u/CIA11

I'm using the Desktop version of Tableau Public and I've made my sheets and am creating my dashboard. I've realized that a lot of things need to be within containers to really have a good layout, but that it seems like it's hard to put things into a container if you set it up without one. Should you first plan the dashboard and know what container layout you will have first and set it up before creating the dashboard?

For instance, I made a map chart and put it on my dashboard within a container that has other charts. On the map, I did 2 custom float objects. I am now realizing that if I want to move the map, it won't bring the 2 floating objects on top. It seems like if I had put the map within it's own container, and so those floating objects are in it, that I can just move the container and everything will work.

Again for context, I'm using the Desktop version of Tableau Public so I know that it comes with less features so idk if this is something that makes it harder to work with?

I got a degree in statistics and wanted to be come a data scientist (would be okay with being a data analyst too, but I thought DS work was a little cooler and soooo many people are applying to be data analysts) but when I was in college planning on being a DS (graduated 2 years ago) the job description for them was a lot different than they are today.

Now, job descriptions for data scientists describe software engineers, cloud engineers, data engineers, data analysts, ml engineers, etc. Basically lots of jobs within 1. As a result, I've found that it's super hard to even get 1 interview if I don't already have experience as a data scientist or a cracked github.

An issue I'm having is I feel like I graduated with 90% of the skills needed to be a data scientist, which is now probably 5% of what's being asked in job descriptions. I'm trying to learn everything that I can and do projects on different things to practice, but the biggest issue I'm having is that because there are so many different requirements, I'm not able to actually get good at each one. For instance, if I need to know how to put a ML model into production with fastapi and so I do a project with it, I can't remember how to use it when I stop using it for 3 months to learn other things and do other projects.

Is this how data science is really ending up? Are there other better career paths out there?

I am looking to buy my first pre-built split keyboard, but I don't want it to look/feel cheap (aka no 3D printed case). I heard Corne keyboards are good, and I like that there's not many keys (so less travel), but it looks like a lot are either DIY or someone else builds it with a 3D case, or less quality.

As a result, I was looking at the ZSA Voyager which seems nice, but I heard the build quality isn't as good as you'd expect for an almost $400 keyboard and it's wired (I'd prefer wireless).

Has anyone bought the ZSA Voyager and thinks it's worth it? Especially compared to other split keyboards?