A growing fad I've seen on the interwebs is creating Resume's in non-traditional software. Whether it be a personal reactive Webpage, or an origami bird, people are eager to distinguish themselves via the delivery of their resume. And fair enough, with how competitive the hiring market is, that could be the requirement to get the attention of an inundated hiring team.
More personally, it's people releasing Tableau based Resumes. Showcasing their professional achievements through elaborate charts. Honestly, this is a domain where I swear a PDF file with words is going to work better.
But not wanting to be left behind, nor to let me grouchy self get in the way of good ole fashioned progress, I put together my Tableau resume. The hardest part is not inundating it with words. I've put most of the pertinent information in hover overs, but feel like I've left enough top line information that this is consumeable without Tableau.
But I'll let you be the judges. Here is the URL, with the JPeg below.
A right to Canadian passage is being able to complain about the snow winter after winter. It's also customary to confidently announce to anyone who will listen that "this is the most snow we've had in a long time". But how short our memories are. Slightly annoyed with the rash claims made by friends and neighbors, I again turned to data viz.
Environment Canada does a great job at keeping detailed weather files from most Canadian cities. Unfortunately the level of detail and measurements documented vary by polling station. I had plans of expanding my analysis across Canada but it was time consuming to find formats that would cooperate.
I also doubt the accuracy of some of the reporting. Especially as I was tracking this live and comparing to my front lawn. But I'm sure there is some system in place.
The biggest challenge with this piece was creating a static way to show 12 years of data. I'm into making tool agnostic reports lately that can be widely consumed. It's a learning curve but it's a good direction to head. The balance of putting not too much nor too little information is tricky.
But, with that said, no tableau link for this one as I got to all fit in a nice Jpeg. Here is a look at 12 years of Fredericton Winters.
Far too often I find myself trying to solve my real life problems with data visualizations.
Here's an example. My wife has been put off work for a while. She's got a touch of cabin fever and by the time I get home she's ready to get something exciting going.
We're both into board games but without other people there are only so many options. To boot, we've recently been introduced to the joy of Co-op board games.
So I was tasked with combing though boardgamegeek to find some great 2 player, co-op board games. But why browse a website when you can just, you know, viz it. Thanks to a brilliant datasouce on Kaggle, I was able to put together the ultimate, filterable board game search tool. I went a bit flexible with the design, opting for a 5000 point bubble chart. When it's for internal use you can take some liberties.
Here's the URL if you want to do some drill downs of your own. And you'll want to, because the static image does not give you much in terms of detail!
This is something I wrote for my LinkedIn feed, but thought you guys would probably be more interesting in it than anyone there. Two of my clients have Red Brands and they both do KPIs differently, which got me thinking of this topic. Anyway... read on if you want.
Ah, red. The colour best known for Christmas, blood and when your KPIs have gone horribly horribly wrong. It's also the primary shade in some of the world's biggest brands such as Coca-Cola, Toyota and Verizon.
Well, what do you do when you have to balance a company's branding with a functional KPI color palette? In one of the most pedantic things I've ever written, I present to you the dos and don'ts of building your red branded KPI dashboards (please don't tell my younger self this is what my life is now).
1 - DON'T use Red for bad. If you're presenting to an executive, a real company man, you don't want their brand to mean things are in the gutter. You might not care, but it will get noticed. Instead use a black or gray. Something neutral.
2 - DON'T use too much red. This is dashboard not the end of a Jack Bauer rampage. Too much red is an affront on the visuals and is almost impossible to work with.
3 - DON'T corner yourself into using colours that are relevant to the competition. It can work, but if it doesn't, throw it out.
Here is the DON'T dashboard
The numbers line up to my points above. The headings contain a lot of red, it's all branded but hard to take in. The charts on the left use red to represent the brand while the charts on the right use red to represent bad. Which could create the mental fallacy of Brand = Bad. It might sound silly, but that does happen. The bottom right bar chart uses political party colours to help readers instinctively know what is what, but it's bright and out of place to the rest of the palette.
Here is my DO dashboard, with some of the changes explained afterwards.
You'll notice right away (hopefully) that you're not blinded by red. But there is still enough to consider the dashboard branded. Replacing the bar chart with a line helps as it gives more white space. The right hand tables have a gray base and, where it's obvious, red as positive. I've not done this for the by province chart as it was not intuitive here that red was good (since there are no labels or legends).
I think my favorite change is to the % of Revenue by Year. I love color coding with party colors as it's intuitive for the user. Unfortunately I think it distracts. This version flows much better and you don't lose much in terms readability.
So. That was bland. Not many get the joys of restless nights thinking how do make an appealing red branded dashboard. But maybe, just maybe, this will inspire someone working with palettes in the future. And if not, I appreciate you sticking it out with me anyway.
Oh and if you'd like to see the full dashboard and be able to see other parties, click here
What is the only thing that stands between you and your wildest dreams? If it's a constant stream of small donations from strangers worldwide then this blog post if for you. Thanks to a Kaggle Dataset amalgamating 392,000 completed Projects we've broken down the easy 5 steps between you and your unsustainable dream.
And if this freeze frame doesn't lineup to your situation, then here is an interactive, choose your own destiny version.
My wife and I have been spending more time than usual looking up baby names. Going from website to website, hoping to find some inspiration.
This didn't appease my analytical, data visualization, desires. To combat it, I did some digging. The social security administration has interesting data on male and female name popularity in the US dating back to 1880. I used this to not only see a list of the most popular baby names over the last 150 years, but to see how my name has fared the test of time.
In Part one (which you can see on LinkedIn) I explained how to get this data and what to do with the important information.
This is part two, a look at some of the fun stuff.
The first thing from the file that I was immediately curious in was my friend count. Facebook lists out the added and removal date of all your friends.
One interesting thing in the data is your added friends list doesn't include people added and then deleted, which is unfortunate. But oh well. Life goes on. Below you can see a common trend. Gradual friends added and big days where friendships are ended. Also note the chart has a different axis's. I'm not a big fan on doing that but sometimes it just makes sense.
Messaging was a bit trickier but thanks to an awesome tool that can be found here I was able to get all the messages into a mostly useable format. Besides looking over some past antics, I wanted to see a few interesting things.
One theory I had was that, in my infinite maturity, I would now be typing sophisticated messages instead of quick whips. But that turned out to be a miss.
I underestimated something. In 2011 I got my first smartphone (late to the game, I know). Which means my primary messaging device suddenly sucked to type on. And thus, the length plummets.
What I really wanted to look at was my history with individual people, and one of the most interesting patterns was my wife and I. We see each other a bunch (obviously) and primarily use Messenger to send each other images when we're lying on opposite couches. But there are some noticeable exceptions.
There's a lot more you could do with the data, but finding any other pattern was pretty challenging. The creators had a really great view that you can see and use at this link
It's probably obvious by now that my go to tool is Tableau, but I have dabbled in a few others. G2Crowd has an excellent annual report that surveys end users and aggregates the results. See how Domo, Board, Qlicview and Tableau rank in the BI landscape. Less exciting, but also see if you can spot my first ever viz-in-a-tooltip.
Shut up and show me the dashboard
I've had an interesting use case over the winter.
Next week I'm competing at the Quebec Winter Triathlon. It's my first one. I've spent almost as much time looking at last year's results as I have training. And as I'm looking at last year's results I can't help but compare my current training to the field and figure out how I'll fair (spoiler - not great).
And I don't think this is a totally crazy use case of analytics- looking at a dataset and wanting to input your own data.
Let's see what that might look like.
First - the winter triathlon.
It's a 25 KM race that involves Snowshoeing, Skating and Skiing - in that order.
There's an elite and an age class category. Obviously I'm not elite, so let's look at the Age Class. Last year there were 75 racers, the majority were men and somewhat surprisingly the 40-60 year olds dominate.
I took a look at each of the disciplines to see how people ranked compared to their overall finish time. A fun side note - the finish time also includes transition time, so Snow+Skate+Ski not = to Finish time. Which threw me for a long loop when doing data validation.
Somewhat interesting is that the Ski is most correlated to the finish. But that kind of makes sense considering it takes the longest to complete.
I can only spend so much time looking at this before selfishly thinking of myself. How would I have finished based on these results. Where do I rank in last year's race.
In Tableau you need a row in order to do anything. With this database I got lucky as they have a dummy participant's row. So I've simply hijacked that. What you can also do with Tableau's unions, is join a blank txt or csv file which will generate a new row.
My first bit of code is replacing the dummy row
IF CONTAINS([Name],"Tirage au Sort")
then [Name Param]
So if it's the dummy row, then replace it with the variable name, otherwise use the name. And you need to do this for every field where you have a field you want to import. For me it looks like this
And now to enter my abysmal times. The Snowshoe and Ski are easy. My snowshoe is brutal and I'm coming in at about 31 minutes. Hopefully that's due to my courses and fatigue or else I'll be starting well back. The Ski is significantly more competitive, I did 11 K the other day in 39 minutes which translates to a 33:10 9K. The Skate I have no idea. I've been skating but it's hard to do 11K on a 200 meter track in a thick crowd. But I used to be good at skating so I'll generously give myself a mid pack time of 29:30.
So those times put me 13th. Which is ludicrous. But a couple of things that I'm sure you're noticing as well:
It doesn't include transition time, which is why my distance ranks seem funky compared to the overall. That could easily tack on 5 minutes.
I haven't factored in fatigue of doing the three events back to back, which will surely be significant.
With this kind of format you can play with it as much as you want, tweaking your inputs to see how you compare to a field. It's a very interesting way to compare your current results to a historical dataset. .