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Nowadays BioMedical Engineering is the fastest growing career all over the world. Biomedical Engineer is responsible for developing, designing and improving medical devices. They are also responsible for the procedures for efficient diagnosis, operation and treatment of diseases and other health ailments. Biomedical Engineering innovations examples are prosthetics, artificial limbs, imaging machines and different other medical devices. Biomedical Engineer evaluates the safety, effectiveness and efficiency of biomedical equipment. For taking admission in Bio Medical Engineering, the candidate must have to clear the entrance exam like JEE Main or any other eligible entrance exam that you can apply after 12th only.

A Masters data science degree program or a PhD might be a way to go, in developing and waving a technical data science skill set to potential employers but is not a prerequisite to get started with a career in data science. Lack of a highly quantitative degree does not debar one from learning data science. It is possible to learn data science even without a Masters degree. For high-functioning individuals, who really have the knowledge and expertise with the required tech skills, having a Masters or a PhD does not matter in the data science space. Real data science experience always outweighs the time spent in acquiring a Masters degree or a PhD because getting a PhD can prove to be a very long grind.
A Masters or a PhD matters when you are applying for a job but not having a PhD is not going to stop you from becoming a data scientist. If you are applying for a data science job at Google, then a Masters or a PhD might be a requirement but other companies will have biases in other directions for hiring a data scientist. PhDs matter only if you are applying for a higher level data science position. When beginning to learn data science, PhD or a Masters Degree is not a necessity.