Not that long ago I arrived at a conclusion over the similarities between The Karate Kid and becoming a Data & Analytics professional. This conclusion is centered around the main character Daniel LaRusso, whom is played by Ralph Macchio. More than half the movie chronicles Daniel’s seemingly unrelated chores that in the end serve as the foundation for his martial arts success.
With this movie having come out nearly 40 years ago and me having not watched in close to 25 years my recollection at this point in time is mainly high level. However, the overarching principles centered around learning by doing still resonate loud and clear.
In the beginning of the movie Mr. Miyagi saves Daniel from a group of bullies. As a result, Daniel decides he wants to pursue martial arts. Having witnessed Mr. Miyagi’s fighting excellence Daniel wants to quickly follow suit. He imagines himself learning complex martial arts movements that can only be achieved through years of discipline and practice in an almost immediate fashion. He displays a level of naivety, disrespect, and impatience that gets eroded throughout the movie.
These activities requested by Mr. Miyagi served the purpose of teaching skills and building experience. In the case of Daniel it was muscle memory, motion discipline, and strength training. At the time, when Mr. Miyagi was requiring Daniel perform these tasks the justification was not understood. Daniel viewed these tasks with frustration and irreverence.
As the movie progresses Daniel begins to understand what exactly Mr. Miyagi was teaching. The way in which he framed his training, his life, and the tournament changed. He had acquired a new perspective and found appreciation for the deconstructed and segmented training style of Mr. Miyagi.
The lessons contained within this film bring wisdom and perspective to many different types of aspirations. For this article we are focusing on learning Data & Analytics.
Data Science as a career field has grown substantially over the past 10 years. There are numerous opportunities available to teach individuals the skills, principles, disciplines, and techniques. Individuals seeking career’s in this field are reassured by both the salary classification and the social validation. (reference)
As with Daniel, many aspiring analysts fall into the mindset of attaining the skills and experience necessary to be a successful analyst in an almost immediate fashion.
This sentiment is reinforced by education systems when considering programs are 4-6 months with some stretching to 12 months. This can be interpreted as asserting Data & Analytics skills can be mastered in this short timeframe which is far from the truth.
Learning Data & Analytics in a manner similar to how Mr. Miyagi taught martial arts will yield promising results. If aspiring analysts accept the proper perspective it will empower them to focus on the foundational elements with the correct discipline to actually learn and retain.
As with Daniel, application and repetition translate into functional knowledge that can be built upon. It is important to understand that doing a task only a handful of times teaches familiarity but in order to master a skill for application in all situations you must do that task many times.
Again this reinforces the concept of Learn by Doing. Daniel embodied this through his chores. For aspiring Analysts it’s a bit more challenging. Daniel learned by doing in real scenarios that simulated martial arts movements. A comparable medium for aspiring analysts may be through an internship or work study program. Unfortunately those options are minimized by time constraints within your position and restraints around system access to information.
It is not my intention to end this article in a seemingly helpless place for aspiring analysts but in some ways this is accurate. Countless analysts graduate from Data & Analytics educational programs only to be met with employer rejection due to lack of experience.
As with Daniel, when life knocks you down you have to get back up. There are opportunities for aspiring analysts to learn by doing and gather experience but you have to pursue it and not be defeated. Step outside your comfort zone, embrace uncomfortable professional situations, make connections, ask for what you want, find mentors, and display your drive.
About the Author: My name is Ion King and I am the Chief Officer at SimDnA. My focus is on helping others passionate about growing careers in Data Science & Analytics achieve their goals. Connect with me on LinkedIn or find more of my articles on medium