge and expertise in this field and generally making teams more efficient through automation.



Karina’s top 3 traits:

  • Programmer
  • Communicator
  • Data Wrangler


Karina, when you first got your results back from the radar, did any of the results surprise you?

“In general no, I do think that my radar is a true reflection of my current skillset. I expected to be a high communicator and programmer, which I am, but I expected slightly higher on the modeller as that was part of my degree. However, the projects that I have worked on at Mango over the past few years have not been focussed on modelling and so I have not been utilising those skills recently, which is reflected in my radar. This does go to show that modelling is only a small proportion of what we do as Data Science Consultants and not every Data Scientist needs to come from a mathematical/statistical background”.


What impact has the radar had on your recent work?

“I was involved in a long-term project that was to develop the capability and training support for ~ 3k employees who were moving to a new Cloudera data platform from their current complex network of different systems. My role was to provide the technical support for those moving onto the platform, whether they were using excel, SQL, R, Python, or Spark. My communicator and programmer skills here were key for this and meant I was an ideal fit for this project. Not only could I effectively communicate to different teams in understanding their needs for training and support for this new platform, but I also had the programming skills to be able to write materials and provide individual technical support to those who needed it”.

Which parts of your radar would you like to improve the most and why?

“I would like to improve my communicator and visualiser skills more. I think they are key skills in many of the projects that I wish to work in, and as I enjoy working closely with clients – having those strong communication skills are essential”.

Are you looking to build the ultimate data science team and want to know more? Check out our Building a Winning Data Science Team page.

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Project managers guide to playing to a team’s capability with Data Science Radar

Data science consulting is a rapidly evolving beast that is often difficult to tame. The many moving parts require a broad set of capabilities that, in many cases, simply don’t exist in any single person. Engaging our customers in what is a relatively new discipline, and one which is rife with exaggeration, misconceptions and misaligned expectation requires a team effort. To be successful, finding the right fit of team is a critical component of working productively at both technical and strategic level. For us, Data Science Radar (DSR) enables an efficient and effective route to building project teams aligned to both the culture and needs of any client. And it can do the same for you.

Building the winning formula for your team

Beyond the shape and relative strengths that one can evidence by looking at the output of DSR, I’d suggest it’s the inputs that are most important. The language of advanced analytics can be ambiguous at best, so having a tool that makes clear how analytics can be categorised around central themes provides a foundation that has brought our organisation together.

Mango isn’t unique in the way it might conduct its’ business, but I’ve yet to see another consultancy that has a common language set that enables everyone – practitioners, managers, leaders and support staff – to understand relative skills and capabilities in such a diverse and complex environment.

Why is this important?

Knowing the Radar’s of your team gives a greater understanding of the skills, capabilities of the whole team or common language to manage resourcing. My belief and observations are that having a common language enables our whole organisation to understand and talk about what we do. This brings us together, helps us build a commonality of purpose, and means that what we present to the outside world is more powerful. Additionally, the route to our messaging is more efficient and on point. Having an organisation that can sing from the same hymn sheet (my Welsh roots coming through here!!) sounds like an obvious requirement to building a data-led culture.

Strategically and culturally DSR has provided a platform for us to get stronger. But its more than a strategic platform. Operationally, DSR is used everyday to identify who in our team may have the best technical skills to deliver projects and bring value to our clients (Fig 1). Not just individually, but as a project team. Getting the right mix of skills, all executing aligned to respective strengths, means we can be confident to deliver on time and to the exacting standards we set ourselves. Being the right cultural and strategic fit only takes us so far – backing this up with the right technical mix ensures success across the full project lifecycle.

Develop and retain key skills

As a growing company in a fast-moving industry its critical to continually attract, develop and retain key skills and capabilities. Many of our staff have a strong desire to continually grow themselves and are keen to engage in professional development wherever opportunity presents. In some cases, this happens as a natural consequence of engaging in a wide variety of project work. But in others, it happens due to development planning aligned with our career framework. Before DSR, this meant looking at generic descriptions of technical capabilities – eg using, teaching, mentoring, coaching in R – which whilst valuable didn’t offer a level of specificity many of our team were after. Post DSR, we’re able to explicitly align development needs with DSR traits and allow our team to grow capabilities in the areas that are of most interest to them. We can also guide and shape this outcome based on business need, meaning we have a significantly more engaged workforce with a set of skills that the business needs. It’s a win-win for everyone, including our clients.

Software as a service

Data Science Radar is a piece of software, powering analytic capability. It isn’t a panacea that provides users with a utopian state of analytic health, nor is it the silver bullet that ensures everyone in your organisation understands the language of analytics. But it can be the differentiator that helps secure competitive advantage.

Gartner indicates that 80% of analytic projects fail. Reasons vary from lack of executive buy-in, misaligned expectations, disconnect between technical teams and users, and ill-defined business problems. DSR doesn’t eradicate these problems, but it does provide a platform through which many of these issues can be surfaced, discussed, understood and acted upon. Once better understood, it can then be used to ensure the right people are tasked with the right actions to execute effectively. DSR removes square pegs commonly put in round holes. It can bring an organisation together; help empower growth and provide clarity in terms of existing capabilities. Once you know these things, building project teams for the future becomes a natural next step, and keeps you always looking at where the gaps are, and how to close them.

We never built DSR to close strategic or cultural gaps, but that’s what it has done. And its enabled a more empowered and more engaged technical workforce too.

Pete Scott is Client Services Director here at Mango. For more infomation about a demo of Data Science Radar please click here.