Data science is not only about statistical, mathematical, and technical work. The nature of data science-related tasks and activities also requires an individual to possess a certain vision, imagination, and creativity. These attributes incite professionals to seek answers for questions and explore their problems. 

Without the right passion, data science is a tough nut to crack. Aspiring data scientists need to realize that they can’t forge a strong career foundation based on hard skills (programming, mathematics, and statistics) alone; soft skills are equally crucial for their career ambitions. This has been proven by recent Google research. 

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Why Soft Skills Are Critical to Data Science?

Google ran a study to identify the most productive and innovative cogs of an organization. Surprisingly, it turned out that the best teams were not always comprised of the top technical talent. Instead, they turned out to be interdisciplinary groups that greatly benefited from professionals with soft skills—enhancing overall collaboration—which in turn improved business outcomes. Additionally, it was unveiled that the critical indicators of success within Google were discovered to be empathetic leadership, insights about others, and good communication.

Data science is a human pursuit. Organizations bring data scientists to augment their IT infrastructure by adding a humanized, unique angle along with their specialized skill sets. Thus, other than mastering technical skills, data scientists must polish their soft skills and strike a perfect balance between them.

There is a wide range of soft skills that can aid data scientists in generating business value for their company and boost their career trajectory. The role of a data scientist is fluid. Each data scientist will have a unique experience and comprehend their work differently because data science environments are distinct and inimitable.

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Soft Skills to Master

A firm grasp on the following soft skills can serve you throughout your career as a data scientist.

  1. Communication

    A data scientist should have a knack for linking business orientation with the scientific, analytical, and technical facets. They must communicate their findings to business users and decision-makers and explain what value these insights can bring to the business. This research must be conveyed effectively to both technical and non-technical audiences. In this way, they can promote data literacy within the organization, which can shine a spotlight on their contribution and make their role more visible across departments. 
  2. Curiosity

    Intellectual curiosity inspires data scientists to look for answers to address business crises. Professionals can dive deeper and go beyond the initial assumptions and surface results. It continuously begs the question “why,” preventing data scientists from being satisfied with a single answer. A data scientist must be inquisitive enough to not only unlock answers to known problems but also seek solutions for the hidden, overlooked queries. As a result, they derive a higher quality of insights from their data sets.
  3. Business Acumen

    Data scientists have to deal with a massive amount of knowledge. If they don’t translate it effectively, all of this priceless information goes down the drain because the upper-level management never gets to use it to make business decisions. This is why data scientists should equip themselves with knowledge of current and upcoming industry trends and acquire a foundation of basic business concepts and tools.

    They also must strive to obtain an extensive understanding of which strengths and weaknesses define their current organization. A shrewd know-how of their organization’s business objectives and market position can enable them to provide answers to current business problems and use data to accelerate the future growth of their organization.
  4. Storytelling

    Storytelling aids data scientists in conveying their results coherently and understandably. It takes data visualization to another dimension, allowing decision-makers to see things from a new perspective. A compelling storytelling approach builds a strong data narrative where stakeholders attain a new sense of clarity and identify the best course of action.
  5. Adaptability

    Adaptability is one of the most sought-after soft skills in contemporary talent acquisition. This is because technological innovation is climbing up at a rapid pace, forcing professionals to adapt to the latest technologies quickly. As a data scientist, you have to stay on your toes and respond to varying business trends.
  6. Critical Thinking

    Data scientists should be critical thinkers. It allows them to perform an objective analysis for a given problem, empowers them to frame questions correctly, and determine how their findings can help the organization and brings them closer to the required course of action. Analyzing problems objectively during data interpretation is prudent before you establish your own opinion. It encourages data scientists to take a closer look at the data source, examine all problem angles, and continue to remain curious—removing bias from the final output.
  7. Product Understanding

    As a data scientist, designing models isn’t everything. You have to offer actionable insights that can improve product quality. By gaining a complete product sense, professionals can accelerate quickly with a systematic approach. They can bootstrap models and enhance feature engineering. Additionally, this skill also aids them in perfecting their storytelling, revealing overlooked thoughts and insights about products.
  8. Team Player

    Data scientists don’t work inside a bubble. They must recognize the importance of teamwork and collaborate effectively with others. To do this, they need to listen to other team members and use their input to their advantage. 

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The Real Benefits of Soft Skills

Everyone can become well-versed in soft skills. A study found out that a factory achieved a nearly 250 percent return on investment when it chose to educate employees with a training program for soft skills. Thus, these skills can easily be imparted to the workforce. Soft skills can boost productivity, efficiency, and support decision making. 

Interestingly, these soft skills are future-proof. A survey predicts that automation will replace almost half of the current work activities by 2055. The impact of this paradigm shift will force workers in the United States and Europe to work more on their soft skills. Since it’s difficult for automated solutions to use artificial intelligence or machine learning to replicate soft skills, organizations should motivate their data scientists to embrace them.

Final Thoughts

Soft skills offer an excellent opportunity to cultivate and sharpen data science performance. Developing soft skills not only adds value to your organization but can also take your career to unprecedented heights. More importantly, unlike a lot of current skills, it can’t disappear overnight due to automation. 

Both aspiring and current data scientists can turn to Simplilearn, which is an industry-leading certification company that can help provide Data Scientists with all of the skills they need to become an asset in their domain.

About the Author

Ronald Van LoonRonald Van Loon

Named by Onalytica as the world's #1 influencer in Data and Analytics, Automation, and the Future Economy (Tech), Ronald is the CEO of Intelligent World and one of the top thought leaders in Data Science and Digital Transformation.

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