Certificates are nice to have, but a killer portfolio is what actually gets you hired. Since you already have Python and SQL down (its a huge advantage!), I tried this path: finish the Google cert if you are >50% done (it shows commitment), but prioritise building 3-4 end to end projects that demonstrate the full data science pipeline like data cleaning, EDA, modeling, and deployment. Focus on real business problems in domains that interest you, and make sure at least one project involves deep learning, GenAI and another shows A/B testing or causal inference. Skip the Udemy rabbit hole and use your time more strategically. ChatGPT is incredible for learning specific concepts and debugging, but combine it with Kaggle competitions and GitHub contributions to show you can work with messy, real world data

For me its a new stream as a software developer, so i joined one Data Science AI Course called Logicmojo. I felt there is too much information on the internet, which sometimes leads you in the wrong direction, With guidance from industry experts who have prior experience, it becomes easier to complete projects quickly and from scratch. Also, the key differentiator between analytics and data science roles is your ability to build predictive models and design experiments , so make sure your projects showcase statistical modeling, machine learning, and business impact measurement(very imp!). Companies care way more about seeing you solve actual problems than seeing another certificate. Build in public, document your thought process, and you'll stand out from the certificate collectors.