How to be a better data scientist… by being an interior designer
By: Jacob H. Marquez
Tags: Data Scientist, Data Design, Data Visualization, Data Science, Data Science Training

Photo by Kam Idris on Unsplash

Like me, many data scientists did not start their careers thinking about statistical modeling. Or data, for that matter. Psychology is the degree I chose to pursue; creating social media posts was my first real job. Linear algebra was on my “stay away” list. Therefore, I can personally attest to the reality of imposter syndrome.

Plenty of resources are available for building technical skills in data science, especially as the field matures and becomes more defined. If you’re interested, I can recommend a few I found helpful in my journey. Working as a data scientist means much more than learning how to implement Python packages and build pipelines, however. It also requires non-technical capabilities, which some call “soft skills.” For these, I seek out expertise of people outside data science.

My partner and I recently remodeled our kitchen (read: we ate microwaved meals for two months). We worked with an interior designer, who is also a great friend, to create something in which we would be proud to host Martha Stewart–like brunches. Our designer’s ability to translate rough, curated-by-Pinterest ideas into the compelling “story” that is our kitchen today leaves me in awe.

I cook a lot more.

The experience inspired me to think about ways our designer’s expertise and skillset could be applied to my work as a data scientist. In the spirit of helping others who are also looking for ways to be a better data scientist, here is my list.

Find inspiration

[Me]: “Well, I love Pioneer Square and the industrial feeling.”

[Interior Design Friend, aka I.D.F.]: “Great! Let’s make your kitchen into a mini Pioneer Square.”

[Me]: “Minus the trash and rats — right?”

Creative artists find inspiration from the most unlikely places. Interior designers are no different. They may consider not only resources standard in their industry, but also fashion, landscapes, junkyards, and old school furniture (true story — lots of tears).

It’s all about pulling elements from various places and applying them to something new. Here are a couple of ideas to get you started:

Looking for a new color palette?

Pantone releases a new “Color of the Year” every 365 days based on what’s being used by trendsetters across industries. Their website also provides a “palette exploration” experience to show other complementary colors. Pick one or two to make your next visual pop.

Pantone releases a new “Color of the Year” every 365 days based on what’s being used by trendsetters across industries. Their website also provides a “palette exploration” experience to show other complementary colors. Pick one or two to make your next visual pop. What’s your favorite childhood movie?

Major production companies like Disney and Pixar have mastered the art of storytelling to captivate audiences young and old — so much so that there are core narrative elements they use that even have names of their own, such as “Handcuffed Characters” and “Epiphany Moments.” Data stories are no different in their need for narratives to get their points across, and so can often benefit from using some of these principles as well.

Data scientists are also artists. As such, they should look for inspiration — for their visualizations or models — in how other things are designed.

Don’t forget the classics

[Me]: “Subway tile? That’s so cliché.”

[I.D.F.]: “No. It’s classic.”

While looking for inspiration, remember there are standards that have stood the test of time. Bar charts, scatterplots, histograms — and yes, even pie charts — should not be overlooked when you craft your story. They may not seem exhilarating and innovative today but, just as mid-century modern furniture once revolutionized furniture design and lifestyle, the set of data visualizations we know and use today revolutionized how data is presented when they were first created. Their ubiquity today belies their importance because their familiarity increases the chances for viewer comprehension.

There are ways to enhance them, however, to make them more modern, compelling, and yours. That’s where “finding inspiration” comes into play. Here are some ideas:

Play around with saturation to make colors pop (think about how Pixar does it).

Take away the axes and use data labels instead.

Use the title of the visualization to state the key point.

Leverage Gestalt principles to group objects in place of reference lines.

Exclude chart elements that can be readily inferred.

If you want to add something whimsical, experiment with an icon or image to replace default points and bars. Because some may disagree with this as it might distract the viewer of the visualization, my suggestion is to master the basics first and then experiment on a project outside of work.

In these ways, you can start with the classics and then make them your own.

What’s your point?

[I.D.F.]: “What is the feeling you are looking to have when you walk into the kitchen?”

[Me]: “I don’t know. Typically, I just feel hungry.”

Every visualization, presentation, or research paper comes with a chance to tell a story. These stories are opportunities to elicit responses from their audiences.

Unfortunately, key points are too often buried in the aftermath of unguided creation. Visualizations and slides more suited for elementary school art class competitions. As a result, the viewer’s brain takes more time to comprehend what the visualization is trying to convey. In some cases, viewers may even leave with the wrong interpretation.

With visualizations, I recommend making only one point per chart. Adding one more can exponentially increase complexity and decrease comprehension. The aim is to direct the viewer to a quick insight and not showcase everything potentially interesting.

After choosing the point to make, make it easy for the viewer to grasp. Here are a couple techniques I like to use for this:

Look away…wait ten seconds…then look again. What is the first thing you see? Is that what you want your viewer to see? If so, great! If not, tweak.

Add one color to highlight a key trend; make everything else grey, black, or otherwise neutral.

Ask yourself, “Is this really important to know for my story?” If not, make it grey, black, or otherwise neutral — or remove it.

Tune your stories and visualizations like you tune your model!

Do less

After going through this process — starting with basics, generating ideas to add our personal style, and building the “that-would-be-cool” backlog — our kitchen designer posed her final question:

What can we take away?

Effective design is executed through intentionality. “A clean, uncluttered kitchen is a welcoming kitchen.” Keeping it simple allows the showstopper items, such as carefully chosen colors, shelves, or a fancy stove, to pop. Do the same with your data science projects.

It’s useful to keep two levels in mind to start with:

The macro story: What is the theme? Do all the major points support that theme? If not, put them in the appendix. Stick to three of four insights with two to three pieces each of supporting evidence.

What is the theme? Do all the major points support that theme? If not, put them in the appendix. Stick to three of four insights with two to three pieces each of supporting evidence. The visualizations: Determine the key points to highlight and remove elements that may otherwise distract the user. Try to stick to one key point for each piece of supporting evidence you would like to provide. In fact, perhaps a chart isn’t needed at all — sometimes a colorful number or word will do even better instead.

“Doing less” is about experimenting and taking risks. Play around with removing elements of a visualization to see whether the interpretation changes as a result. Before presenting your work to your boss or other stakeholders, ask a co-worker to have a look and describe it back to you.

Did they interpret it correctly? If so, fantastic! If not, CTRL+Z and modify again until it’s simple enough to be concise but detailed enough to be understood. Doing so helps to focus attention on the key points rather than on meaningless details that serve no purpose.

Go “design” your data science

I have endeavored in this article to tightly relate each section to the others, especially the last two. I hope this conveys my belief in the importance of spending time tweaking the final presentation of your work so that it is memorable and effective.

Knowing better statistical methods and experimentation design is fantastic, but not being able to elicit the desired responses from your projects might leave you feeling discouraged (speaking from experience). The next time you’re working on a presentation, channel your inner interior designer to create better stories.