You Ask, I Answer: Data Science Soft Skills?

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You Ask, I Answer: Data Science Soft Skills?

Monina asks, “How can marketers adopt a data science mindset? Outside of hard, analytical skills, what soft skills should marketers possess?”

Data science is exactly what it sounds like: performing science with data. The soft skills which make for a great scientist thus transfer to a data scientist and any marketer who wants to adopt a perspective of using the scientific method to improve their marketing. The seven data science soft skills are:

Curious
Humble
Open
Patient
Persistent
Passionate
Resilient

It’s so important to note that if your workplace lacks or actively opposes these qualities, your ability to grow will be seriously hindered.

You Ask, I Answer: Data Science Soft Skills?

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In today’s episode menina asks, How can marketers adopt a data science mindset outside of heart analytical skills? What soft skills should marketers possessed? So data science is exactly what it sounds like.

It’s performing science with data using data.

And so, when it comes to soft skills for data scientists, when it comes to soft skills for marketers who want to behave like and function like data scientists, the soft skills that make for a great scientist, thus transfer to a data scientist and to any marketer who wants to adopt that perspective of using science using the scientific method to improve their marketing.

So, there are, I think, seven major soft skills that a good scientist a good data scientist, a good marketer should have In the pursuit of using data to improve their marketing, so let’s actually bring this up here.

those skills open, resilient, curious, patient, persistent, humble and passionate.

So let’s talk through these.

Number one is open.

Great scientists are communicators.

They’re open books, they are open to the world, they are open to discussion into debate, their minds are open to possibilities, that openness allows them to communicate really effectively, someone who is very closed off, it’s not a great communicator.

And a key part of data science is being able to communicate your results to somebody else to another human being, and explain to them why the work that you’re doing matters and how it impacts them.

So that’s number one.

Number two is resilient.

The data scientist any scientist has to be comfortable with possibly even friendly with failure, tons and tons of failure.

The amount times that an experiment won’t work or that your code won’t work or that your data is screwed up our legendary their region.

And so what happens is that you fail a lot and you fail at every part of the data science lifecycle.

Until you get it right.

The very idea behind what’s going on in data science is that you have to be ready to fail, so that you can get to success eventually.

So that resilience is so important to be able to bounce back from failure after failure after failure.

Probably the only profession that fails more than the science of sales, right? 99 rejections 100 rejections 1000 rejections? Same here, you’ve got to be resilient.

Gotta be able to bounce back from failure easily so that you can get to success learning all the time.

Number three is curious.

You as a scientist have to be curious, you have be wondering about things and that that curiosity should drive you should motivate you to want to find the answer no matter what.

Even if, you know you have to work extra hours or you’re working outside of work, or it’s a pet project, or whatever the case may be.

Curiosity is essential to being a scientist.

If you’re in curious where you just want to get to the answer, get to the answer, get your work done.

You don’t care.

If you’re not, you don’t want to go on it down a rat hole, you just got to get things done.

That’s a problem.

If you’re a scientist because it means you will not want to get to the actual answer whatever it is.

Number four is you have to be patient.

Data Science takes a long time and the in the data science lifecycle.

There’s a whole phase of getting the data and extracting it, cleaning it, transforming it, preparing it takes so long depending on the data set.

I’m in the middle of a project right now where it takes time.

half a day just to get the data loaded and cleaned up before you’re ever ready to do anything fun, sexy and awesome and super tactical.

Now, there’s a whole bunch of drudgery that you have to be patient with and you have to be patient with the scientific method.

You can’t hurry it, you can’t make results happen instantly.

This Bible goes back to the openness because you also have to be able to communicate this to your stakeholders like yeah, this stuff is not instant.

It’s not, you know, snap your fingers and it’s done.

That’s not how math works.

Number five is you have to be persistent.

You have to keep digging for the answer.

keep striving even if you run into challenge after challenge it challenge.

Resilience is bouncing back from failure.

Persistence is keep going in the face of failure.

One of the face of challenges and when something difficult comes up to not shy away from it to say, Yep, Scott, keep on hacking away at this thing.

Number six is need to be humble.

And this is Really important for being effective at communicating and collaborating with others, you have to be willing to let the work be first as opposed to yourself.

So it’s fine to have a personal brand that’s fine to, to build yourself as a marketer and have the things you do.

But the work has to come before you do if you’re all in on your personal brand, and like, look how awesome I am and look how how, you know, technological I am, that turns people off that makes collaboration really hard.

And that also can poison your data.

Because in your efforts to become known for something or to be seen in a certain light that contains how you approach your data.

You become in curious when you’re not humble, right? You You have to start thinking about having an outcome in mind that’s going to make you look good, as opposed to focusing on the work and what the work and what the science is.

Gonna tell us, if you’re humble and you can work with others, you can let other people take credit.

But you are focused on doing the good work.

That’s that’s how you will achieve long lasting success.

And the last, you have to be passionate, you have to love the various aspects of data science, you may not love them all equally, and that’s fine.

But business and domain expertise, technical skill, mathematical skill, scientific perspective.

Those are things that you have to be passionate about.

You have to really love the work and the math and the coding and all these things.

Because otherwise, it’s if you don’t love it, it’s very hard to be persistent.

It’s very hard to be patient.

It’s very hard to be curious if you’re just if your heart’s not in it.

Having that that passion that drive, that motivation, that love of the science that you’re trying to do the answers that you’re trying to find the mysteries that you’re trying to solve.

That’s what it’s going to make you a great scientist.

When you think about some of the great scientists in the world and you think about some of the, the science personalities out there, you know, the Carl Sagan, Neil deGrasse Tyson’s, they you can see that passion for their work in what it is that they do.

And you can see these other traits as well.

So these traits are things that soft skills you have to develop.

Figure out which one you’re weakest at and start figuring how can you put yourself in safe situations that are uncomfortable to train yourself to develop these personality traits more to flesh them out, or if you don’t have them, team up with people who do to compliment you in the spots that you’re weak.

So that’s the soft skill stuff, which by the way, it’s not soft skills, its its underlying foundations that drive you to develop the analytical skills.

Without these characteristics.

You won’t ever want to be common data scientist or a scientist or marketer, who is data driven, you just won’t have those things right.

And if you work in a workplace was actively discourages these traits, that’s going to work against you.

So make sure that you’re working in an invite in an environment for people who are open and resilient and curious and patient and persistent and humble and passionate.

If your workplace doesn’t have these things, you’re gonna have a very, very difficult time and developing them yourself and expressing them in your work.

So really good question is important question because these are the prerequisites to being a good data scientist.

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