Learning in Reverse

The path of far too many Data Science & Analytics Professionals

How often have you seen a house being constructed with the roof built first? How about a new road where the asphalt is laid before the over-brush cleared? Maybe learning calculus before mastering basic addition and subtraction?

No? None of those questions make any sense? Good, I was hoping not.

Now that we’ve established a common understanding that some steps in a process must occur before others, let’s consider why we often find that individuals are learning Data Science before learning the basic concepts of analytics?

Individuals attracted to Data Science & Analytics, as their first career or as a career change, almost always seem to enroll in programs that teach the tools and skills associated with data science applications. It would be easy to assign fault on the individual for putting the cart before the horse but it is not that simple.

Data Science & Analytic career seekers are being incentivized by both employers and educational institutions to focus on Data Science skills almost exclusively.

Take a look for yourself at analytic job postings or curriculums of educational programs. Everything has a heavy focus on tools for Data Science.

I want to be clear: Data Science is an important and powerful discipline within the field of Data Science & Analytics. However, there are prerequisites that must be taught first. Some prerequisites are best learned through experience, while others are available as frameworks to process an analysis.

A common framework referenced by my colleagues and I is IPODS; Information, Process, Organization, Determination, Synthesis. We refer to this as the analytic process (TAP). You can read more about TAP here.

Frameworks are critical to Data Science & Analytic professionals. All aspects of their work must be analyzed within the appropriate context, perspective, and scope. DS&A professionals are responsible for understanding how data is generated by business processes, how that data relates to the overarching business model, how the subsequent information is being used to support decision making, and how data and decisions flow through these steps.

Taking an individual educated to use the tools and skills of Data Science but without the underlying frameworks creates a steep learning curve that often underserves both the individual and the organization. It is always unfortunate when expectations and reality misalign. As an example, a newly hired employee freshly trained with Data Science skills tasked with creating a segmentation model by leveraging customer comments entered into a messaging system will typically end with an overly complex plan, without a meaningful model, and likely severely delayed.

DS&A professionals need to understand not only the foundational frameworks for accessing context, perspective, and scope, but also the means for accessing this level of insight.

There is much variation in how well or quickly individuals learn these skills. Some educational platforms make this a portion of their curriculum; however, experience is always best way to achieve mastery.

While each individual is advised to determine their own education and learning path, the systemic challenges are better addressed by the employers and educators guiding those individuals. These organizations must realize the benefit of teaching DS&A professionals to be well rounded in their skills, tools, and capacity to leverage them in real world applications. The best way of accomplishing this is for cooperation between these organizations in crafting programs that allow students access to real scenarios and real data.

I do know that there are a number of educators and companies doing this today but there are far too many not. Ideally individuals with aspirations of a career in DS&A will find educational programs with well-rounded curriculums that offer substantive exposure to real world problems and data.

About the Author: My name is Ion King and I am the Chief Executive Officer at SimDnA (Simulated Data & Analytics). Our focus is on helping others passionate about growing careers in Data Science & Analytics achieve their goals. We take an innovative approach by leveraging simulations of real world business data environments to create experiences for users to learn by doing.