While the field of Data Science is vast, we can break it down into two simple genres: Decision-Based, and Feature-Based Data Science. Let’s dig a bit deeper:
Decision-Based Data Science
This genre of Data Science is simply an extension of Data Analytics. People operating in this space instrument the techniques that help tackle overly complex problems that require advanced data-modeling and statistical techniques. Specifically, they help build sophistication around answering the last two questions from our list above (i.e. What is likely to happen / What actions should we take)
For example, the interpretation of whether or not a product experiment was successful (especially if it was tested with a small sample of customers) would typically be categorized as a Data Science effort, as it would require the use of advanced statistical and probabilistic methods.
The common role in this area of Data Science is: Data Scientist
Feature-Based Data Science
This genre of Data Science refers to the building of data products. Data products are literal product features based-on data that are consumed by a companies’ external users/customers. Let’s dive right into an example:
Amazon’s recommendation feature, “Customers Who Viewed This Item Also Viewed” is a data product. Data Science folks at Amazon have built a data-model to relate transactions that have been purchased together with the item you’ve just selected. Amazon is using data to not only deliver this data product, but continue to make it more accurate over time.
The common role in this area of Data Science is: Machine Learning Engineer