Data science knowledge is vast and varied. It entails an in-depth understanding of data, the impact of models on this data, and various ways to refine the data making it more useful for these models. Also, it has to do with ways to depict this data graphically and make useful predictions based on it, using new data as inputs. Specialized data science knowledge also involves depicting this data in different ways (e.g. via a graph structure), gathering it from various sources (e.g. text), and creating interactive applications based on the data models built. Naturally, all this can be of value not just to data scientists but also to other data-related professionals. Let's examine how.
So, how can data science knowledge help data analysts and business intelligence professionals? After all, they are the closest to the role of a data scientist and deal with data in similar ways. These professionals can benefit from data science knowledge through a more in-depth understanding of the data, particularly when it comes to ETL processes and data wrangling. Also, for those more geared towards data models, they can learn more advanced models such as the machine learning models data scientists use and start using them in their work.
As for data modelers (data architects), data science knowledge can help those professionals too. After all, designing a useful information flow or implementing such a design into a database is closely linked to how this information is used. So, by understanding the potential different variables have (something that's bread and butter for a data scientist), a data modeler can optimize his work and build systems that are more future-tolerant. That is particularly useful in cases where the domain is dynamic, like in the e-commerce field.
Programmers can benefit a lot from data science knowledge too, especially those versed in OOP and functional languages (e.g. Julia and Scala). After all, data science involves a great deal of programming so there is a good overlap in the skill set of the two types of professionals. For this reason, many programmers end up getting into data science once they familiarize themselves with data science models, something they can do easily once they get exposed to data science knowledge.
Finally, data-driven managers have a lot to gain from data science knowledge, perhaps more than any other professional. The reason is that f you are involved in data-driven projects, you need to know what’s possible with the data you have and what kind of products or services you can build using this data. This is something you can do even without getting your hands dirty, by thinking in terms of data science. So, having some data science knowledge (particularly knowledge related to how data science is applicable and what data products look like), can go a long way. As a bonus, recruiting data scientists to implement your ideas is much easier if you are familiar with data science, something your recruits are bound to appreciate.
If you found this article interesting, you can learn more about data science and how it is leveraged in an organization through a book I co-authored last year. Namely, the Data Scientist Bedside Manner book covers this and similar topics thoroughly, along with some other practical knowledge on this subject. So, check it when you have the chance and spread the word about it to friends and colleagues in the aforementioned lines of work. Cheers!
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.