25 Sep 23

The Importance of Data Science Soft Skills

Hermione Lamb

In the rapidly evolving field of data science, technical skills and knowledge are undoubtedly essential for success. However, it is often overlooked that data science soft skills such as communication, data storytelling, and confidence, are equally important. In fact, these data science soft skills are often what set exceptional data scientists apart from the rest. Grasping the nuances of data science soft skills can drastically improve one’s capability in this field. This blog explores the importance of these soft skills in data science, highlighting both their significance and my own experience and growth. By understanding how these skills contribute to success and learning ways to develop and strengthen them, aspiring data scientists can unlock their true potential in this competitive industry.

In this Article

My journey into data science

I have always had a skill for numbers and problem-solving, which led me to pursue a degree in mathematics. Throughout my academic journey, I gained a solid foundation in a range of technical skills such as programming, statistics, and analysis. These technical abilities, combined with the initial foray into data science soft skills, undoubtedly boosted my career. They allowed me to effectively analyse and interpret complex datasets. The ever-growing volume of data and the increasing demand for insights intrigued me during my final year of university, pushing me towards a career in data.

However, as I entered the professional world of data science, I quickly realised that technical skills alone were not enough to succeed. I noticed that the most successful data scientists not only had a strong technical background but also possessed exceptional data science soft skills. Due to my degree and nerves around public speaking, I knew that this was a weak area within my skill set, and was keen to develop both my confidence and experience with soft skills.

This is where the training course at Rockborne was key, in which there were soft skill sessions integrated with the technical training. This provided a unique space to develop these data science soft skills, but also highlighted their importance in relation to data and how the two interlink.

Soft skill training at Rockborne

My data science soft skills training at Rockborne was led by Liz Needham where a wide range of topics were explored via lessons, interactive exercises, and real-life scenarios. Some areas explored were interview practice and how to sell yourself, stakeholder management and assertiveness, presentation skills, and data storytelling (a topic I found particularly interesting). The mentioned topics are explained and explored in more detail below:

1. Interview preparation and practice

As a data consultant, effectively communicating expertise in both technical and data science soft skills during client interviews is key. For this topic, I found that the most value was found within the numerous practice interviews that were conducted. These practices came in a wide range from technical and non-technical interviews to different interviewer personalities. The simulated client interactions helped me practice how to showcase my expertise and tailor my approach to match each client’s unique needs. From the exposure I have received during training, I feel a lot more confident in my approach to tricky questions and articulating thoughts in the most effective manner.


A man writing on paper with a pen


2. Stakeholder Management and Assertiveness

In the realm of data consultancy, stakeholders come from diverse backgrounds and hold varying expectations. Meaning it requires adept stakeholder management, another crucial data science soft skill. The training’s focus on stakeholder management pushed me out of my comfort zone. Through interactive sessions, I learned to identify different stakeholder personas within client organizations and adapt my communication style accordingly. Role-playing scenarios challenged me to think on my feet, engage with empathy, and navigate conversations with clarity. These experiences not only improved my client interactions but also highlighted the importance of building a solid rapport with the individuals allowing to drive projects forward and feel comfortable expressing my ideas.

Alongside stakeholder management, the topic of assertiveness was explored showing how to push back or negotiate requirements and deadlines, provide project recommendations appropriately, and the borders between passive, assertive and aggressive. As a data consultant, there may be scenarios where stakeholders or peers have less technical knowledge of a project or have unrealistic visions. In these cases where a requirement or deadline cannot be met, being able to convey hurdles or suggestions in an appropriate – assertive – manner is key.


Stakeholder management


3. Presentation

Another crucial data science soft skill, delivering presentations is at the core of a data consultant’s role. The training deepened my understanding of presentation dynamics – from structuring content to utilizing visuals to enhance impact. Through hands-on workshops, I learned how to captivate an audience and convey complex insights with clarity. These sessions and practices were a large area of training that helped to increase my confidence and grow used to explaining methods, reasoning, and solutions to a range of audiences.




4. Data Storytelling

Perhaps the most transformative aspect of the training was the emphasis on data storytelling – a skill that lies at the heart of effective data consultancy. I learned to weave together data, context, and actionable recommendations to craft narratives that resonated deeply with clients. This skill enhanced the way I communicated my findings, turning data into a captivating narrative that drove actionable insights and decisions, and helping to highlight key findings.


The Importance of Data Science Soft Skills

It’s evident that the need for data science soft skills is imperative in nature. While technical aptitude lays the foundation, it’s the mastery of these often-unspoken skills that elevate a data scientist from good to exceptional. Here’s why soft skills are an indispensable tool for success within data.

Data science soft skills empower data scientists to communicate intricate insights with clarity. By honing clear articulation, active listening, and empathetic engagement, we ensure that the significance of our findings transcends technical jargon. This ability to translate complex data into relatable narratives magnifies the reach and resonance of our work.

With data storytelling, raw numbers can be transformed into compelling stories that resonate across audiences. These narratives don’t just inform; they inspire decisions, combining data-driven insights with the power to drive tangible change. Data scientists who utilise this skill have a unique ability to bridge the gap between analytics and action.

The importance of specific data science soft skills in areas such as stakeholder management and assertiveness in this context can not be understated. They are the basis of effective collaboration. These abilities enable data professionals to navigate diverse perspectives, negotiate situations, and confidently convey ideas. By fostering trust and credibility, a position as a reliable team member in the journey of problem-solving is founded. In closing, it’s crucial to recognise that the journey from technical proficiency to impactful data science is incomplete without the knowledge and use of effective data science soft skills, emphasizing the importance of soft skills in shaping your success. These abilities will allow you to navigate complexities beyond algorithms and numbers, enabling you to convey insights effectively, craft narratives that resonate, collaborate seamlessly, and consider the holistic context of your work.


Interested in joining our diverse team? Find out more about the Rockborne graduate programme here.



Frequently asked questions on Data Science Soft Skills!


Is Data Analysis a soft skill or a technical skill?

Data analysis is a technical skill involving processing complex data for informed decision-making. Data Scientists use tools like Python and statistical methods for analysis. While data analysis itself may be a technical skill, your ability to communicate your findings effectively to non-technical stakeholders can indeed be considered a soft skill. In the data industry, this is what is known as data storytelling.

What are the top soft skills for a Data Scientist?

Employers seek specific data science soft skills. Communication for instance is paramount; bridging gaps with stakeholders is crucial for informed decisions. Teamwork and time management, often overlooked in courses, are vital too. Remember this for success in the field.

What is an example of a hard skill for a Data Scientist?

Proficiency in programming languages like Python, R, or SQL is a fundamental hard skill for data scientists. Being proficient in Python for instance allows data scientists to efficiently write code, automate repetitive tasks, and create complex algorithms for data analysis.

What is the most in-demand Data Science Skill?

Machine learning is highly sought after in data science due to its ability to predict trends, automate tasks, personalize user experiences, detect fraud, optimize processes, enable natural language understanding, and provide businesses with a competitive advantage. By harnessing machine learning, companies can make data-driven decisions, enhance efficiency, and innovate, staying ahead in the rapidly evolving digital landscape.



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