Where Trees Grow Upside-Down

If you'd asked me just a few months ago, I'd say with utter confidence, and not a little smugness, that yes, in fact, I do know what a tree is. I've climbed my fair share. I've wandered through a forest or two. I mean, I'm no expert, but come on; I know trees.

So you can imagine my confusion when I ran across this familiar word while listening to a podcast about computer science. There, a "tree" is used to describe something called a "nonlinear data structure". I looked up pictures. It didn't make sense. The "root" is at the top of every diagram, and the "branches" point down. Why? Why call that a tree? Maybe the top should be called a "stump" and the rest of it are roots? And what are those "nodes" - fruit?? I don't know much, but I absolutely know what trees are and that's not a tree!

Not A Tree.

If you can't tell, I don't have what you'd consider a "typical" technical background. And I hate feeling dumb. When I run across tech concepts that are treated like normal and yet make zero sense to me, I tend to get defensive. It's frustrating and embarrassing to feel like everyone in the room knows something I don't.

And yet, here I am, trying to teach myself computer science concepts through podcasts. It's part of my prep as I get ready to start a technical boot camp on Data Analytics. If I'm going to make my way in this nutty career I've come to love, I'm going to have to get used to being wrong about stuff.

I graduated with a degree in Anthropology and nothing at all to do for money. My first admin job bored me to tears. I think my manager felt sorry for me. When the IT group downstairs got new reporting software, he let me go train with them. I picked it up so fast they let me design dashboards, train colleagues, even study the relational database in the back-end of the tool. When my manager's boss told me to come back to my old admin desk, my best friend who'd moved to Northern VA helped me land a job in DC that required experience with the specific tool I'd just learned. I send a thank you gift to my manager and set off to launch my technical career!

Then things got sticky. My new job didn't actually let me access most of the software I'd gotten to use before. As a federal contractor for overworked and understaffed HR offices in the DC metro area, you don't get a lot of fancy tools.

Instead, I found a new challenge: federal databases. My clients' data are stored on legacy systems whose maintainers have long since retired. There's no time for deep-dives, they're on the hook to provide Congress with a million reports no one reads, and the only "tool" they have is a formula-infested Excel workbook some consultant created three years ago where one single bug can hang up the whole system for a day.

Solving their problems felt really rewarding, though. I'll always remember the first time my very basic technical skills came in handy: Someone called my desk from an EEO field office, one of the specialists tasked with making sure underrepresented communities get a fair shake in the federal workforce. She was having a hard time understanding some demographic data. I was the EEO office's only analyst, so I answered. We had a nice chat. I walked her through the basics of a pivot table, over the phone, and verified what she saw by working through the same data on my own government-issued computer.

Finally, she figured it out. Not only did she get the numbers she needed, she could also see where they came from and how to find them again. The specialist thanked me effusively. She said it was wonderful to talk this through with someone so understanding. I didn't tell her, but it really wasn't hard for me to empathize... I'd only just learned pivot tables a few months prior to her phone call.

That feeling, solving tech problems for non-tech people who spend their time helping others... I love it. I really do. But after a while it started to feel overwhelming. I knew there had to be better solutions to their problems. There had to be ways to make my job easier and my data more accurate. I just had to find it.

Turns out, searching the internet for advice on building up "data" skills in the early 2010s is a really terrible idea. I made myself crazy trying out different LinkedIn Learning courses on statistics and Python and a million different ways to define "AI". Someone said I needed to get a Github account, so I stumbled into that morass with zero knowledge of what a "git" even was. The search for knowledge left me feeling defeated at every turn. I changed jobs four times in five years trying to find some sense of self in the data world. Even the job descriptions couldn't decide what my job really was.

It was time to change things up. I realized that most information that sticks in my brain comes from relationships, from talking to people. These days, it came from the next best thing: podcasts. I searched my podcatcher for computer science, data, programming, coding, all the key words I could think of. What I found was a whole genre of tech people who actually enjoy talking to people... and a lot of them knew what it was like to come into the tech world from the outside.

That's where the "tree" concept came from: the BASE.CS podcast is a genius piece of work where a "code newbie" and a computer science "expert" talk about computer science concepts and break them down for those of us who never got to sit in on a CS class in school.

After getting over my indignation at hearing a new definition of a word I thought I understood, I started actually trying to learn it. A "tree" in the world of data is a type of data structure - a way computers hold information so the machine can find it quickly when you ask for it. This tree, from what I gather, shows a "nonlinear" type of data structure: it stores bits of data as they relate to each other using "nodes" (the root) and "branches" (the... branches?), instead of lining up each bit of data in a row and having to go from point A to point Z in order to find the bit you need.

(The terminology doesn't bother me as much anymore, to be honest. At least it's WAY better than those "master/slave/daemon" relational terms I run across in old Stack Overflow posts. If anyone ever wonders why the world of tech is so very problematic, just look at the words its original citizens used to describe things. Good Lord.)

Podcasts like BASE.CS and its parent project "CodeNewbie" helped me learn the language tech people use to describe their world. The way they talked about tech stuff also helped me feel less stupid about it all, to be honest. Then I found "We Belong Here", a show that brings on guests who discuss what it's like to learn to be a developer, a programmer, a data scientist, a member of the tech community, without having the typical education or experience the industry seems to expect you to have from jump. And yet, they made it. They got the help they needed and get to do the work they love.

I needed every bit of hope I could find from these shows. I came into my career with a solid background of writing, researching, and qualitative analysis. I am a "soft skills" expert. Now, after five long years of translation errors, I am finally finding the language I need to ask the right questions and get to the harder stuff.

I enrolled in Thinkful's Data Analytics flex-scheduled boot camp. I start next week. It's intense, but it'll set me up with a mentor twice a week and a whole Slack community of newbies like me.

This boot camp promises to be a LOT of work. The curriculum includes skills I've tried to develop on my own, like Excel and Python, but connect them with coursework designed to add to my understanding and put it all into the context of data analytics. It's not enough to build on what I've been doing so far. I need to retrain my brain.

I come from a world where trees grow in a forest with roots in the ground and branches reaching toward the sky. I've got to make room for another kind of world now, another language with its own syntax and grammar and interconnected meanings. In the world of data science and analytics, I'm going to have to uproot a lot of previous assumptions to make the most of my training.

So! That's the plan. And this blog will be my semi-weekly account of how it goes. I invite you to come along for the ride.