In fact, I am not a computer scientist nor engineer, instead a physician in the public health field. Nevertheless, I am now passionate about learning data science and artificial intelligence (AI), because AI is acquiring a great position in the medical and public health fields as well. I am a bit afraid that my job might be replaced with AI. Also, I am considering that learning AI will be a good path for my career, because my professional experience will be useful for AI for medicine. Moreover, I am interested in medical error by AI, about which I would like to talk later.

As you know, the job market for data science is rapidly growing. It is widely told that there will be high demand in the near future for data scientists, including AI specialists. At present, AI is widely used in various fields from business to public health. Thus, it can be highly recommended to many people having different specialties to study data science from now for their future career success. However, many people tend to find AI too difficult to learn.

In my impression, there are mainly two kinds of people who are learning data science and AI: 1, computer scientist, mathematician, and statistician, who invent a new AI model; and 2, specialists in the other fields, such as business, public health, and medicine, who consider how to use invented AI models for their special fields.

For example, computer scientists or statisticians are not familiar with medicine, so they do not know how to use AI models for medical diagnosis. They have to cooperate with physicians to develop medical AI. Therefore, it is also meaningful for physicians to study AI. However, they do not have to study AI so deeply because they do not invent a new AI model; They only have to use an invented AI for medical practice as medical AI. Thus, it is not always necessary to begin to study AI at a young age.

In fact, the basic principles for data science and AI comprise mathematics, particularly statistics, linear algebra and calculus. Simply speaking, AI is to analyze the past by statistics, to predict the future by linear algebra, such as vectors, and to modify the prediction by calculus. Thus, knowledge of mathematics is absolutely required in advance.

Moreover, this process is performed by computer, thus knowledge of computer languages is also essential to order a computer to create AI. Python and R are extremely important in AI. Actually, computer language is created based on English, and many study materials for AI are offered in English. Thus, English proficiency is advantageous. If you well know how to learn a foreign language, you will be able to master a computer language easily.

It is not mandatory to be familiar with physics, chemistry, or biology, but a plus to apply AI to various technologies.

I think that many beginners begin to learn data science and AI with some study materials written in their mother language. On the other hand, there are a lot of teaching books and videos in English that are suitable for beginners. You should find the best materials for you by yourself. Here, I would like to recommend a few studying materials for beginners.

Complete Data Science Bootcamp at Udemy (They often offer a great discount)

Getting Started With R: An Introduction for Biologists: Beckerman, Andrew P., Childs, Dylan Z., Petchey, Owen L.