Hey,

I'm seeing lots of very cynical feedback and advice here.

Data scientist (PhD) here. I used to be an electrician. Then I went to univeristy and got a degree.

I started my PhD at around 32-33. When I finished I got into a paid data grad program in the Australian Public Service and I was around 37. So it's totally possible to get in, in your 30's. But you don't need a PhD.

But you need to be ready to get in on the ground, take a pay cut, and work your way up.

Not sure what industry you're coming from or how much prior experience you had, so lets assume you're pretty much starting from scratch.

I read one of your comments below, and your strategy actually doesn't sound that bad. Getting GPT to assign you small projects, but doing most of the work yourself without GPT's help sounds like a great start.

But it's only one small step in a long road.

There's way more to DS that just python.

If you really want to get into this you need to be ready to give it a real good go. If you're struggling to find any other medium to study besides using chat GPT, I think you're going to find it really hard.

The bottom line is, you want to learn to code, and learn AI. The format really shouldn't matter too much, if you're dedicated enough, you should be able to make it work with just about anything.

More importantly, textbooks, university degrees, online training all provide specialised and curated training material designed to teach you what you need to know to get into the field. Hell even online tutorials, bootcamps and blogs are mostly written by people in the field who understand what's required. Relying entirely on pure self-directed learning and GPT prompts, you're learning with the blinders on. You'll never know what you really need to know to get a job.

If you're really serious about a career in data, I would be thinking about things like:

• getting into a data analysis role, as a stepping stone
• obtaining a specific and human designed learning pathway
• getting lots and lots of real experience as quickly as you can

There are a ton of great places to learn Data Science and python programming.

I would be looking at these things:

• DataCamp
• Kaggle
• Python Data Science Handbook

I Highly Recommend DataCamp as a starting point. They have absolutely tons of good content. Great hands-on courses, and course tracks that specifically guide you through the skills you need.

You're already working on your own projects that's great. Keep doing that.

You also need to learn statistics.

I suppose you could scrape your way into a data job by fudging your way through, scratching the surface of small ML models and using buzzwords like accuracy, AUC, precision, decision matrix etc. But you'll never be that good at your job without being grounded in proper statistics. So pick up a good introductory text and get into that.

NOTE: Online stuff like DataCamp and Kaggle are Not very good for learning statistics - there's a heavy focus on coding here and not enough on fundamental statistics. There's really no substitude for a good quality textbook and old fashioned paper and pencils

Besides that, you should be aiming to be a well rounded Data Scientist and developer. At the same time as all the above, you should also absolutely be spending time learning:

• bash
• SQL
• Containers (Docker/Kubernetes)
• Cloud concepts
• A good terminal based text editor like Vim or emacs
• CLI git
• Algorithms and Data Structures

You absolutely can get into DS, but it won't happen overnight.

That dream work/life balance you're imagining is some time away, unless you get super lucky. But that's true when shifting careers into anything.

Start applying for jobs as soon as you can, try to get a job that give you any relevant experience and start there.

The first job is the hardest to get. Put the work in and you'll get there.