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10 Skills of A Successful Data Scientist

It takes more than just knowing how to program to be a good data scientist.

Photo by Lewis Keegan on Unsplash
Photo by Lewis Keegan on Unsplash

Changing Careers or starting a new one is often a confusing trip; that’s because, in the beginning, you often don’t have sufficient knowledge of the field or what is requires to excel at it. This gets more challenging if the field you’re trying to master involves interdisciplinary skills all working together.

A great example of such a field is data science. Data science is fundamentally a computer science field built upon other knowledge bases that are not necessarily a part of computer science. And so, to become a data scientist, you will need to grow your knowledge beyond programming and mathematics.

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This knowledge actually becomes a necessity to succeed in the field and eventually land the job of your dreams. Therefore, we can consider these skills as soft skills needed to become a successful data scientist. This article will go through the 10 soft skills that every data scientist needs in order to have a successful career.

Although all these skills are essential, the degree of their importance varies based on your exact role responsibilities and the nature of your current project.

№1: Having a curious mind

I will start this list with the skill that I believe is the most important if you’re considering a career in Data Science, and that’s curiosity. Data science can be defined as the process of shaping, transforming, and telling the story told by a certain set of data.

As a data scientist, you will need to be always questioning why certain things happen; you will need to wonder why some events affect that data more than others, you will need to ask a lot of questions about your data and its behavior to fully understand it, and be able to utilize it to predict future data and make correct decisions. Intellectual curiosity is the driving force behind all these questions and looks for their respective answers.

№2: Communication Skills

Often, when a data scientist is given a new project, they will need to communicate with a client or manager about the project’s requirement and what the end goal of the project is supposed to be. So, an essential skill to have is to know how to ask the right questions and formulate your problem statement.

The importance of good communication skills goes beyond the first stages of the project; you will need to present and communicate your findings to your client or manager at the end of the project. Your findings will be used to make decisions for future steps, and the way you communicate these findings will be the deciding factor between making the right or wrong future decisions.

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№3: Storytelling

Building on the previous skill is storytelling. I have written my fair share of articles about using visualization to make more efficient storytelling and communicate your finding more accurately in a simpler manner. Data Science is about telling your data’s story. But, the way you tell the story makes a huge difference.

If the data scientist is a good storyteller, they can convey their findings in a coherent and easy-to-understand way, which is an essential aspect because not all people will present their findings to have the same technical knowledge. Thus, working on your storytelling can make you and your work stand out in the data science community.

№4: Adaptability

One of the important skills to develop if you’re in any technical field and data science, in particular, is your ability to adapt to change and new tools. Data science is an ongoing field; researchers develop new tools, algorithms, and methods regularly. So, as a data scientist, you will need to be always ready to use a new tool or a new technique.

Adaptability covers other smaller skills, like the ability to learn a new tool in an adequate amount of time and transfer and apply your knowledge to new or different techniques or styles. Moreover, you need to be able to respond to varying trends in the field.

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№5: Business focus

Most of the time, the findings from any data science project are used to make business decisions. Because of that, data scientist need to have a solid basic understanding of business models, how they work, and the scope of trends the client or management are considering for this particular project.

This skill is probably my least favorite aspect of data science, but it remains an essential knowledge to obtain. Knowing the company’s model, their perspective, and their client base can help you make the correct decisions when analyzing and modeling the data.

№6: Critical thinking

Another skill that’s important regardless of your particular tech field is critical thinking. Critical thinking allows data scientists to think and approach their analysis objectively. It enables them to frame questions correctly and make the right decisions about the algorithms they use and how they will benefit the overall project.

Critical thinking will enable the data scientist to look beyond the obvious trends and anomalies and take a closer look at the data and the information it’s trying to convey. It also reduces and sometimes eliminates bias from the final outputs of the project.

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№7: Structured Thinking

Another type of thinking that is important in data science is structured thinking. When you are first given a project, it may look complex and impossible at first. That’s why data scientists need to know how to take a big problem and divide it into smaller, more manageable parts.

To be able to divide a big problem into smaller sub-problems requires structured thinking. This skill will help you complete your project in a timely and efficient manner. So, spending some time working on this skill will save you much time and effort in the future.

№8: Ethics

One of the biggest concerns you may face when deciding to get into data science is data ethics. As a data scientist, you will have access to some confidential and sometimes sensitive user information. You will need to use this information to build and develop your models.

But, just because data can be collected doesn’t mean you should collect it and subsequently use it. The problem is, there are cases when the decision is not that clear or easy to make. So the ability to explain your choices and why you made them will make it easier for you to prove the value of your work and how the decisions were made based on ethical guidelines.

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№9: A passion for learning

As we discussed earlier, data science is an ongoing field. There are new techniques and methods developed regularly. So, as a data scientist, you need to be always in a learning mode, and having a passion for learning new things will help you greatly in this career path.

Having a passion for learning goes hand to hand with having a curious mind, and together they make a great contribution to helping you become a better data scientist. Another connect skill is learning how to learn efficiently. The amount of knowledge you need to obtain as a data scientist can be overwhelming, so knowing how to apply previous knowledge to learn new ones faster can make a big difference in your career.

№10: Collaboration

The last skill on our list is a collaboration or the ability to be a team player. When you’re a data scientist, you will probably be a part of a team, each working on a specific aspect of the project. You will need to collaborate and sometimes with other teams to get feedback, possible solutions and build a solid project.

You will sometimes also need to build on other team member’s work, so you need to know how to work with others and how to build upon other’s work. This skill also connects to having good and efficient communication skills.

Final thoughts

If you are thinking of becoming a data scientist or just trying to work on yourself, you can create a list of the possible skills you will need to develop to become a good – or better – data scientist. That list will most likely contain two types of skills, technical ones and soft ones.

Perhaps when it comes to a technical field like data science, guessing or stating the technical skills required is not a difficult task at all. Even if you’re not very familiar with the field, you can guess that you need some sort of programming and maths knowledge to get into the field.

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The challenge arises when we discuss soft skills. Because data science is a very interdisciplinary field, the soft skills required may not be straightforward to reach. In this article, we explored 10 such soft skills that can make you a better data scientist and help you achieve the career success you deserve.


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