100 percent this. Typically a data scientist is gonna have some sort of mathematics background. That's not for everyone. Its why they get paid 6 figures fresh out of college (often Masters/PhD level graduates). But there's still a huge gap between the average business analyst and a data scientist, and its filled by data analysts.
So if you ignore the more advanced aspects like AI/ML, NLP, hell even big data or non relational databases. Understand the core concepts of doing data analysis you'll likely carve a pretty good role for yourself. I've sat on teams of analysts and been a hero for being able to run simple SQL queries.
My curriculum for data analysts would start with a heavy focus in:
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ETL. Extract/Transform/Load.. essentially prepping the data for analysis
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Data Visualization. Lots of UI friendly point and click options that you can apply best practices too without advanced statistical modeling.
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Excel. Not so much because its the ultimate analysis tool. But it can do a lot and can give one a good sense of creative problem solving in both prepping data and running analysis. Lots of novel plugins as well like Fuzzy matching, and the analysis toolpak
Once you're comfortable with those, then you can explore R and Python (numpy, pandas, etc..). Learn how to apply your critical thinking/problem solving to the huge libraries of code that exist in those tools.
A few courses I just quickly snagged from Coursera that would be good for a newbie:
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https://www.coursera.org/learn/excel-data-analysis
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https://www.coursera.org/learn/ai-for-everyone
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https://www.coursera.org/specializations/excel-mysql
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https://www.coursera.org/specializations/business-analytics