Data Scientists must marry a deep technical skillset with domain expertise and context in order to extract real value from raw data.
That said, it seems data scientists can become valuable in many industries and research areas, and those already established in the field seem to have encouraged a nimble, pivot-friendly culture for both generalists and specialists.
Data scientists must also have passion.
The best solutions only arrive with determination, and I believe life is more fun when intrinsic motivation fuels a bigger chunk of your daily outward effort than coffee.
Without strong direction and a dose of perfectionist qualities, data scientists may be prone to stopping short of finding the most optimal way forward.
However, effective data scientists are aware of when to stop analyzing and when to start deploying insights, crafting recommendations, solving problems, and inspiring action.
Data scientists must be curious.
There are often many unique ways to approach a problem, and a good data scientist must consider alternatives often.
I find it consistently beneficial to ask myself if there are there better ways to acquire, clean, and merge data than the method I’m currently employing.
It requires both process and creativity to be effective.
It demands that you exercise your analytical, left brain to complete tasks and projects, and thereafter, to step outside, smell the roses, and be creative about where you can effectively apply your efforts next.
The work equally benefits from an autonomous, distraction-free approach, and a productively critical team dialogue.
Programming alongside a colleague in a driver-navigator format has a compounding effect on productivity.
It’s built for the curious and open-minded, as the field itself is always changing as new innovations get adopted.
A good data scientist must be aware of the latest news stories and advances, and up-skill in lockstep.
Tied to the idea of unrelenting curiosity comes an important acknowledgment that not every problem can be solved with data.
Certain aspects of a decision must be left to human intuition.
Data should be used to inform a decision and improve a prediction, but there are times when it should not be held as eternal truth.
It is fallible and has its limits.
It’s often an art as much as it is a science.
Sometimes models are stale.
Sometimes business environments change.
Sometimes predictions fall victim to bias, variance, or the data community’s least favorite variable: the idea of “irreducible error”.
Confidence in a prediction will never reach 100%, and nobody can truly know the future.
We can only improve how we go about navigating the unknown by taking full advantage of what’s known.
x: The UnknownMore powerful than the pursuit of new knowledge is the recognition that there will always be so much we cannot know.
I’m learning to accept this in business, in life, and move forward in spite of it.
I don’t know if my dreams, my plans, and my skills will all come together to form the perfect cocktail of data science, joy, success, and fulfillment, but I do know that I’ve never regretted a calculated, education-first risk that puts me outside of my comfort zone.
The most impactful experiences I’ve had have been stationed in such environments.
The more I learn, the more I realize, with increasing certainty, there’s so much I don’t know and so much left to discover.
That said, let’s stay curious together.
Let’s keep learning and improving together.
Thanks for reading,AlexStay tuned as I move forward from here with increasingly technical posts as I dive further into data science.
I encourage you all to like, comment, share, or message me directly with your thoughts on the ideas presented here, or suggestions on interesting topics I should look into going forward.