To become a great Data Scientist, learn the business.

The core of every Data Scientist's work (sorry, it's not coding)

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Many aspiring Data Scientists believe that the following skills are necessary to become a Data Scientist:

1. Coding

2. Statistics

3. Mathematics

4. Machine Learning

5. Deep Learning

6. Other technical skills.

The list above is accurate; these qualifications encompass most of what you need to become a Data Scientist in the current job market. These skills are often prerequisites in job listings, making them virtually unavoidable. Consider, for example, the Data Scientist job requirements and preferences listed below

Image from indeed.com

Most of the requirements sound technical; they include degrees, coding, math, and statistics. However, there's an underlying requirement for business understanding that you might not initially glean from the job advertisement.

If you look closely, they're looking for someone with experience applying analytical methods to solve practical business problems. This suggests that your daily tasks involve solving business problems, which means you'll need to understand the nature of the company's business and its processes.

You might ask, "Why do I need to understand that? Can't I create a machine-learning model and solve the problem?" This line of thought is misguided, and I'll explain why.

To remind you, what makes a Data Scientist great is not solely their coding ability, understanding of statistical theory, or even mastery of business comprehension. Instead, it's a combination of many elements.

Of course, anyone is free to agree or disagree with my perspective.

In my experience as a Data Scientist, no skill has felt as underrated as understanding the business. Early in my career, I even believed it was unnecessary to comprehend the business side of things. I was significantly mistaken.

I'm not ashamed to admit that I didn't initially consider business comprehension essential. Many data science educational programs and books didn't even include it in their teaching.

So, why is it crucial to understand the business, and how does this impact your role as a Data Scientist?

Consider this scenario: You work in the data department of a food company with candy as its main product. The company plans to launch a new sour candy product and asks the sales department to sell it. Knowing there's a data department, the sales team requests leads for potential sour candy customers.

Before anyone objects with "That's not our job, we create machine learning models!" or "I'm a data scientist, not a salesperson!", it's essential to realize that this is precisely what Data Scientists often do. Many of their projects involve collaborating with other departments to solve company-wide problems.

So, in our scenario, how should you approach this problem? You might think, "Just create a machine-learning model to generate the leads." Yes, that's on the right track, but how exactly do you build the model? On what basis? Is the business question even suitable for a machine-learning model solution?

You can't simply implement a machine learning model without proper context, right? This is why business understanding is so critical for a Data Scientist. You need to delve into the specifics of the candy business. Continually ask questions like:

1. "What is the precise business problem we're aiming to solve?"

2. "Do we even need a machine learning model?"

3. "What attributes are related to candy sales?"

4. "What are the candy-selling strategies and practices within and outside the company?"

And any other business-related questions that pertain to your industry.

It's essential to understand the nature of the business your company is involved in, along with all related facets. As a Data Scientist, your role will require you to make sense of the data in the business context.

While it's easy to assert that understanding business is essential, gaining that understanding is not straightforward.

Education plays a role; for instance, if you're applying for a data science position in a PR company, having a background in communications might give you an edge over someone with a degree in biology.

However, work experience can quickly bridge this gap. If you've had experience with a different job title in a similar industry, you'd have a significant advantage because you already understand the business processes.

For newcomers, breaking into the industry might be challenging, but there are also many benefits to being a novice. Recent graduates can:

1. Be well-prepared.

2. Exhibit eagerness to learn about the business.

3. Make an impact.

Newcomers should be targeted by companies that have already established their data journeys. These companies could teach a great deal about the business to these beginners, who often have no prior experience in the business world. In my opinion, one should never underestimate newcomers.

I'd also like to share a personal experience. When I first received a data project, I didn't think about the business aspect; I just tried to build the machine learning model. The results were disastrous.

I presented the model to the relevant parties with high expectations. I believed my model was excellent, I understood everything about the data, and I knew the theory behind the model I used. It seemed like a straightforward process, right? But I was so wrong. It turned out that the users didn't care about the model I used. They were more interested in knowing whether I had considered business approach "A" or why I used data that seemed unrelated to the business. The session ended with a conclusion: I required more business training.

It was an embarrassing situation, but I'm not ashamed to admit that it was my mistake to overlook the importance of business understanding. I could excel at model creation or statistics, but ignoring the business aspect proved to be a catastrophe. Since that day, I've made it a point to learn more about the business process itself, even before contemplating any technical aspects.

Conclusion

Whether you're a newcomer or not, strive to understand the business as much as possible.

Focus on an industry that interests you - finance, banking, credit, automotive, candy, oil, etc. Each business has its own approach and strategy; you just need to concentrate on learning about your preferred industry.

Securing a position as a Data Scientist is challenging. Entry into this field is difficult, with numerous applicants with similar skill sets. To stand out, you need a unique attribute. A thorough understanding of business is the skill that will undoubtedly distinguish you from the rest of the competition.

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