Data Science has gained a tremendous popularity in recent years. The ever-increasing ability to collect, transfer, store, and process data is a significant factor in the prevalence of data science.
More and more businesses are able to create value out of data. They apply data science techniques or data-oriented strategies to improve their processes. Data-based business decisions are also proven to be highly efficient and accurate.
As a result of what we have mentioned in the first two paragraphs, a great number of people make a career change to become a data scientist. Similarly, college students focus on data science education more than ever.
However, it is a challenging path to become a data scientist. It takes time, effort, and dedication. You need to learn many tools and obtain different skills.
There are numerous resources, including some of my posts on Medium, that explain what you need to learn to become a data scientist. However, most of them focus on hard skills such as Programming languages, software tools and packages, or theoretical knowledge in certain areas.
It took me about 2 years to land my first job as a data scientist. It was a difficult journey because I had to keep my job and study at the same time. However, I’m so glad that I changed my career path.
After I started working as a data scientist, I have realized that there are some soft skills that a data scientist should possess to become successful. Your Python and SQL skills or statistical knowledge are, of course, very important. However, the soft skills I will mention are just as important.
Communication
A data scientist is highly likely to work in a team. You will not be expected to finish a project from start to end just by yourself. Furthermore, you will need to collaborate with not only data scientists but also people from other professions.
A data science project or product has many aspects. There is the client side that will have a set of demands. You need to have a clear understanding of the demands. Otherwise, the end product might be useless.
Data science can be applied to any domain where we can collect data. Although some operations are standard, each domain has its own dynamics and you need to take it into consideration while designing your product.
Domain knowledge is a crucial input for a data product. It is not possible for a data scientist to have domain knowledge in every field. Thus, you might have to communicate with people who actually work in that domain.
If you work on a small project, you might be responsible for ETL operations or some other data engineering tasks. However, as the project size gets bigger, these tasks are likely to be handled by data engineers or other data scientists.
What I want to emphasize here is that a successful and efficient data product is created as a result of teamwork. What makes a team efficient is clear communication.
What I mean by communication is not just talking. You need to be able to tell what exactly you need, what the steps in the project are, and what bottlenecks or challenges you might encounter.
When communication between the contributors is not efficient or clear, it is highly likely that your project will fail or not meet the deadline.
Presentation
It is one thing to create the data product. However, it cannot sell itself. You need to be able to present what it does, what it achieves, how you can measure its performance, and what the value it creates.
Otherwise, you will have a hard time convincing the clients or other departments in your company.
Presentation is a fundamental skill to have for a data scientist. Your clients may not have the analytical mindset you have. Even your teammates might not understand your point unless you make yourself clear.
Your presentation skills will help you overcome these challenges. I’m not talking about just preparing powerpoint presentations. The important thing is what you put in the presentation and how you present it.
How you present has a great impact.
You may need advanced software tools for presentation and reporting such as Tableau or Power BI. They provide you with highly capable features. Again, how you make use of them is what makes the difference.
Problem solving or analytical thinking
Problem solving and analytical thinking can be considered as two separate skills. But, they strengthen each other. Furthermore, one is hard to achieve without the other.
As a data scientist, you will not always be given explicit instructions to solve a task. Instead, you will be given a problem. How you approach the problem and design the solution will be up to you.
To take it one step further, you may even need to figure out what the problem is. In some cases, you will have a process that needs to be improved. It may not necessarily contain a problem. You will just be expected to make it better.
All these tasks require problem solving and analytical thinking skills.
How you can obtain these skills is beyond my level of expertise. Working as a data scientist will, of course, help you improve your problem solving and analytical thinking skills.
It is important to emphasize that data scientists also perform lots of standard tasks. They follow a routine. For instance, cleaning the raw data or doing exploratory data analysis will not be so difficult after you gain some experience. You need the hard skills for these operations so we do not focus on them in this article.
Conclusion
I think data science is a great profession and I’m glad that I changed my career path to become a data scientist.
It was a long journey that required both time and dedication. I spent a substantial amount of my time trying to obtain hard skills. After I started working as a data scientist, I realized that soft skills are just as important as the hard ones.
Thank you for reading. Please let me know if you have any feedback.