When I was in college since that time I was aware of data science, artificial intelligence, or big data But When I started my professional career, the relevance of AI in the industry was quite low which is why I refocused on other topics over the years. However, I tried to keep myself up-to-date in AI. And some areas like neural networks or fuzzy logic were leveraged in some success stories.
Basically, there are two perspectives that are essential:
In what topics are you personally interested?
What are the departments that have the most influence on your current or future job?
In the ideal case, these areas have an - preferably large - intersection. With respect to digital alteration and Industry 4.0 AI, data science, big data, and AI are some of the main drivers. If you are involved or interested in digitalization, you should aim for theoretical knowledge of all these topics. And you should also get deep information of at least one of these, probably the one you are most interested in.
In addition, these three fields are tightly interrelated in many possible applications. If you recover big data, how can you extract the most valuable information from these data? This is the place where data science and AI methods such as deep learning are really helpful. Think of preventive maintenance as a feasible use case. You‘ll find that industrial ecosystems such as GE Predix or Siemens MindSphere are heavily using AI, data science, and big data.
You can check our Data Science Course, Artificial Intelligence Course, and Big Data Course to upskill yourself.