Top 6 Soft Skills to Absolutely Have as a Data Scientist

Hone up before you start your job as a data professional

In an exploding field like data, it is very easy to be lost finding your way to become the unicorn. The expanding world of business today demands data wizards to not only excel in technical skills, rather, bring a perfect confluence between soft and hard skills on the table.

Graduating with a major in Data Science & Analytics and working professionally now as a Data Analyst, one transition that only business experience teaches you — incorporating soft skills into your body of work. I cannot stress enough how important it is to have the right mindset for the business.

Just Python, Excel, SQL or Tableau IS NOT ENOUGH for a data professional!

Having said that, here are xx soft skills that I developed over time and I believe are quintessential to grow in the ecosystem of data.

1. Business Acumen

Data science is a technical field and my strengths are stats, math and computer science. Why should I have business acumen as a data scientist?

Understanding the business comes prior than even opening up your Jupyter notebook or setting up your data environments. After 800 lines of code, if you do not understand what an error means for the business, you miss the mark from correct decision making.

The goal behind understanding business first is to translate data into results that work for the organization.

Businesses really would want their head-liners to know what organizational problems need to be solved and why, the impact of business decisions, outcomes of data projects and how it completes an entire data science life cycle as well.

Goals of business acumen for Data Science

• Create avenues for business to extracting result-focused insights from data
• Identify business opportunities by understanding how value and information flows in a business
• Plan for all areas of a business’ operations and how they are interconnected

2. Storytelling

Everyone loves a good story and when the protagonist is data, it’s quintessential you are good at telling a comprehensive story with data.

And by storytelling I mean communicating a past-present-future of the data on-hand. For instance, you are leading marketing analysis for a new product and your business launched a campaign to boost the sales. The business would like to see the performance across demographics, out-of-stock analysis, shopping cart abandonment ratio and the best way to put a coherent analysis is a story — pre and post-campaign comparisons, moving down on granularity, and only more you can do.

Goals of storytelling for Data Science

• Derive and pass on data-driven insights in business-relevant terms and language
• Convey derived insights to prove value of action and set a premise for long-term processes
• Communicate complex analyses as actionable insights in a language comprehended by business and IT

3. Curiosity

Data curiosity is the secret ingredient for a successful data-driven organization.

A good data professional is one who can identify opportunities from problems, drive the search for answers, and also ignite curiosity in stakeholders or consumers of analysis.

Curiosity often awards you in the most unexpected ways — be it professionally or for your team. The eagerness to know more about the problem on hand or instinctively seek new or existing data, question it, and use it to make more informed decisions

Goals of curiosity for Data Science

• Discover ways to approach challenges, starting with identifying existing assumptions and resources
• Wear your detective’s hat and research about the most effective methods to lead to the right answers
• Dive deeper than surface results and initial assumptions
• Think creatively with a drive to know more

4. User & Product Understanding

As a data scientist, designing models isn’t everything. Time and again, you are required to offer actionable insights that can improve user experience and product quality.

Now, you would argue that I do not have a product/client facing role and cannot relate to the skills. But the way I look at it is, each product/service/solution has a consumer and if your project has an end-user, everything will become relevant.

A comprehensive sense of your product and users can facilitate professionals to accelerate quickly with a systematic approach. It is quintessential to understand how people use products, measure the impact of new features, and prioritize the opportunity of potential new projects.

Goals of user/product understanding in Data Science

• Bootstrap models and enhance feature engineering
• Create targeted strategies per user’s needs, demands, pain points
• Develop data-driven hypotheses for new A/B tests
• Design actionable metrics
• Explore unbalanced classes, user and product sense, product metrics

5. Effective Research

Sometimes, the answer to ‘what is the best way to represent this data?’ opens up a horizon of opportunities to unravel implicit insights.

The world of data science and the roles of those who work within it are changing fast.

Because of this, one of the most important skills to cultivate the sense of representation. If you do not know the best channel to tell your story, all you do goes in vain.

I personally research a lot when it comes to working on a novel problem statement. Effective research leads you to adapt an existing approach and modify/build on top of it — not having to reinvent the rocket from scratch.

Research helps to deliver a working solution with lesser time and effort. It can be reading published papers, studying GitHub projects or repos, public dashboards.

Goals of effective research for Data Science

• Widens the perspective to solve a problem
• Keeps us up to date with the most efficient way of solving a problem
• Contribute ideas to what the team is working on outside your body of work

6. Critical Thinking

By definition, critical thinking employs other skills as well such as curiosity, creativity, skepticism, analysis, and logic. But what’s important here is the realization.

In some article I read, the relationship between critical thinking and data analysis is reciprocal: While analyzing data strengthens critical thinking, critical thinking in turn helps data analysis. Meaning, data analysis requires you to think critically by probing, connecting disparate facts, synthesizing. And, critical thinking is enabled by the ability to think analytically and apply tools to help extract insights and actionable information from data.

Goals of critical thinking for Data Science

• Objectively analyze questions, hypotheses, and results
• Understand what resources are critical to solve a problem
• Look at problems from differing views and perspectives

That’s it from my end for this blog. Thank you for reading! If you have any more soft skills in mind that have helped you in your data journey, let me know in the comments and I would love to know more about them.

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Happy Data Tenting!

Rashi is a data wiz from Chicago who loves to visualize data and create insightful stories to communicate implicit insights. When not rushing to meet school deadlines, she enjoys blogging about data with a good cup of coffee…