Data Science is domain-agnostic in the sense that it is a collection of methods, tools and techniques that is necessary to extract insights from data assets.
What we are seeing for the last few decades and this trend is only increasing, almost all industries are adopting digitization. One major aspect of digitization is the increase in data collection.
All of a sudden, a company will go from collecting very little data to now collecting data from their website (views, clicks), employee data, manufacturing (from machines, processes, employee activity) and any other aspect of their business that needs to be improved.
And if you cannot measure it, you cannot fix it.
With professionals across industries showing interest in Data Science, it is necessary to choose a domain. Especially if you have knowledge and experience in specific domains such as Finance, Marketing, Manufacturing, Healthcare, Fraud Prevention, Banking, Retail to name a few.
These domains are seeing a rapid uptake of Data Science techniques and your domain expertise is what can make the difference between simply apply Data Science vs thriving and improving business outcomes with Data Science.
So the question of choosing a domain is one of perspective. As a fresher I would suggest to join a company that is growing so that you will get to work on real problems which have business impact. Once you gain experience as a Data Scientist, then you can continue exploring other domains.
If you are an experienced professional and have deep understanding of a specific domain or industry, I recommend that you start reading about how Data Science is being applied in your industry. One good place to start is reading McKinsey or BCG reports and white papers (google e-commerce data science mckinsey for example).
They do a great job in surveying the landscape of Data Science in that particular industry and will give you just enough information to help you connect the dots. After you have learned the jargon and concepts of applied Data Science (such as Market Basket Analysis, Clustering, Recommendation Engine and so on), then you will be far more equipped than others in your industry to partner with stakeholders on Data Science initiatives in your organization.
Organizations navigating the Data Science adoption curve are often not fully utilizing their domain experts. And domain experts, unfortunately are staying out of these conversations thinking Data Science is only about technical skills and maths and computer programming. This cannot be further from the truth.
Data Science adoption only makes sense if the domain experts are going to be steering the Data Science ship. The Data Scientists themselves bring in the tools and techniques, but need the collaboration of their domain counterparts to clear the web of processes and create the paved path for implementation.
Domain experts are the catalysts that will ensure that the technology and code from Data Science actually converts to the value creating engine that the business desires.
Without domain experts, the Data Science initiative will land up as a nice LinkedIn post for the company page, and that would be a loss for all stakeholders.