How can I add value to Data Science?

I’ll share with you my experience.

My take on this is, brace yourselves, controversial and contrarian: that technical skills can hinder your ability to add value.

To convince you of this, I will tell you why the question, “How can I add value?” may not be the right question to ask; how technical ability might disadvantage a data scientist; and how regardless of background, you can become successful.

I will also share my take on the most critical skill to have.

Why the question is poorly framedFirstly, to me, the question “How can I add value to Data Science” is poorly framed.

A better way to ask that question could be: where can I contribute my personal expertise or insight?The word “add” subconsciously directs me to believe that I literally have to “add” something through my analysis to create meaningful value.

However, in the world of data, that may not necessarily be true.

In the world of data (which I call Datatopia), data scientists know that simply cleaning data is the start of any meaningful analysis.

According to a survey by CrowdFlower, data scientists spend approximately 60% of the time in organising and cleaning data.

The reason is simple: if you can’t make sense of the data, figure out what is relevant and what is not, then there is no purpose to the analysis conducted subsequently.

Interestingly, this stage of analysis often requires you to “subtract” information rather than to “add”.

Examples include filtering out unnecessary variables and observations, finding duplicates and discrepancies, and deciding what to focus on.

Although it may sound trivial, this simple step has tremendous advantages later on in the data analysis process.

And guess what at the most basic level this step can be done by anyone with a sharp eye and a keen understanding of the organisation.

Many of my classmates with no technical ability have aided in data cleaning and organisation just by beefing keen observers.

Thus, to think that one must add something (either visualisation or data set or an algorithm) to create value is inherently wrong in my opinion.

You can keep “subtracting” (the right things) and still generate a lot of meaningful value in Datatopia.

The disadvantage of the tech-advantageWhile technical skills can help you build jaw-dropping data analysis and visualisations, it might hinder your ability to think outside the box.

Technical knowledge allows you to realise what is possible with the relevant tools and technologies and imparts you with the skill to achieve the same.

Although with this new-found knowledge you might see an endless pool of possibilities, the free-flowing thoughts in your mind are somewhat already directed to think along a particular line, tool or method.

Your mind starts translating the ideas into pseudocode (which is definitely important) but shifts your focus from ideation to how the idea will come to life.

In Datatopia, creativity is as important as technical skills to translate ideas into reality.

“Crazy” or “Out-of-the-box” ideas bring tremendous, unprecedented insights, which serve the main purpose of inference in Datatopia.

Therefore, while giving one immense power, technical knowledge sometimes limits one’s ability to come up with “crazy” ideas.

Once a crazy idea is formed, collaboration and online search for the code or tool to fill in the gaps in your knowledge more often than not allow some form of that crazy idea to be realised.

The advantage of thinking “outside the box” is what makes the non-technical person an asset in Datatopia and which is why everyone can contribute to creating meaningful value, beyond cleaning into the ideas that create novel analysis and visuals.

Everyone can contributeThese are just a few examples of how everyone can contribute to the process.

An important thing to realise is that Data Science is an interdisciplinary field and thus you can be data scientists with a different focus and expertise.

Don’t be a data snob.

To reiterate, if you are reading this you probably will be good at visualising data sets using programming languages like Python and R, but hopefully, sometime soon you might also be reading this as a superstar in collecting, finding, cleaning, or storytelling with data.

While it is great to be a Data Scientist who is balanced across all the domains, the fact is that you don’t have to be.

But to be, soon, it may be easier than you think.

For example, even now there is a wide range of possibilities for the non-technical person.

The recent rise of several automated tools enables almost anyone who has previously used a computer (e.


, for email or word-processing) to use intuitive tools to process and visualise data.

For example, the Plot.

ly drag and drop builder can create (almost) the same interactive charts that Python’s bokeh library does.

What is left after technical ability is not the defining factor of a data scientist?.The things I mentioned above, you, and your personal contributions to the data.

Technical skills are still importantI don’t want to give the wrong impression, it is always advantageous to have the technical skills to customise things to a great extent and cater the tools to your needs.

And if you have them please…Team Up!.As an enthusiastic citizen of Datatopia, where we all understand that everyone can contribute I cannot stress the importance of collaboration, which is why my first visualisation was produced working in as a team with Wei Qi on the World Happiness Report 2018.

I also worked with Darren Lim, Jaymee Justiniano and Yeo Shao Jie on the same dataset but with a different focus and purpose.

Apart from filling the gap in expertise, such collaborations are a great medium to foster peer-to-peer learning and “pick-up” skills from other citizens of Datatopia who are good at what they do.

The most critical skillSo you might be wondering by now: what is the most critical skill in Datatopia?.Is it collaborating?.Technical skills?.Even if everyone can contribute one has to hold the greatest weight.

You’re right, one skill does hold tremendous weight, and again it is accessible to everyone.

To your delight, while asking the question to yourself “how can I add value to Data Science” you are already practising the most important craft!Wait, what?Yes!.The most critical skill in Datatopia is asking questions.

The right questions will direct the entire process of data analysis: from where you get the data to how you process it and to how you choose to present it.

Your first data assignment or your next one is a simple google search away.

Knowing this, welcome fellow citizen of Datatopia and welcome Data Scientist!But how do we know how to ask the RIGHT questions?.For that, you need to read my next story: What are the RIGHT questions in Data Science?.

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