Infographic Resume

I have been on the job market since September.

The academic job market takes just short of forever, and is a HUGE commitment. As a result, I have become curious about other job markets and how they work. I essentially know exactly nothing about how to get a job outside academia, despite training for a PhD for the last 5 years. Correction. I knew nothing about the market ‘out there.’

In this blog, I take an industry professional’s resume, and turn it into a cute, easy to read infographic using picktochart, a mostly free online drag and drop application that creates a platform on which you can build an infographic.

What is an infographic?

Customer Magnetism explains it really well, with pictures. Pictures are essential to infographics, as ‘graphic’ is the central part to displaying the ‘info’.

So when Oscar Rieken, Lead Software Engineer and All-Around Awesome Person, asked if I could make his resume into and infographic, I said, “Of course I can. Just tell me what you want featured, and what colors you want.” And we were off. I even got his permission to write about it here!

After we established the rough parameters for what he wanted his resume to display, I set to work making different visualizations that we could choose from.

Programming Languages Beta

The above shows the way we decided to display Oscar’s skills, after one or two trials with other charts. Each bar displays languages he knows and the level, (1-5) at which he places himself in experience, with 5 being “nothing left to learn.”

We decided this way, on a basic blue/green color scheme that we used consistently all the way through the infographic.

All of Oscar’s Skills, AND Tools are displayed this way.

Next, and perhaps the most difficult, was deciding how best to display his work experience. Here are some trials we played with:

Experience-visual-sample-1

Here, we can see Oscar’s current place of employment, and the skills/tools he uses most at this position. But the circular chart is very difficult to read, and the key is bulky and strange.

Experience-visual-sample-2

Next I tried displaying them on a similar chart that would visually match his skills and tools above. It looked better, but still didn’t necessarily warrant a display in an infographic.

Home-depot-visualization-2

I asked him to split his skills and tools up into the way he spends his typical day. Oscar created a spreadsheet for me with data that indicated that, in his current job, he spends 40% of his day in development, 30% in coaching, and so on. Using powerpoint, I arranged his skills and tools by logo, and put them into a ‘tech stack’ inside a container, which here, is a circle. In earlier jobs, I used the shapes of the states and countries he worked in, which worked to meet Oscar’s visual tastes.

Thoughtworks-Brazil-Tech-Stack

 

Here, you can see the skills and tools Oscar used when he worked in Brazil. Creating the graphic was incredibly easy, and I just used some simple formatting in PowerPoint to create a .jpg that I could then upload to piktochart and insert into the infographic.

It took a lot of work, and a fair amount of consulting to get this the way Oscar wanted. But eventually we completed it, and he was quite happy with it, especially after I added the little robots as accents. Oscar is really into robots.

Below, is the infographic in its entirety (with full permission).

Immigration to Atlanta: Historical Data Visualization

This week before Spring Break, I was fortunate enough to get put on a new project coming out of the History Department. Working with Dr. Marni Davis, a team of SIFs and I are helping Marni to organize, visualize, and present her data on immigrants to Atlanta. We are beginning with data in the late 1800’s, and working up to the present. Currently, we have a lot of data up to about 1930, which is what I have been working with.

To begin, Marni supplied us with a spreadsheet of data with about 1600 entries on immigrants. These entries have data points such as name, birthdate, port entry city, date of immigration into Atlanta, date of naturalization, country of origin, address in Atlanta when applying for citizenship, and so on. Because I have experience with Tableau (a program which can produce beautiful data visualizations), Marni asked me to take some of this data and create charts that we could put up on her new GSU sites website dedicated to the Immigrants ATL project.

To begin, I decided to try and create a bit of a story that showed very simple data. For example, in the first figure, I show the m/f immigration difference, where you can see that men immigrated to Atlanta 100x more than women in this period.

Immigrant-Gender-1

In the next figure, you can see a comparison between when Asian/Pacific peoples were immigrating into Atlanta, versus when Central Europeans were. Additionally, you can see that there were far more Central Europeans immigrating to Atlanta than there were Asians.

Region Immigration Comparison

The visualizations are really wonderful in that they make the data much easier to mentally process and compare, and they will be easy to present in any venue. Further, we can arrange any data visualization into a kind of story that we want the data to tell.

Currently we are working to create more graphs like you see above, but also to incorporate some maps that show data such as average age of immigrants into Atlanta from various regions, countries, and cities.

While these visualizations are gorgeous, and not difficult to make, there are some issues that arise to complicate matters. For example, there is not a year of entry for absolutely every immigrant to Atlanta. In order to create the graphs, I have to omit whole people who may only be missing one piece of data. Further, I learned the hard way, that Tableau does not read the formulas that we make in Excel. I had a lot of loading issues and eventually found that Tableau is set up to do my computing. After several hours of trying to load, visiting the Tableau sub-reddit, and doing a lot of Googling, that I could subtract the Immigration year from the Naturalization year right in the graph.

Next, we are going to build out more visualizations, meet with the rest of the team to see what they are working on, and hopefully create a really robust and face-smackingly wonderful set of data that Marni can present in any venue easily as she works to collect and manage all this data.