7 Important Soft Skills For Data Scientist

Mamta Mitali

Jul 13, 2021
7 Important Soft Skills For Data Scientist

Data science is one of the most sought-after jobs in technology, and data scientists are in high demand in almost every field. Data scientists act as the middleman between the company's goals and product strategy.

To be successful, you must have the right soft skills because you will need more than just technical skills to overcome these challenges.

What are Soft Skills?

Before we get to know which soft skills every data scientist must have, let’s understand what are soft skills? 

“Soft skills” are a set of positive characteristics and competencies that can improve job performance and productivity, strengthen relationships, and make an individual more marketable in the workplace. Creativity, problem solving, teamwork, empathy, and versatility are all examples of soft skills.

While “hard skills” such as technological expertise or computational skills are the most important criteria for employment or grades in colleges/schools, soft skills are now considered equally important.

Similar to core data scientist hard skills like programming or statistics, mastering soft skills leaves a lasting impression on interviewers, assisting you in your career transition. The success and productivity of teams in any type of organization are dependent not only on their knowledge or technical proficiency but also on the soft skills of each team member.

What is  the Need for Soft Skills for Data Science?

Data science is more than just statistics, math, and technology. The nature of data science-related tasks and activities necessitates a certain level of vision, imagination, and creativity. These characteristics motivate professionals to seek answers to their questions and analyze their problems.

Data science is surely not very easy to master without the right passion. According to various Job portals surveys, it has been found that aspiring data scientists must recognize that they cannot build a strong career foundation solely on hard skills (programming, mathematics, and statistics); soft skills are also essential for their career goals.

According to industry experts, simply hiring a data scientist is insufficient. Managers must exercise special caution in aligning business and data teams, allowing data scientists to be self-sufficient.

There is a wide range of soft skills that can help data scientists boost business value for their company and boost their career trajectory. A data scientist's role is ever-changing. Because data science environments are distinct and unique, each data scientist will have a unique experience and perceive their work differently.

Let’s have a look at different soft skills that you as a Data Scientist must possess: 

  1. Communication: Why? Because your job will frequently demand for explaining various insights, trends, and nuances related to your data to customers, upper management, the general public, and so on. You must communicate effectively clearly and concisely so that the customer fully understands the context of the data, how it should be used clearly etc. Being able to communicate effectively also allows upper management or decision-makers to be confident in data-driven decisions.
  2. Teamwork: Data scientists cannot work alone, so it is critical that you can work well as part of a team for projects and tasks to be completed correctly and on time. Data scientists collaborate with business executives to develop strategies, create better products, launch improved conversion campaigns, and work with server and client software developers to improve workflow and create data pipelines. Data scientists, regardless of their field, collaborate with everyone in the company, including customers. There will be numerous challenges along the way, so you must consider other people's opinions and thoughts, even if they differ from your own.
  3. Critical thinking: With this skill, you will be able to do analyze questions, hypotheses, and results objectively, recognize which resources are required to solve a problem, and examine problems from various angles and perspectives. When dealing with data interpretations, it is also critical to analyze problems objectively before forming an opinion. In the field of data science, critical thinking entails seeing all sides of a problem, considering the data source, and remaining curious at all times.
  4. Storytelling: The non-technical audience must understand why data scientists came to a particular conclusion. There has to be a better way to deal with this than to use the concept of storytelling. At the end of the day, what matters is how convinced the audience is of your findings, and if people are unable to comprehend the same, then these findings are meaningless.
  5. Problem-solving: A data scientist who is unable to solve problems does not do justice to the job description. Finding the best solution to the problem is also critical. As a data scientist, you should be able to identify the problem area or the tricky issues, come up with various approaches to deal with them, and finally apply the best method to solve the problem.
  6. Business Understanding: As a data scientist, you must understand things that are unique to the industry in which you work. Companies ensure data scientists have domain knowledge and can translate data to results for them, in addition to knowing a lot in your field. A company's primary goal is to make a profit. As a data scientist in business, you are expected to add value to the organization through data.
  7. Flexibility: One set of data may not be providing you with what you require, or one of your other stakeholders may have changed their preferences, or you may simply be unable to get that ‘One Excel Function To Work!’ Because most data scientists are perfectionists, detail-oriented, and slightly OCD, mid-project course correction can be extremely frustrating. However, if you stick with it, you'll often discover that taking a step back and being open to change can help you become even better at your job.

How you can learn these skills?

Yes, you heard it right! You can learn these skills by PRACTICING!

Practice! This is how to develop soft skills. After all, it would be pointless to develop collaboration skills and then isolate work responsibilities. Most soft skills can be developed at your own pace by putting yourself in situations that put them to the test...

Take the opportunity to take your newly acquired soft skills for a test drive. Outdoor retreats provide a fun and risk-free environment to practice soft skills like teamwork and communication. Opt-in assignments give the chance to practice problem-solving and creative thinking skills.

Remember that you won't know how to improve or develop soft skills unless you know which soft skills need to be improved. You should reflect on the soft skills you already possess and be honest with yourselves about the skills that could benefit from little improvement.

Soft skills provide an excellent opportunity to develop and hone data science performance. Soft skills development not only adds value to the organization you will be working with or are working with but can also propel your career to new heights.

At Perfect eLearning, we will help data science students/professionals with all the skills they need to become an asset in their field. You can always contact us via our contact details to learn more. Hope you have an idea of the soft skills which the data scientist needs to be the best in the field.

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