Lessons I Learned As a “Handywoman”

And how it relates to Data Science

picture of nails, hammer, paint brush
Photo by Julie Molliver on Unsplash

Eleven years ago this month, I founded my first company. My youngest child was 10 years old and in the fifth grade, and my two other children were in the 7 grade and 9 grade. There was a bit of breathing room in my schedule and more time in the day for a job. Since I still wanted freedom and flexibility, I started a “handywoman” business instead of taking an office job.

Fast forward to 2020 and I’m starting on my next adventure in life: searching for work as a data analyst/scientist during a pandemic. I find that the two seemingly dissimilar jobs have commonality despite the outward appearance of disparity. Here’s my top 5 list of “words of wisdom” that apply to data science and home improvement:

Don’t tile yourself into a corner

Analyzing data requires planning, too. Again, it’s wise to map a course carefully and diligently. Make time to ask: what is my goal with this data? What am I trying to achieve? And how will I get there? Time and energy are wasted if one doesn’t think about the end product.

Cleaning (ugh!) goes a long way

Similarly, in data science, it can be tempting to jump into a data set and start plotting, graphing, visualizing, or modeling to see what’s going on. However, if there are NaNs, missing values, mistakes in the data, or improper data types, you’re not going to get too far. Time and patience are needed to thoroughly clean and prepare data so you can create a beautiful finished product.

Do your research on what is behind the wall

In this instance, though, I didn’t do a good job of researching the pitfalls of the job by thinking about what was behind the wall. If I had done a thorough job of thinking about that particular wall and project I would have investigated what was above the living room on the second story (the en suite bathroom) and looked into the basement to see the water pipes running right through the living room. The client was very understanding when I punctured their water pipe while nailing in the molding. (The damaged pipe was repaired by a licensed plumber.)

The moral of the story that applies to data science is that every project is different even if they seem the same on the surface. It’s a worthwhile endeavor to take time to really look at the data and determine how it is unique from other data sets you’ve previously worked with. Are there different tools and libraries needed? Is there research that can be done to enhance the understanding of this project?

There’s more than one way to remove a screw

In data science, there’s usually more than one way to do something — it may not be efficient, elegant, or precise.

Take a break to get back on track

Sometimes it’s possible to power through bad situations and think of solutions on the fly. It’s that “tough it out” mentality. However, there’s nothing weak about walking away for a break to regroup and ferret out a solution. In the popping-off-the-wall tiles, the break allowed me to come up with a unique solution to remedy the situation and finish the backsplash. With data analysis, when the mind is tired, making careless errors and not focusing, a break away from the data and computer can work wonders.