Chee-Keong C.

Use LLM to your advantage. Ask LLM to demonstrate what's Chi-Square for example, what are the use cases of Chi-Square? LLM can also generate data for you to practice if you're having difficulty finding one. Read the license agreement first, some LLM don't want you to train your model using their LLM generated data.

One way to be good at picking data methods is to read on use cases. Participate in topical forum like Medium, Towards Data Science....etc. Read one article a day. I'm talking about technical stuff, the math behind the methods stuff. Try to understand it. Start from there, if you don't understand the formula, go find resources online. I think it's better than just reading text and it also more applicable to the real world.

There aren't any "perfect" tools that you only need to learn once and you're set. Go check job ad, see their requirements. Do a simple statistics yourself to see what are the common tools that the market is looking for. Most people will not know every tools or knowledge that companies are looking for. Just keep on studying and be good at certain ones but somewhat familiar with other tools, just keep on applying.

I think as far as data sources, it'd be better if they're as close to real world data as possible. It's not something that's perfect and you'll need to spend a lot of time making it useful. That's how it is in the real world, everything is messy. One other thing is to find database related to the field that the new data analyst is trying to enter.

How to learn statistics? I would say if you're a beginner, start from the basic, things like min, max, mode, median, mean. Slowly move up to more advance topic like variance, hypothesis testing...etc. The key to learning statistics is to practice. One small step at a time. I'd suggest getting books from renowned author like Douglas Montgomery, Angela Dean...etc.

What statistics to use? It really depends on the problem that you're trying to resolve. 1. description statistics, you can describe the visible characteristics of the data. 2. inferential statistics trying to use smaller data to infer to larger data. 3. if you want to test a drug, a webpage change, you can use experimental design, where you apply your test and the methods concentrate on applying the test effect evenly throughout the test subjects and also minimize the individuality in a group. 4. if you want to find relationship you can use regression, correlation. 5. If you want to test multiple drugs effect you can use factorial design. And so on and so forth.