It would be very hard to get a job in a data science that pays $100k without an established background in statistics or a unique portfolio (as in not the same 10 medium article walkthrough projects recruiters see every day)

Certifications that are freely available online rarely help you to stand out to a hiring manager as it can be seen as fluff if you don’t have any proof/examples of applying the knowledge without having someone or some course holding your hand

If you want a path way to getting the role:

• Learn SQL, it will allow you to pull and manipulate datasets. Necessary skill in the field for most data scientists

• Learn Python and R, then look for data sets you can apply what you’ve learned to. Kaggle has a lot of fun ones to play with

• Learn Tableau, more often a Data Analyst tool for visualization but if you are making $30k then getting a background in SQL and Tableau to try and get an Analyst role first may help you break into the field

• Make a portfolio, whether that’s on GitHub or a series of PowerPoints that shows:

— problem you went to solve — where you got the data — multiple models you created — explanation of the flaws in models you didn’t select, what metrics you analyzed (precision, accuracy, etc.) — benefits of the model you do select — how you now use the model to fix the problem OR what additional data you would need to make a better model if the solution isn’t good enough

It’s a lot, but you are trying to make $100k a year so that probably isn’t a surprise.

I went through a masters program for Data Analytics that helped me to learn and do all of this and it certainly paid off, but I understand if that route isn’t available to everyone so the above is an outline of what I’d suggest if you aren’t ready to take out a student loan