The 7 habits of Data Science

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This post was originally published by Ignasius Harvey at Towards Data Science

Every day, I go through my routines. Repeat.

In the world of data science, most of the work I do is exploring unknown realms (read: data). The workflow is more or less similar to every project iteration. Data gathering — exploration — process—modeling — test — deploy—monitor — present — repeat. I wonder then, how can I make sure I’m not just repeating the same cycle over and over again? How can I do better?

The answer I found is: adapt my habit. How you: view and reframe data science problems, create mutual collaboration, and maintain effective communication are habits that are often overlooked. Remember, data science isn’t about building the best model but making positive impacts through data. Your habit affects your respond to each problem.

Inspired by Stephen R. Covey, The 7 Habits of Highly Effective People changed me.

It’s still a powerful book to read for me. Data science or not, applying these habits in my workflow boosts up my productivity. I want to share with you, these habits that have put me in a positive frame of mind.

– So here’s my take on the 7 habits, seen through my lens of data science.

– As we go through it, ask yourself, do you need to work on these habits?

Well, not that kind of active. Instead, take initiative for the greater good.

Data science isn’t about doing what stakeholders want. It’s the tool to growth hack and solves business problems. No one understands the data more than you do. An effective data guy should recommend on how data can be utilized! People may not be aware of the power of data.

Focus on what value the data can bring!

“Reactive people are driven by feelings, by circumstances, by conditions, by their environment. Proactive people are driven by values — carefully thought about, selected and internalized values.” — Stephen Covey

Data science isn’t about finishing your work but finding the best path to your goal.

When answering business questions, figure out what the outcome will be. Without it, no project can be well-planned and well-executed. How can we work on something when we don’t know the scope? An effective data guy should have a good idea of how the project will be done. Best practices for each project may differ.

Whether it be a dashboard, a model, a data product, or a spreadsheet file; what’s the best output and how to do it?

“To begin with the end in mind means to start with a clear understanding of your destination. It means to know where you’re going so that you better understand where you are now and so that the steps you take are always in the right direction.” — Stephen Covey

The Eisenhower Decision Matrix. Image by Author.

Data science isn’t about how many analysis you’ve done.

Tackling the most important and urgent problem must be prioritized. If a data science team is overwhelmed by the sheer number of requests, these 4 quadrants must be made clear. An effective data guy knows what should’ve been done first. Why should we bother on insignificant things?

To measure what’s important, what’s urgent, and always stick with the highest priority will pay off in the long run.

“The key is not to prioritize what’s on your schedule, but to schedule your priorities.” — Stephen Covey

Data science isn’t about your work performance.

Ever hit a roadblock with anyone and wanting to solve it your way? Great organization collaborates within (even outside). Thinking how both sides win is essential to gain trust and secure long term success. An effective data guy knows that everyone can win! If one can’t win, think again.

Focus on solutions that are not one-sided or even just a quick fix.

“It’s not your way or my way; it’s a better way, a higher way.” — Stephen Covey

Data science isn’t about how complex your project is.

Most data people think numbers & logic-driven, while the rest majority don’t. Not all problems can be explained by numbers though. The key to attaining a full understanding of people is empathy and open-mind. An effective data guy should understand first before making assumptions. False approach is less effective.

Seeking another point of view may open up another better possibility. Let’s listen more!

“Most people do not listen with the intent to understand; they listen with the intent to reply.” — Stephen Covey

Data science isn’t about your solo-work.

For a well-built plan to be executed with perfection, teamwork is essential. Imagine this: data modeling without implementation; dashboard without user’s defined metrics; analytics without data engineering. Useless. An effective data guy must be able to work in sync with others. Organization exists because everyone is in it.

Seek collaboration! Each person may offer different and better alternatives.

“Synergy is better than my way or your way. It’s our way.” — Stephen Covey

I have realized, data science is HUGE.

A deeper understanding of data science can only be obtained through consistent learning. Strive for more knowledge and adapt to new problems. There’s no one size fits all. An effective data guy is always ready to tackle any problem. Develop yourself and take on the challenge!

Remember that you are your greatest asset!

“Unless you’re continually improving your skills, you’re quickly becoming irrelevant.” — Stephen Covey

I’ve known these habits for around 5 years now, yet I’m still working on it. Controlling habit is hard since they force you to get out of your comfort zone. But, instead of worrying over things you can’t control, remember that you only have control over yourself.

“Start small, make a promise and keep it. Then, make larger promises and keep them.” — Stephen Covey

Going further, there’s actually the 8th habit. Find your voice and inspire others to find theirs. The talk shifts from effectiveness to greatness. Expressing vision, discipline, passion, and conscience — into inspiration. Consider reading it if you’re a data science leader!

One step at a time! Stay safe and healthy, folks.

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This post was originally published by Ignasius Harvey at Towards Data Science

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