Becoming a Better Data Scientist
One does not simply “become” a data scientist. It doesn’t happen over night or upon receipt of a degree. It takes time, effort, studying, questioning, and lots [I mean LOTS] of learning.
In 2008, I had never heard of the term Data Science. I was a hard-engineering graduate who was wrapping up four years of load-diagrams, fluid mechanic calculations, and enough DiffEQ to make a man mad. It wasn’t until I was neck deep in night courses for a social-science Master’s program (another four years of education and work experience late) that the real world application of my evolving data skills began to coalesce around the concept of Data Science. Almost a decade later, I am finally becoming comfortable calling myself a Data Scientist. Though on the inside, I may be sheepishly thinking “…sort of”.
My evolution from engineer to policy wonk to development specialist has enlightened me to the three headed beast that is Data Science. To truly excel at the emergent field of data wizardry requires a mad-scientist amalgamation of computer programming, statistical prowess, and social understanding- all wrapped up in a designers lens for visualization and presentation. Without the coding skills, your analysis and ideas will remain at the Diet-Coke+Mentos bottle rocket phase instead of elusive Falcon-X, perfect landing. Understanding your data and pulling out statistical meaning is essential to find the million dollar treasure in the proverbial junk yard rubbish pile. And all of the analysis in the world can be useless without the guided hand of application and language required to turn complex numbers into digestible insights.
These skills are not easily attained. They are sourced from the towers of Academic study in principles and concepts; refined in the fires of the business world; polished through long nights and hair-greying projects. And even after years of work, the battle to become a data scientist is never finished.
Data science has floated to the top of the pile in emerging skill sets and careers, as both a driver and cause of the advances of technology. It is the reason I have fallen in love with this burgeoning field. One of my passions in life is learning, though I’ve determined that I learn better outside the walls of the university setting. New approaches, technologies, systems, and processes for coding, analysis, and visualizations keeps me constantly engaged. Because as Da Vinci said:
Iron rusts from disuse; stagnant water loses its purity and in cold weather becomes frozen; even so does inaction sap the vigor of the mind. So we must stretch ourselves to the very limits of human possibility. Anything less is a sin against both God and man.
For those of you who, like me, want to become a better Data Scientist, here are some great places to sharpen your iron wits and expand your skills. Because the journey to become a better data scientist never ends.
For the self-taught man: Data Science Drop-out Curriculum by David Venturi
Coding from soup-to-nuts: 370 Online Coding Courses cultivated by Quincy Larson
The visualizers playground: Top Codepens of 2016 from CodePen.io
Social meets Data: Impact of Social Science on Data Science by London School of Economics
