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Data Science

Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. In order to uncover useful intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process.

What profession did Harvard call the Sexiest Job of the 21st Century? That’s right… the data scientist.

Ah yes, the ever mysterious data scientist. So what exactly is the data scientist’s secret sauce, and what does this “sexy” person actually do at work every day?

This article is intended to help define the data scientist role, including typical skills, qualifications, education, experience, and responsibilities. This definition is somewhat loose since there really isn’t a standardized definition of the data scientist role, and given that the ideal experience and skill set is relatively rare to find in one individual. This definition can be further confused by the fact that there are other roles sometimes thought of as the same, but are often quite different. Some of these include data analyst, data engineer, and so on. More on that later.

While these, and other disciplines and areas of expertise (not shown here), are all characteristics of the data scientist role, I like to think of a data scientist’s foundation as being based on four pillars. Other more specific areas of expertise can be derived from these pillars.

Data Science Goals And Deliverables

In order to understand the importance of these pillars, one must first understand the typical goals and deliverables associated with data science initiatives, and also the data science process itself. Let’s first discuss some common data science goals and deliverables.

Here is a short list of common data science deliverables:

  • Prediction (predict a value based on inputs)
  • Classification (e.g., spam or not spam)
  • Recommendations (e.g., Amazon and Netflix recommendations)
  • Pattern detection and grouping (e.g., classification without known classes)
  • Anomaly detection (e.g., fraud detection)
  • Recognition (image, text, audio, video, facial, …)
  • Actionable insights (via dashboards, reports, visualizations, …)
  • Automated processes and decision-making (e.g., credit card approval)
  • Scoring and ranking (e.g., FICO score)
  • Segmentation (e.g., demographic-based marketing)
  • Optimization (e.g., risk management)
  • Forecasts (e.g., sales and revenue)
Data Science

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