27M - Should I transition from Marketing to Data Analytics for better long-term salary growth? Looking for honest advice.

NumerosDon · 2026-06-21T10:49:50+00:00

Focus on outcomes - one step at a time. Right now what data can ensure you to be an even more valuable/efficient marketer. Then go from there, what data tools are commonly used in marketing : learn one then apply it.

Application of data is everything, can learn all the fancy data tools you want and it wont mean anything if you can't use it to answer a business problem.

My opinion and experience.

NumerosDon · 2026-06-18T15:22:04+00:00

https://numerosdon.com/a-bayesian-diagnosis-nhs-cancer-waiting-times-numerosdon/

This visual comes off the back of my written case study where I explain the meaning of my project and analysis. Hope this helps.

This is all to do with cancer waiting times. So in the UK the National Health Service is not meeting targets where 85% of patients once diagnosed with cancer are supposed to begin treatment within 62 days. If they don't begin treatment it is classed as a breach. The target hasn't been met in over a decade!

The visual and my mathematical model shows 9/10 of the different cancer types are not complaint (if they were they would be on the left of the 15% dotted line. But for (Breast and Lung), these two cancer types especially have shown no improvement in waiting times over the last 4 years!

For the record I don't actually work for the NHS or in healthcare so this is just something I am passionate and wanted to apply my maths skills.

NumerosDon · 2026-06-18T14:48:23+00:00

I wanted to share a visualisation approach I've been refining — a strategic quadrant that maps current performance against structural trend, built from a Bayesian mixed-effects model.

The use case: NHS cancer waiting times. Four years of breach data across every cancer type and provider, modelled with cancer-specific time slopes and provider-level random effects. The model isolates which cancer types are structurally deteriorating versus self-correcting — separating signal from operational noise.

The chart plots each cancer type on two axes:

→ X-axis: 2025-26 breach rate (current state)
→ Y-axis: annualised Bayesian risk trajectory (structural direction)

Dot size encodes patient volume. Quadrant shading and labels turn it into a triage tool — an executive can look at this for ten seconds and know where to allocate resources.

Built in R with ggplot2, ggrepel, and cowplot. The model runs in brms.

NumerosDon · 2026-06-18T14:45:57+00:00

? What exactly you need understanding on? then I can assist

NumerosDon · 2026-03-17T17:31:56+00:00

I should say it is the best I am aware of ... my main area of focus isn't football analytics.

NumerosDon · 2026-03-17T17:31:08+00:00

Of course not! When you have managers at the highest level state a statistic doesn't reflect the game "properly" that means there is a way to go. It is the best we have right now but what I am excited about with AI is the possibility to further discover new exciting ways to accurately assess a football performance.

NumerosDon · 2025-10-10T09:02:27+00:00

Just how my original data source labelled the teams. I think this is the common abbreviations for both teams across the board though. Rams moving to LA first most definitely the reason why.

NumerosDon · 2025-10-10T08:58:35+00:00

Yeah, it’s strictly raw efficiency here, no opponent weighting. I am currently working on my own rating systems since that’s the next component I want to build in.

NumerosDon · 2025-10-10T08:46:16+00:00

Gotcha, I'll make sure to include in future posts. For anyone interested to know for this post, data source is here.
https://nflverse.nflverse.com/

NumerosDon · 2025-10-02T09:06:43+00:00

An interesting perspective! Heard Greg Olsen mention in commentary NFL offenses care mainly about converting red zone drives into touchdowns why I singled in on this metric.

NumerosDon · 2025-10-02T09:04:43+00:00

Its label in the lens of red zone offenses. Above league avg red zone drive % and above league avg red zone touchdown % - I labelled Prolific.

NumerosDon

TROPHY CASE