21 Posts to date | Which have been Adams favourites?

Team #VisualisingHE have had a lot of fun and learned a lot over the last 21 months and I thought I would kick off my reflective post covering some of my favourite bits.

We kicked off the project in March 2017, to-date we have created some 21 posts, we have challenged ourselves to numerous new charts including; Sankey’s (Apr 18), radial charts (Nov 17), and even a chord diagram (Apr 18), to name just a few. Explored many open HE datasets, I have learned a ton about remote communication and working together, peer editing and critique, not to mention furthering my Tableau skills and bettering my fairly poor writing skills.

So what have been my favourite bits?

  1. Overall favourite blog post
  2. Favourite Viz – Elena
  3. Favourite Viz – Dave
  4. Favourite Viz – Adam
  5. Favourite solo blog post

Overall favourite blog post

Having written this paragraph a few times whilst in draft, it became obvious to me I have a few blog posts that I have really enjoyed (for various different reasons).

However I have settled on one standout post for me, of which I wasn’t personally involved, but admire a lot, it is the NSS 2017 – one dataset many dataviz approaches post, where Dave and Elena opened up the concept of #VisualisingHE to the #MidlandsTUG,  the result was a fantastic post showcasing the TUG’s many and varying vizzes.


My favourite viz – Elena

Elena’s contribution to the project has been invaluable. Her style is also unmistakable and has earned her quite a few Tableau public ‘viz of the day‘ accolades! My favourite is without doubt her solo post ‘destination Europe‘ a topic that really sang with her and it showed in her viz and really came over in her superb blog post.

Inflow & Outflow (6)

Interactive viz

My Favourite Viz – Dave

Dave consistently knocks out meaningful and insightful dashboards with care and thought. I love his style, ever clean and simple, which is, as we know often more difficult to achieve than the most complicated and intricate of vizzes.

I think my favourite viz of his is found in our September 18 post ‘A looksie at UCAS‘. It’s clean, simple and effective, uses a minimal colour palette but packs a punch.

UCAS subjects 2009 to 2018 (1)

Interactive viz

My Favourite Viz – created by me

For me, my favourite viz is probably one also featured in our latest post (Sept18). I have tried to develop my concept of #Coffeetableviz over the last couple of years, and this ‘looksie at UCAS viz’ gets close to the effect I have been trying to create. It isn’t totally finished and has a few elements I should tweak, however I think it packs a punch, gets the BANS over to the viewer, has a playful side, provides a takeaway but also encourages the user to take a deeper look at the interactive viz (and that’s what I wanted to achieve).

A looksie @ UCAS placed Applicants

Interactive viz

Given this post is about ‘my favourite bits’, I feel it justified to dwell on another post to which I enjoyed vizzing AND writing (I don’t always enjoy the writing bit as much as the vizzing if I’m honest!).

My favourite post and viz was my post on LEO – #Realtableau to funviz .

Why? Well I took a viz I crunched for my Exec team and Employability directorate and had a little fun with it over a few evenings at home. I really enjoyed documenting my journey of exploration, my battle with creating the radial chart and the iterative nature the viz took whilst working remotely with Dave and Elena. They have both always been on hand to comment on my viz outputs, suggest improvements and laugh with me, whilst I bash out expletives during the struggle to get a viz to behave!

The main viz for the post finished up looking like this:


Interactive viz

Thanks Dave and Elena, I have really learned a lot over the last 21 months.

Can’t wait for the next tranche of open HE data sets to emerge.

Thanks for reading.



The potential TEF metric – sustained employment from LEO

Back in 2016 the government released its first experimental LEO data (or Longitudinal Educational Outcomes to give it its full given name) and it has now matured to be considered under the TEF (Teaching Excellence Framework to give it its birth name) with TEF 2 including an element of it as a supplementary metric.

Unlike the salary data (which Adam did a superb job vizzing here) this metric is not going to grab the attention quite as easily. The metric is sustained employment 3 years after graduation.  The data is sourced form HM Tax returns and does have challenges (lack of self employment, maternity leave etc etc) but it does provide some interesting patterns by subject.

The first viz is a simple view showing where the HEI (Higher Education Institutions to give it its name that its Mother would use when telling it off) sit by its metric outcome grouped by subject.

The interactive viz is here

LEO 3 Year

As you can see the GB average is around 75% for most subjects with just Languages and Combined subjects falling below that. Economics, Education, Mathematical Sciences, Medicine and Dentistry and Nursing all have a GB average above the 75% seen in the other subjects.

At the moment this is just a supplementary metric which will only be used for context when selecting the TEF outcome (another excellent blog explaining TEF here) but it shows the direction of travel and HEIs need to get a handle on where they sit in this metric.

To aid that understanding Adam has created a magnificent more exploratory viz which allows the data to be split by sex, years after graduation (1, 3, 5) as well as region. It can then be viewed by subject with the ability to highlight your institution.

Take it for a spin here

LEO - Proportion of students in Sustained employment further study or both after graduating

Well I hope you enjoyed reading and found it informative – any questions, queries or feedback let us know.



LEO – #RealTableau to Funviz

flow diagramv2

What is this post about?

LEO is back again as the topic of this #VisualisingHE blog post. Why? Tableau Zen Master Chris Love nicely pricked my attention with his tweet:

chris tweet

Charlie Hutcheson among many others have engaged with this great conversational starter and call out for some ‘real life’ examples of Tableau in the wild. For Charlie this ended his blog posting hiatus and encouraged him to write a great post Hiatus over thanks to #RealTableau along with sharing a flurry of superb examples of #RealTableau dashboards on Twitter.

For me, this prompted me to share one of my viz by day V’s viz by night Tableau endeavours. Often my #VisualisingHE vizzes are spawned from #Dataviz by day dashboards. #Dataviz by night explorations both deepen my understanding of a dataset and further my own data visualisation skills (often allowing my more creative streak to get an airing). The LEO data set is becoming my favourite of late, it is interesting and very topical in the current HE environment due to the introduction of TEF. The LEO dataset is public and therefore lends itself perfectly for this blog post about my flow from #RealTableau to a Funviz, showcasing vastly different approaches to data visualisation.

This blog post focuses on telling the story of the evolution of an idea, highlighting how much I value the #VisualisingHE project which encourages me to practice my Tableau skills and benefit from constructive critique.

A little intro to the dataset

In case you haven’t read my previous blog on Introducing LEO’s Graduate Earnings, the Longitudinal Education Outcomes (LEO) data enables us to know how much UK graduates of different subjects at different universities are earning now, either one, three or five years since graduating. It does this by linking up tax, benefits, and student loans data”. (WONKHE beginners guide to LEO).

#RealTableau to Funviz

#Dataviz by day

1. #RealTableau viz. This standard functional data visualisation is designed to give a user an overview of an single entity (HE provider) performances against a sector benchmark (median annual earnings post graduation), display the difference against the benchmark (£) and clearly highlight the subjects performing above and below the benchmark. (blue v’s grey). #barchart

Interact with the viz on Tableau Public:

LEO Provider vs GB Sector earnings_#RealTableau

Provider vs Serctor Median Ann Earnings by Subject area_realTableau

1 – #RealTableau

2. #RealTableau viz. This visualisation provides insight into the distribution of earning differences in relation to benchmark earnings for a particular subject. The business wished to understand a providers positioning within the range of differences in earnings across the sector, the viz allows for the selection of a specific provider (displayed as a larger dot in the plot). #boxplot

UEA difference

2 – #RealTableau difference

Punch the time card and head for home.

Que #dataviz by night

3. Searching for a better vantage point to see the data. Whilst the established #RealTableau vizzes provide insight into the relative performances of a single HE provider against a benchmark (per subject area), I wanted to get a better sense of the whole sector in one visual, so I started to try a few things out and explore the data.

This working viz provides you with actual earnings, difference AND the provider names, yet still this only looked at a single subject area of study at a time. I really want to get an understanding of the differences in salaries by provider and region in one visual.

sample bar

3 – Searching for the stat

4. Looking for inspiration I posted a few visuals to my friends and fellow #VisualisingHE comrades to see if they had any bright ideas.

Dave posted back with a short and sweet…. “I’ve got an idea but it will mess with your head”… #radialbarchart

daves radial idea

4 – Looking to #VisualisingHE for inspiration

He was absolutely right, a lovely challenge and a chart type I haven’t yet tried in Tableau, this should be perfect for presenting a ‘difference’ in earnings. Slightly less functional than the bar (4) but much more fun.

5. OK I know why I haven’t done one before… Math! Luckily Rajeev Pandy has written a fantastic blog about it and Charlie Hucheson had done a great take-apart Tuesday on a Radial Bar chart which should, in theory, allow for some pretty crude blind method copying.

Evening 1: didn’t go so well – more reading required! Confirmation that I need to understand these calculations rather than simply employing the blind copy and paste method! #notabigloverofunderstandingmathbecauseimreallyjustamusicanandloverofstorytelling

Frustrations messaged to team #VisualisingHE, met with encouraging words and support! Cheers peeps!

Que a bit more googling and a little read of Charlie’s blog to see if I pick up any pointers as to what I really need to understand and what I can just gloss over!

6. Evening 2: and the light bulb moment…………The issue was not the calcs, but simply that when reshaping the data – duplicating it up adding a reference id of 1 and 0 (necessary for this chart type), tableau brought the 0 and 1 identifier in as a number, you need to make this a dimension not a measure. Once this was on the worksheet, I got magical fireworks going off. #Wine


6 – Light Bulb moment

Happy faces and gifs galore from #VisualisingHE

7. Understanding the calcs and working out what they are doing in the chart (inner and outer radius). #justplayingandmakingprettypictures


7 – Pretty pictures

8. Utilising the ‘combine fields’ functionality in Tableau to get control of the lines and groupings of the data.

region coloured

8 – Controlling colour

9. Had a bit of a play with sorting. Firstly I wished to see if I could bring to life Dave’s drawing (4) but also wondered if it would read better if the differences were sorted by +/- sector median. #seashellaccidentalviz


9 – seashells and sorting

10. One big picture… Explorations in trying to present all the subject level data and the regional grouping splits together in one viz? #onebigpicture #fireworks


10 – One big picture

11. I love accidents! Whilst playing with the viz messing about changing the chart type I stumbled on changing the line chart to be a square, oh how pretty! #accidentalart #uselessbutpretty

when vizzes go funky

11 – #Accidentalart

12. Back on focus. Having settled on presenting the regional groups by use of colour, I set out on multiplying the sheets, to create a grid of radial bar charts in small multiples. I did need to unpick the ‘normalising calc’ in the viz to set it to a max difference for all the charts, so that they could be compared together in one viz.


12 – Pretty Happy with the look and feel

Next steps: A message out to #VisualisingHE seeking thoughts and comments on what’s missing in the viz. And help sizing the viz.

13. The value of friends to critique your viz is something I never underestimate. Dave sketched out his thoughts on the need for a top section of the viz to allow me to annotate the viz, describe how to read the chart and allow for space for the interactive highlights and filters to have their space in the viz. Elena on hand for #Vizoftheday advice on longer form vizzes and feedback. #whatthisprojectisfor

how about this final iterations

13 – Feedback and further thoughts

14. Ideas and comments. (13) Brought to life in Tableau.


14 – Added context

15. Funviz. The finished viz | LEO Provider vs GB Sector earnings_Infographic (click to interact with viz in Tableau Public)


15 – Fun viz

Hope this post has been interesting, and you have enjoyed following my flow of ideas and iteration with this particular dataset #RealTableau to Funviz

Adam #VisualisingHE



Introducing LEOs Graduate Earnings

What or Who is LEO you might ask?

“Longitudinal Education Outcomes (LEO) data enables us to know how much UK graduates of different subjects at different universities are earning now, either one, three or five years since graduating. It does this by linking up tax, benefits, and student loans data”. (WONKHE beginners guide to LEO).

Yep you read it right, LEO is based on HMRC tax data for ALL UK graduates working or
claiming benefits in the UK, NOT a survey like the long established Destination of leavers survey (DLHE). LEO data brings together information from the Department for Education with employment info, AND benefits & earnings info from the Department for Work and Pensions and Her Majesty’s Revenue and Customs.

What does this experimental data do for us?

The figures allow us to compare variation in employment and earnings according to the subject a graduate studied and by various demographic characteristics (including gender, ethnicity, age, home region and prior attainment). LEO also reports employment outcomes according to the specific institution that a graduate attended. Previously, such granular information was reported only as a snapshot six months after graduation, so we (the sector) welcome this much more long-term and detailed data.

This richer information could prove immensely helpful to prospective students. It should also be very useful for universities (Me as a business intelligence analyst) and our efforts for strategic interventions to support the employability of our students. This data may help tailor the investment in the years ahead and this data will help us target our efforts to where it is needed most.


(paraphrased from various readings and publications – HEFCE/WONKEHE/UUK)

There is no doubt that this development is innovative, but for it to be helpful we do need to recognise the key limitations (and there are a few!). Due to the collection method of tax records, most of the earnings and employment figures published exclude graduates who are self-employed. As per the publication release notes identify, the exclusion of self-assessment data has a particularly large impact on ‘arts’ graduates and, therefore providers focused on delivering art subjects have a larger than average proportion of their graduates are self-employed, therefore provider level and subject level Median salaries may skew the real sector picture.

In addition, the data excludes graduates who are working abroad.  Neither can they account for whether a graduate is in full- or part-time work, or for the region in which a graduate currently works. All these limitations mean those in well-paid part-time work could appear to be earning very little, providers operating in challenging local economic conditions could appear to produce graduates with below average employment outcomes even if their graduates’ employment rate is substantially higher than the regional average. – detail detail detail.

Frankly LEO does to some extent, raise more questions than it answers at this point in time, but as development goes, experimental and innovative it certainly is!

Limitations aside

#VisualisingHE hope to provide an intro to the dataset and hopefully glean some insights into what the data may tell us given the numerous caveats and limitations the data comes laden with.

By way of an introduction to the dataset, please find below a very simple (and slightly tongue in cheek) #Coffeetableviz that presents the Median Annual Earnings 1, 3 and 5 years after graduating per subject area of study.

Are you Earnings Focused

If you wish to see the absolute figures (in tooltips) or explore a comparison of Female | Male or Female+Male pay differences between subject areas, take a look at the interactive viz: Are your earnings focused?

Are you earnings focused – Takeaway

The TOP5 earning subjects area are:

  1. Medicine and Dentistry
  2. Veterinary Sciences
  3. Economics
  4. Engineering & Technology
  5. Mathematical Sciences

Question: If I were to do it all again would I follow my love for the Arts, or my wallet and choose a subject more financially rewarding like the TOP5?

If I’m honest, I think today you go to university for a different reason I did some ‘X’ years ago, and rightly or wrongly I think i chose to read a degree for LOVE not entirely focused on post degree EARNINGS. However with fees being what they are I don’t think students have that luxury any more, graduate destinations and earnings are at the forefront of applicants minds before even setting through the door of HE.

What would you do and would you do it differently second time over?

Below is an exploratory viz again focusing on the Median annual earnings 1, 3 and 5 years after graduating created by Stephen encouraging you to filter the visual by gender and by provider. The viz provides you with a clear overview of the subject level earnings of graduates compared to the sector median earnings, provider by provider 1, 3 and 5 years out. The viz then encourages you to take a delve into ‘a’ subject area plotting the distribution of earnings provider by provider.

I’ve had a lot of fun investigating this viz, curious to know how graduates earnings of various providers perform against sector median salaries and other providers (i.e does it matter what Uni you went to?)

Annual Earnings

Link to viz: Exploring Annual Earnings in LEO data

Exploring Annual Earnings – Takeaway

  • £20,800 – it’s the magic number……Oh yes it is….

“The big number that a top-level analysis of this plethora of data will be compared to is probably £20,800. Why? Because according to the Office for National Statistics, this was the median salary for all 25-29 year olds in work in 2014-15.” – WONKHE winners and losers


Next I specifically wanted to take a peek into the gender pay gap debate and understand how this dataset would help display the problem, here is what the data shows:

The Gender Pay GAP

For a tinker with the interactive viz (and much clearer look at the image): The Gender Pay GAP

Pay Gap – Takeaway

  • Female earnings fall short of Male earnings in 83% (19/23) of subject areas (focusing on 5 years after graduating).
  • 3 years after graduating Females Median salary falls short of males earnings in 74% (17/23) of subject areas
  • 1 year after graduation this figure is less noticeable but still lower than Male earnings in 70% (16/23) of subject areas.
  • It’s not all bad news, in the subjects of Economics, Mathematics and Mass Communications & Documentations Females consistently outperform Males in graduate earnings in all three assessed years after graduating (1, 3 & 5).

Hope you have found this intro to LEO useful. keep #VisualisingHE in your sights for our next foray into HE public datasets.

Sector discussions and links to general reading on LEO

General reading:

Demographic discussions

Source data: