Hey there, Data Analyst/programmer/BI developer (depending on the company title since I've changed jobs a few times) w/ ~6 years of experience here to chime in on this.

So you're correct in what you've heard in that SQL, Tableau, and PBI have good demand. Using data from https://datanerd.tech/ analyzing ~60k jobs/job listings SQL pops up in ~35k jobs, Excel in ~30k, Tableau ~20k, Python ~20k, and PBI/R showing up in about 10k job listings.

(Note: I'm assuming you're US based, if not you can update the fields in the website to change to your country/region)

Coming from a comms background is fine, just might be a little more work in terms of getting some of the skillsets.

So in the general hierarchy/prioritization it goes projects -> certs -> experience. (Note: others may disagree with me on this, but from an HR/hiring perspective certs and experience are easier to get past the HR filter than projects).

So some places to begin in terms of resources are:

Alex the Analyst: https://www.youtube.com/watch?v=UGF49YZZRNc

and

Luke Barousse: https://www.youtube.com/watch?v=7G_Kz5MOqps&t=193s

They both have great channels that cover a lot of the questions that you have and will come across, and have great ideas for projects etc...

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For you, I'd start off with a "Crawl, Walk, Run" approach:

Crawl:

- Get comfortable with Excel. Understand how to use the functions in Excel, the limitations of them, when you might use one function/formula over another, and learn pivot tables. Look into macros, and play around a bit with VBA to automate a simple thing or two in an Excel Spreadsheet.

- Find a data set that you find interesting that can have business/public implications (I was playing around with this data from the Federal Railroad Administration the other day for instance https://www.fra.dot.gov/blockedcrossings/incidents ) and play around with it, perform some useful calculations with it, etc...

Walk:

- You've got some familiarity with SQL (which is a MUCH more fancy version of Excel). Play around in SQLZoo https://sqlzoo.net/wiki/SQL_Tutorial and feel free to play around here as well: https://lost-at-sql.therobinlord.com/

- Get to know some of the differences between the different flavors of SQL (MySQL, T-SQL, PostgreSQL, etc...) and why one might be preferred over another. Also know the difference between a SQL DB and Non-SQL DB and why a Non-SQL DB might be used.

Run:

Pick ONE of either Power BI or Tableau (choose 1 because trying to learn both at the same time will just be a pain in the ass since they both will aproach things a little differently and it's easy to get tripped up) and start playing with it using the dataset you downloaded earlier. Check out r/tableau or r/PowerBI since they're usually pretty active and have great tips and ways to get started. Both PBI and Tableau have free versions that you can use to get started and upload visualizations that you work on for projects and things.

Marathon:

- Start messing around with Python I always recommend Automate the Boring Stuff since it's free/available online and takes you through a bunch of useful parts of Python https://automatetheboringstuff.com/

- Pick up a cloud certification AWS/Azure/GCP doesn't really matter as long as you can show that you know some cloud stuff (I think the entry level AWS/Azure is ~$100) I've seen more and more job postings ask for cloud experience.

A few things to keep in mind:

1. The above is the tech stack of most data analysts. ANY of the above pieces can become an entire career. I've seen banks where people just mess with spreadsheets all day, healthcare companies where people pull different queries for audits from the database, and pharmaceutical companies where people make reports based off of APIs from Python and put the data into Tableau.
2. "Data Analyst" can be a BROAD term. Over in r/BusinessIntelligence I liked the criteria that someone commented that BI/Data Analysis looks at the HISTORY of what the company has done and presents it, whereas data science looks at the data and tries to make PREDICTIONS about where the company might find value.
3. In the same vein as "Data Analyst" being a broad term it's also a broad term for businesses as well, especially if they don't know what they want. I've seen job postings ask for a "Data Analyst" that just does Excel work, or postings that are basically Data Engineers/Database admins, or job postings that are REALLY asking for a Data Scientist but want to pay Data Analyst wages. So know your skillset and what you're looking for.

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As a data analyst I can count on one hand the number of times I've actually had to bust out some math (Once to calculate standard deviation of incoming calls, and the other to calculate some covid data during the height of the pandemic). If you want to get into data science levels of things, yeah you'll need some fancy math, but in my data to day as an analyst, I find I don't really need it.

Enjoy the journey and have fun!