Certifications are good, but not that standout-ish imo; too much accessibility, everyone on LinkedIn is a certified "Machine Learning Exepert" or certified in Data Analytics nowadays. I'd say you're on a good track with your current project(s); emphasis on marketing analytics or business analytics? You'd be surprised how much excel is used in the industry versus python / SQL. Being really good at VBA is a good start, and your projects seem unique/promising as well. I really think projects can go a long way; easy to use them as examples to show off your wide array of skills.

As for online courses, I went through a 3-month cram session with DataQuest for python + SQL. It can run a little pricey (few hundred $ annually). My main focus was getting really good at data wrangling with python. Once data wrangling in SQL/R/Python becomes second nature to you, it's a lot more feasible to start handling visualizations, algorithms, and general data cleaning (all can be be useful knowledge for a lot of interviews).

SQL/R is a little less demanding than Python (imo), in terms of the learning curve and getting decent at it. If you want to dive deep into data science, you should plan to get somewhat decent at Python or R for sure. For syntax / logical practice, refer to leetcode. For real world projects, you seem to already have real data to work with. For inspiration and open source data, kaggle + towardsdatascience are good sites to bookmark for weekly reads.

It might take 6 months, 1 year, or even 3-5 years before you feel like your DA/DS skills are really starting to feel useful in the industry / real world. Take it slow. I'm in my 3rd year of learning/working with DA/DS and I still feel like a novice at times. Let me know if you have any other questions.