You have trouble finding a data science job, but don’t understand why? For every person who has a question, and asks it, there are ten people who have the same question but don’t ask it. If you’re one of those ten, then this post is for you. Hopefully, you’ll find it helpful.
Some steps are hard to take on your own. Schools aren’t good at teaching data prep, ML devops, or networking. Most people learn those things on the job or from a mentor if they’re lucky. Many people never learn them at all. But how do you bridge that gap in the general case? How do you get a job without experience when you need a job to get experience? So to help everyone at the same time, I’ve put together a progression that you can follow from any starting point to actually become a machine learning engineer.
One thing you should do is build a portfolio of your personal machine learning projects. But, how to do that? I’ve seen hundreds of examples of personal projects that ranged from very good to very bad. So in this post, I’ll tell you how. If I had to summarize the secret to a great ML project in one sentence, it would be: Build a project with an interesting dataset that took obvious effort to collect and make it as visually impactful as possible.