I’m currently working as a data scientist with about 3 years of experience. I understand that the common advice is to “find a problem and use data science to solve it,” but I’m struggling with how to identify these problems in practice.
Most of the issues I’ve encountered so far can be addressed with basic data analysis techniques. I’m eager to explore more sophisticated data science methods, but I’m unsure where to look for opportunities within an organization.
How do you find use cases for more advanced data science techniques? Are there specific approaches or strategies you use to uncover these opportunities?
Howdy people,
you may successfully find and implement data science use cases that generate considerable value for your firm by working closely with business stakeholders.
Despite the amount of time I’ve spent studying complex inferential and predictive techniques, I’ve discovered that the majority of useful solutions are based on approaches that are more straightforward.
When the necessity arises, I certainly like creating the more intricate answers, but I believe that changing my mindset from searching for problems that suit already solutions to searching for problems that fit preexisting solutions helped me become a more productive and successful data scientist.
Hi Chip
To find data science use cases, align projects with business goals, identify inefficiencies, assess data and processes, explore industry trends, engage stakeholders, start with pilot projects, and measure their impact. This approach helps tailor data science to your organization’s needs.