Hi everybody, thanks for coming. My name is Josh Laurito, and I’m here to talk to you about the Intersection of three of my favorite things: python, New York City, and trains.
I’m super, super excited to post that I just completed my first full week at Gawker Media. I’ll be acting as Head of Data & Analytics for the company, primarily helping to improve their kinja platform but also supporting the editorial team at their media properties.
Any work-related findings or posts will be at the (brand new) Gawker Data Team blog: gawkerdata.kinja.com.
Late last year, I agreed to teach a master’s course in data visualization at the City University of New York.
In a closely related story, my blogging dropped to zero over the last few months.
Anyways, the class was a great experience, and I think the students did an awesome job on their visualizations. You can see the full list of projects here.
I snuck in one last data project for 2013: a d3 interactive map of the US banking system. You can play with it here. Please do and let me know what you think!
I was really happy with how the project came out. I also was really happy with Github Pages, which I tried out for the first time with this project. If you ever work with git, I can’t recommend it enough. I’m hoping to move more of my projects there in the future.
In a couple of weeks I am going to be teaching a class on Data Visualization for Businesses (you should come!) and as part of the class prep I started thinking about key metrics that my students may want to visualize.
After weighing some of the options, I settled on Revenue Per Employee, which has been on my mind recently. I want to understand what is the minimum revenue per employee that quickly growing companies can sustain? Continue reading
(disclosure- I am involved with and invested in Lumesis, which sells Muni credit & compliance software)
So Detroit filed for bankruptcy last week.
If you live in the US and don’t work in municipal finance, you probably barely registered this news.
And you probably think that it’s not that surprising that something bad happened in Detroit. I mean, during the recession a house there cost less than a car. So we all saw this coming, right?
Well, yes and no.
Yes, everyone in the muni market knew that Detroit was a poor credit. It’s not a surprise- that’s not why it’s a big deal. Continue reading
Analyzing the Analyzers is a recently published report by Harlan Harris, Sean Patrick Murphy and Marck Vaisman, documenting the results of a 2012 survey of ‘several hundred’ data scientists.
The report is free and just 25 pages of text, plus an appendix- you should read it.
The authors’ central contention is that there is not one set of skills that organizations should look for in a data scientist. Instead, there are four distinct skill groupings that you will find in the ‘data science’ world:
- Data Businesspeople: managers primarily focused on their organization and the bottom line
- Data Creatives: hackers who feel comfortable with the entire data pipeline, from extraction to presentation
- Data Developers: back-end and infrastructure engineers primarily working on data extraction, storage, and scale issues
- Data Researchers: academics, usually with a strong background in statistics
Just a note to the readers of this blog: you may have noticed that something is different.
Like, everything is different… Continue reading
I once asked my brother, who studied large organizations, what was more effective- the hierarchical, top-down organization of, say, Apple, or the distributed decision-making of, say, Urban Outfitters.
My brother said “both”.
Apparently, the best way to capture the benefits of hierarchies (order, coordination) and delegated authority (reaction speed, creativity) was to cycle between the two. There was generally no single best system for any one organization, not even for very large organizations with stable missions.
Change was best. Even though it imposes high switching costs, change is best.
That conversation occurred to me this week as I looked at the dashboard that I provide my team, updating them on the state of our business. My dashboards generally shift from being very simple to being much more complex, until we all agree it’s time for a different look and we burn them down again.
Now I’m starting over again with a new dashboard, and I’ve realized that this process has repeated itself enough that I really recognize a cycle, which I’m calling, super-creatively, ‘The Dashboard Life-Cycle’. It goes like this:
Creation: It starts very simply. “What are our three top priorities or KPIs and how are we measuring them?” A first cut of a dashboard might be as simple as 3-5 numbers, tracked over time. People look at it and say “That will do for now, I guess”. I always think of the dashing demo as a fine example of a dashboard in this stage.