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Let’s Put Soft Skills on the Spot for a Little While

It is time to close the gap between technical and non-technical people! Master these eight soft skills to shine as a data scientist

Photo by Farnoosh Abdollahi on Unsplash
Photo by Farnoosh Abdollahi on Unsplash

Many organizations do not experience full gains from introducing Data science methods. This is not because the technology is unmatured. It is simply because the gap between the mindset of a data scientist and the organization is still too wide. The mindset of a data scientist is too focused on the technical aspects.

If we expect organizations to have faith in data science methods we need to focus more on soft skills! By doing so we can build a strong bridge to close the gap and invite non-technical people into our world.

Shine as a Data Scientist with Great Soft Skills

Photo by Greg Rosenke on Unsplash
Photo by Greg Rosenke on Unsplash

In the Data Science community, I see an overwhelming bias towards hard skills. Let’s not forget about the soft skills!

Even the most hard skilled data scientist cannot shine without developing soft skills. Without the ability to communicate with others your technical skills are of no value

In this post, I highlight five Soft Skills to master to shine as a data scientist including:

  1. Effective Communication
  2. Clear Presentation
  3. Problem-Solving Mindset
  4. Critical Thinking
  5. Prioritize Creativity

For each of the skills, I will elaborate on: Why the skill is important, what you will learn, and how to master the skill. As a side benefit, this will also make you super attractive in most companies. Let’s dive in.

6 Key Soft Skills Makes the Difference

Photo by Nikola Knezevic on Unsplash
Photo by Nikola Knezevic on Unsplash

1. Effective Communication

Effective communication is key to present purpose and results. This ensures no misalignment and confusion.

"Good communication is the bridge between confusion and clarity", Nat Turner

By mastering this skill you will be able to: Explain complex technical terms to non-technical people and communicate results in a precise manner.

Strive for non-technical and straightforward wording. When communicating data science material do the following:

  • Limit the amount of using data science buzzword
  • Focus on the purpose and what can be concluded from the results
  • Highlight the key takeaways before going more into detail

For more information on this please check out my towards data science article: 20 Data Science Buzzwords and What They Really Mean

2. Concise Presentation

Making decisions based on a clear presentation requires much less energy and brainpower. Decisions can easily be made when the message is clear and well-considered.

"Ask yourself, ‘If I had only sisty seconds on the stage, what would I absolutely have to say to get my message across’", Jeff Dewar

Practice these core presentation techniques:

  • Spend time to build a clear storyline in your presentation
  • Put the conclusion first for others to understand the context
  • Then dive deeper into the analysis results
  • Practice your presentations for yourself and/or others
  • Strive to always interact with your stakeholder when presenting to keep focus. Ask questions doing the presentation or ask for examples

3. Problem-Solving Mindset

Photo by Ryoji Iwata on Unsplash
Photo by Ryoji Iwata on Unsplash

Bugs are inevitable but only a true problem solver will have the drive to identify the root cause. Only from this, you can propose an optimal solution.

"If I had an hour to solve a problem I’d spend 55 minutes thinking about the problem and 5 minutes thinking about solutions", Albert Einstein

By mastering this skill you will be able to identify optimal solutions based on your data science toolbox. This will allow you to identify and solve tricky bugs.

Curiosity is key. Put some effort into solving problems in all aspects of life. This can be everything. See examples below:

  • Solve puzzles
  • Understanding how a guitar works.
  • Solve complex mathematical problems

4. Critical Thinking

The ability to think critically about problems can simplify the working process. This skill is indispensable for a data scientist. We often face complex problems. These need to be both broken down into smaller sub-questions and limited in scope.

"It is the mark of an educated mind to be able to entertain a thought without accepting it.", Aristotle

Critical thinking implies asking the right questions to secure an efficient process. Strive to objectively analyze the problem and results. Being hypothesis-driven will limit the number of eventual detours in a project.

Master this skill by continuously asking the question "Why?". Never accept black-box results or methods.

5. Prioritize Creativity

Photo by Alice Dietrich on Unsplash
Photo by Alice Dietrich on Unsplash

Creativity is a difficult size. Luckily it is a skill you can train. One thing is certain, applying a creative mindset in your everyday job can be game-changing.

For me personally, I use Medium to strengthening my creativity. This is a place where I can let my mind loose. Here I can write about what comes to my mind. Find your own creativity spot but accept that you might need to test different platforms.

By mastering this skill you will be able to:

  • Allow your mind to be more creative
  • Improve research results or presentations by thinking creatively

Concluding Thoughts

Photo by Miriana Dorobanțu on Unsplash
Photo by Miriana Dorobanțu on Unsplash

Mastering simple soft skills can unlock the full potential of data science. Let’s build a solid bridge between data scientists and non-technical people.

In this post, I highlighted five soft skills to master to shine as a data scientist including:

Communication, Presentation, Problem-Solving, Critical Thinking, Creativity

Let’s put some effort into developing our soft skills to make this journey as smooth as possible!


What are your thoughts? Has this changed your view on the importance of soft skills? Feel free to leave your thoughts in the comment section.

Thanks for reading


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