Never before have we seen so many job ads for a full-stack data scientist. But what exactly is one? A full-stack data scientist is a unicorn who is capable of fulfilling the role of a software engineer, data engineer, business analyst, machine learning engineer, and data scientist, all wrapped up in one package. These individuals have diverse skill sets beyond even that of a regular data scientist and could be a company's one-stop shop for managing the entire lifecycle of a data science project. This full lifecycle approach means that full-stack data scientists are capable of identifying the business need (or working with C-level executives to determine which problem needs to be solved), setting up the data architecture required for the project, analyzing data and building models, and finally deploying the model into the production environment.

Suppose you are the hiring manager for a data scientist position, and interviewing a prospective candidate. The candidate starts to express the skills hoping they are enough for the position and the best card among these skills is MS Excel capability. What would you think about this candidate? I suppose most of you would consider this candidate as mediocre, which is ineligible for most of the companies. Let's make a little change in our hypothetical interview by replacing MS Excel with predictive modelling.