First, identify what it is you want to learn.

Can you come up with a list of topics and projects that you wish to work on? Then, you will have to research various degree programs and their curricula for overlap. Additionally, you should then look at different companies and the kind of work (company blog, webinars, industry talks, etc.) and then identify if they would help you gain the experience you want.

These are questions that can turn into exciting research projects for your spare time on weekends. And if you do proper research into this, you can share your learning on a LinkedIn post or share a google doc with your findings for the benefit of others who have similar questions.

Once you have done your part of the work to answer this complex question, you can then reach out to more experienced professionals in your network to ask precise questions, which will increase the chance of receiving a useful answer.

This is important to remember. You have asked me this question but it is not clear what work you have to do answer this question yourself first.

Once you attempt to answer this question based on resources and information that is freely available online, you will be far far ahead in the journey of discovering the root of this question.

Usually, you arrive at this question if you are hitting a roadblock in your job (or sometimes unable to land your first job). You may not be getting the pay hike you were expecting or the promotion that was promised you.

So when you ask this question, what you are really asking is, "how do I get better at attracting the opportunities I desire?".

As a Data Scientist, you can improve the odds for doing great work by constantly seeking feedback from your peers in the workplace and from the industry at large. Kaggle is a very good place to establish a benchmark. Try to hang out there as much as you can and absorb.

When you do feel the need to ask a question then I request you to take a look at this guide https://stackoverflow.com/help/how-to-ask first.

The discussion shared above was part of many Q&A sessions Harsh Singhal conducted with Data teams at various companies and colleges.