Hi Raina! I'd like to appeal to your hard-won wisdom on The Adult World-- do you think it's possible to teach yourself/find online resources to help learn things like data analysis? I want to look at numbers and graphs and spreadsheets for money but don't know what resources are good enough to trick capitalism and bosses into hiring me for it!
Oh, yeah, 100% Source: I pretty much did that. So first the disclaimers: I did have a bachelor's degree in applied mathematics, and I did shell out for an MBA as my happy-divorce-day present to myself. I know that I really don't use either of those things in my day-to-day work, but hiring managers probably are considering them when they look at my resume. I'm also white, a native English speaker, and talk like an educated middle-class suburbian, which I'm sure also play into mangers' willingness to give me the benefit of the doubt. So my exact path may not work for you. That said, data, in particular, has several advantages right now:
1) Demand is large, and supply is small. If my department doubled in size, we could still not quite answer all of the questions the business leaders are asking of us. I don't think there's been a moment that I've worked for this company that we didn't have a least one job slot we were hiring for. In addition to making this a lucrative industry, it also makes it fairly easy to break into, because hiring managers are willing to accept far less perfect candidates if only they can get someone who knows something working on this project.
2) The field is changing very fast. No one knows what languages or software we'll be using a year from now, so it doesn't really matter that much if you don't know the one we happen to be using right now. Supervisors are much more concerned with "When it turns out we have to switch our entire reporting scheme to Scala, will you be able to learn that?" And in that context, it is incredibly encouraging to hear a candidate say that they once had to do a thing in Python but they didn't know Python so they Googled and perused Stack Overflow until they could do the thing. You are very likely to have a question at the interview that's something like "Describe a time you had to learn something quickly" or "What's your approach when you don't know how to do something?", and as an autodidact, you will have lots of examples for those moments.
3) There are lots of places that don't have anything at all. A person who knows how to put conditional formatting on a column in Excel would be an improvement on what some smaller companies are currently doing. If you can make a graph and code a vlookup() function, then you're an Expert!
4) the field is so new, and is changing so fast, we're still working out the distinctions between the assorted sub-fields. Which means you can start as someone who does data visualization, pivot to data science, change your mind and end up in data engineering, and then decide to do database administration instead.
So yeah. My recommendation is to search job boards for things that look like they might be what you want to do, and write down what the minimum qualifications are for each one. If you already meet 70% of the requirements, start applying! If not, make a li'l histogram of requirements they want and you don't have, and start finding ways to get them.
I, for instance, downloaded an SQL syllabus from some university class that had it publicly posted, and learned SQL by just doing the assignments on my own time when things were slow at the retail job I had. I got an office job on the strength of that, buuuuut my first assignment wasn't really doable in SQL, so I did the work in Python (a language that, up until that point, I had made 0 programs in, but I had watched while someone else made a program in it), and then bodged it into Excel for visualization. That made me look enough like a developer that the data science team was willing to talk to me, and so I got to sit in on the Alteryx intro seminar when they did, and then (since Alteryx was brand new and didn't really have any documentation or communities at that point) taught myself how to use it by trial-and-error. That got me enough experience that I subsequently got offered a job paying twice as much, working with BASH, hql, scala, and Jenkins (a list of coding options that -- you'll notice -- I had not yet had any experience in).
Basically, as a rule, hiring managers have no idea what all is going on behind the scenes, nor do they care, as long as they get the intended outcome. So my approach for interviews is to approach it as communication/translation problem for the first half: what exactly are they hoping the person in this position will be doing? "So, for example, {possible project based on my understanding of how they described the job}, would that be the type of thing?" Repeat until you're pretty sure you know what they're looking for. If you can do that thing, then you're justified in saying "I can do that", and you probably have evidence to back it up. So the second half of the interview is using their questions as an opportunity to lay out your evidence. Bonus: asking questions in the interview makes you look both smarter and more engaged!
If you get a technical interview/whiteboarding interview, don't panic! They're looking more at how you approach the problem than they're looking at your actual ability to write solid code / know the exact names for everything (my last interview I had to ask "What's that?" after, like, 3/4 of the questions. Then the interviewer would start describing it, and I'd be like "OH yeah, so then ....". I got the job.) So if you don't know what to do, start writing out outlines, mind-maps, lists ... whatever would help you figure out how to get started. Write down the facts of the situation, and implications of those facts; write down questions you have, and how to get them answered. This is a situation where partial credit is very VERY much a possibility, so get as many possible partial-credit sources on the whiteboard as possible.
So yeah. Coursera, Khan Academy, etc, all great. You can also just find some school that doesn't password-protect their class materials, and if you can mess around enough to solve the problems on the homework assignments, then you know (at least) as much as anyone who's officially taken that class. Alteryx and Tableau offer free online training with a web-portal sample of their software. You could also check for volunteer opportunities: I'm organizing permit applications for Sierra Club, and I bet there's a non-profit near you that would be equally delighted (read: fucking overjoyed) to let you take over all graph/numbers/spreadsheets for their projects. Then you get them to write you a letter of recommendation, and put the reporting work on your resume, and you've got "real world experience" while you're saving the world.
I wish you the best, and feel free to ask more questions as you go farther along your journey! I definitely recommend the data-work life; it's been my favorite career so far.