#MakeoverMonday Week 33

Following up my Makeover Monday Tableau post for Week 33. It featured the travel information for the late Anthony Bourdain’s shows. The data was supplied by Christine Zhang (@christinezhang).

I enjoyed Bourdain’s shows and was saddened by his death, as were many others. For me, it was his humanity when traveling to places. He did not let his money, fame, or skills get in the way of good food and a good story.

For my viz, I wanted to show more than just the places and to see in more general terms where he went. In other words, going to Tokyo is fine, but where else did he go in that region? Oceania? Europe? Also, it would be interesting to see as a television production if the regions are grouped.

My roadmap was this:


Couple things in the details I need to learn still. Particularly “This is episode X of season Y and was the A of B shows in [Region]”. Still a process. Final edition and link to the interactive viz is below.


(I know that title sucked.)

Wanna go fast

I have a problem.

It’s the problem about acting like a toddler. The way I mean is when a toddler is learning to run, they must first learn to, you know, toddle. Then walk. Then after falling on many occasions, off they go.

Usually into the wall or glass door, but the point is that they run, even if for a little bit. They will adjust what they do – walk a little faster at first, then run more. Until they can run without thinking.

For me, I have an idea of where I want to be as far as my skills go. I want to get better in Tableau. I want to get better in Python. Learn R. Etc. The issue is that I start to look at everything I want to learn and so I go in head first without any sort of a plan read all the blogs while listening to the podcasts on the contests on Kaggle so I can work on my GRE while finding a grad school and I am very tired and somehow banged into a glass door.

Yeah. A problem.

Thankfully, I found a book sort of by accident called “So Good They Can’t Ignore You” by Cal Newport. The very short version (a longer description) is that to find work that we love and are passionate about, we need to first build our skills. This I understand. But I wanted it all now. I want to be doing these things – at least in the abstract. What I have not yet done, the piece I missed is what Newport calls “career capital”. Once you have the skills they can’t ignore, you gain capital to use to find better work. It is the opposite of following your passion into a business you aren’t ready for.

I found it refreshing and useful. Build my skills, through the demonstration of those skills you get the capital to get better work, and then you get better work. It’s pretty simple and I really recommend the book for a better and more detailed look – I am really not doing Newport’s work justice. However, it helped to focus my thinking and that is the important thing.

To that end, I have a plan. I have a way to go.

The rest of August is Tableau month. This means being part of #MakeoverMonday and at least attempting the #WorkoutWednesday problems. One hour per day will be to deep focus on my Udemy tutorials and practice.

Since I have a head start, by the end of the month I am hoping for more significant progress. By focusing on one skill at a time, I will be able to better improve overall, rather than having a “taste” of a lot without mastery or specialized skills in any one thing. Assuming a successful August, in September, I ride the snake. Python month. Stay tuned.

PS’s (think of it as the filler at the end of the podcast):

    • Not a red card. Maybe a yellow. This blog supports Everton. We will argue with you about this. For a while.
    • BTW, the links above are not referral links. I get nothing if you buy that book. If that changes, I’ll say so.
    • And lastly, Phish at Walnut Creek in Raleigh, NC. Webcast vid, until they pull the plug.


Hello and Welcome!

Hello there. Thanks for stopping by.

I will be honest with you – the format as we begin is going to be a little loose. Some will be posts about my data journey. Data visualizations will be part of the mix. I am also very interested in the role that data analysis plays in clinical trials.

Part of my real-world job is to evaluate sites and countries on (partly) the speed and accuracy of the data entry to our EDC system. I look forward to discussing this, in broad terms, and hope to have a discussion online about it as well.

Then again…some might be about books. Learning is something that I do and will work on as well and not all of it will be data related. Just as an example, I am currently trying to learn to speak Hebrew, which my partner and her family speak.

I hike and take random pictures. I run less than I should, meditate when I don’t skip, and go to yoga when it is convenient. Oh, just learned to ride a motorcycle. So there will be some of this, too.

A legit question: Why not just have a data blog and be done with it?

Because while data science and my journey will be a focus, it is not the entirety of me. I would like this page to be a conversation with other people. A bold goal for someone at the beginning, but bold goals will also be the point here as well.

The picture above was from Sunday. On Friday, I could not ride. Period. I was bold and now I have a license and the itch to do more. I will get into some other goals that I have in future posts. But let’s just say I’d like to start 100 day challenges for about 7 things – full two years worth of commitment. I need to take a breath and plan. Possibly more than one.

I am humbled you stopped to read this. Thanks very much. It’ll be a hoot.

Let’s get down to the nitty gritty; let’s get this show on the road.