data scientist

Technical skills are overrated, particularly in data science. Many data scientists quickly realize that much of their job challenges aren’t due to what they can or cannot do. Rather, the mentality with which they approach tasks matters a lot.

For instance, a data scientist who has mastered communication will present their insights better than their more (technically) skilled counterpart whose reports are jumbled. Likewise, extrapolating insights from raw data require a huge dose of creativity and critical thinking, both of which are not taught as technical skills but must instead be developed personally.

Other soft skills that are necessary for data scientists include business aptitude, problem-solving, and adaptability.

All of these are time-proof skills that transcend technological innovations. Success in 2021 and beyond as a data scientist will heavily rely on the development of these soft skills.

Critical Thinking

This author defines critical thinking as “the judicious and objective analysis, exploration and evaluation of an issue or a subject in order to form a viable and justifiable judgment.”

Critical thinking is often regarded as the most essential skill in data science.

It makes you well-informed, enhances your judgment, and makes you better equipped to make more effective decisions. As a data scientist, you must be capable of examining the available data from multiple perspectives. To develop critical thinking, do the following:

  • Question your assumptions: as a scientific field, your job is to apply empirical methods to analyzing data and extracting insights. However, the human mind remains subject to all kinds of biases and presuppositions. You must thoroughly interrogate them to hone your reason and avoid decision pitfalls.
  • Engage different perspectives: As social beings, we are drawn to people who act and think like us. But the lack of healthy dissent leads to poor decision-making. Thinking critically means consistently seeking out fresh perspectives. This doesn’t necessarily mean disagreement; it could be as simple as connecting with colleagues from another department in order to understand their outlook.

Communication

The purpose of data analysis is to make informed decisions. And your responsibility as a data scientist includes being able to present your findings in a clear manner to the non-data-scientists who have to make the decisions.

Your non-technical audience needs to know how you reached a specific conclusion, the justification for your methods, the implication of your findings, and why you consider one solution better than the other.

You can make your presentation more effective through storytelling. As Brent Dykes says in his book, Effective Data Storytelling,  “…narratives are more compelling than statistics if your goal is to make an impact on your audience.”

Visuals achieve the same effect; when used right, they help your audience see and understand patterns between scraps of data. Your insights don’t matter unless you can make others understand it and drive them to take the necessary actions.

Problem Solving

A data scientist is like a detective. Both workers investigate the available facts and data to address problems. In one case, the purpose is to solve crimes; on the other, the purpose is to deliver business value.

Data is what we make of it. And a data scientist needs to be resolute at, and equipped for, investigating issues to the root. Project managers love a data scientist who can identify creative solutions to problems.

For instance, discovering that your company’s customers behave in a certain way is different from why they behave so. And even then, the job is most likely not done. You must still use the available data to determine how to make the customers behave differently or to make the company adapt to the customers’ habits.

Data science is a continuous job of evaluating data and weighing options, determining why one approach to fulfilling a goal is better than the other. The consequences of your conclusions could be massive; so you need to get it right, at least based on the data available to you at the time.

Practice makes you a better problem-solver. There are websites that help you learn to tackle various data science challenges with real business impacts.

Business Aptitude

Analyzing data is one thing; contextualizing it to solve real business problems is another. Dr. N. R. Srinivasa Raghavan of Infosys is widely quoted thus: data science is more than just number crunching: it is the application of various skills to solve particular problems in an industry.

Without a good understanding of business processes and operations (such as supply chains, customer service, finance, human resources, logistics), it would be impossible to extrapolate actionable insights.

Data science is a field involving so much theory but has far-reaching practical implications. Therefore, a good data analyst is one that understands the business model and can quickly adapt to various business situations.

How does the business work? How does your company work? What do you know about your industry? How does your company make money? What product/service does your company deliver, and how does that work? What makes your company lose money? Who are your competitors?

These questions, and more, are important to understanding business operations. You can develop this by research. But you first need to possess a keenness for business and understand that data science is not just about Python, SQL and all the technical parts.

Adaptability

Adaptability has to do with how quickly you are able to adjust to new conditions, which may be positive or negative. In this information age, innovation grows at such a rapid pace that it is often difficult to keep up. We are living in a world of possibilities, and what’s new today can become outdated in a few months or years.

In fact, the tools you use for data analysis five years from now may be different from the ones you employ today.

Adaptability is also important for moments of crisis, a time when data scientists come under greater pressure to deliver. Consider the COVID-19 pandemic. The global spread of this virus has disrupted business operations everywhere and altered, perhaps permanently, the course of work and business.

When there is a setback, people seek answers; they want to know exactly what went wrong and how they can move forward.

Today, everyone relies on data. In this world of several unprecedented changes, you must be ready to adjust to the prevailing trends.

Conclusion

Soft skills deal with how you approach data. You may know all the technical bits of data analysis, but a wrong approach almost always leads to wrong results.

More importantly, the technical aspects may change. In five years or a decade, the currently popular data science tools may be entirely out of the limelight, edged by newer advanced tools.

But skills such as critical thinking and problem-solving will endure. Developing these skills early is a great way to secure your career in the future.

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