The data scientist and the data analyst both deal with data analysis as their primary task, yet those two roles differ enough to warrant an entirely different set of expectations for each. Both share common attributes and skills, however, making them more similar than people think. This similarity allows a relatively more straightforward transition from one role to another, if needed, something not everyone realizes. This article explores this situation's details and makes some suggestions as to how each role can benefit the other.
The two roles are surprisingly similar, in ways going beyond the surface kinship (i.e., data analysis). Data scientists and data analysts deal with all kinds of data (even though text data is not standard among data analysts), often directly from databases. So, they both deal with SQL (or some SQL-like language) to access a database and obtain the data needed for the project at hand. Both kinds of professionals deal with cleaning and formatting the data to some extent, be it in a programming language (e.g., Python or Julia), or some specialized software (e.g., a Spreadsheet program, in the case of data analysts). Also, both data scientists and data analysts deal with visuals and presentations containing these graphics. Finally, both kinds of professionals write reports or some form of documentation for their work and share it with the project's appropriate stakeholders.
Despite the sophistication of our field, we can learn some things from data analysts as data scientists. Particularly the new generation of data scientists, coming out of bootcamps or from a programming background, have a lot to benefit from these professionals. Namely, the data analysts are closer to the business side of things and often have domain knowledge that data scientists don't. After all, data analysts are more versatile as professionals in employability, making them more prone to gathering experience in different domains. Also, data analysts tend to have more developed soft skills, particularly communication, as they have more opportunities to hone them. Learning all that can benefit any data scientist, especially those who are new to the field.
Additionally, data analysts can learn from data science professionals too. Specifically, the value of an in-depth analysis that we do as data scientists are something every analyst can benefit from undoubtedly. In particular, data engineering is the kind of work that adds a lot of value in data science projects (when it's done right) and something we don't see that much in data analytics ones. What's more, predictive modeling (e.g., using modern frameworks, such as machine learning) is found only in data science, yet something a data analyst can apply. Once someone has the right mindset (aka the data science mindset), it's not too difficult to pick up those skills, particularly if they are already versed in data analytics.
If you wish to learn more about the soft skills and business-related aspects of data science, you can check out one of my relatively recent books, Data Scientist Bedside Manner. In this book, my co-author and I look into the organization hiring data scientist, the relevant expectations, and how such a professional can work effectively and efficiently within an organization. So, check it out if you haven't already. Cheers!
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Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.