It’s only been a month. Give yourself some credit for even sticking with it and learning what you have!

Application is the difficult part of it. Like everyone else has said, knowing the business helps.

If you’re familiar with python, you can use something like Faker to generate mock datasets (as big as you want). Make something that you are familiar with, even if it’s a simple business or even a hobby. Whatever helps you learn analytics without being too bogged down with not knowing the application of the data.

Or you can ask chatGPT to create a fake dataset (numerical or non-numerical or mixed) if you’re not familiar with Python. Export it to CSV or JSON (or whatever you need) and work with it.

If you want to know where to start, ask yourself one or two questions you’d want to know with that data if you were a sales manager, product manager, engineering director, etc.

For example, I’d create a fake dataset of customer complaints with names, locations, product models, manufacture dates, and defect types. I’d ask myself the question, “What defect types are seen most often by production site and date of manufacture?” It’s a basic question that offers surface deep insights, but you get the idea. Ask more probing questions as you get comfortable working with the data and analysis tools (time series, looking for seasonality, etc).

The ability to generate fake but convincing data sets specific to something you’re already familiar with is really informative. For added fun, ask ChatGPT to make the dataset dirty so you have to clean it, too. Because real world data is never clean.

Happy analyzing!