What Makes Someone a Data Scientist? Data scientists are not monolithic and more often than not, the work they do and the tools they use vary wildly. However, across the different fields of work, some common traits differentiate the more successful data scientists from others. Here are some of their common characteristics:
1. Successful data scientists adopt clean coding practices
As a data scientist in the industry who is worth their salt, you will certainly have to get your hands dirty with coding to a certain extent. While the code you wrote frenziedly might do the job in a pinch, your future self, as well as others who might come to rely on your code down the line, will thank you for taking the bit of extra time to write orderly code that is easily understandable.
There are a few ways to code cleanly and responsibly. At the very least, successful data scientists use meaningful names for variables and functions, practice using the single responsibility principle and document their thought processes regularly.
2. Successful data scientists keep up with new developments
Data science is a field that is rapidly evolving. Every few months, popular tools become outdated and new exciting findings claim the spotlight. In order to stay relevant and increase the pool of resources they have at their disposal to tackle different business problems, data scientists make concerted and regular efforts to keep up to date with new developments in the industry by reading relevant blogs, research papers and books.
3. Successful data scientists are open to change
As mentioned, the data science field is fast-paced and ever-changing. In order to keep up, it is important that data scientists are versatile and adaptable in the face of constant change. To increase versatility, successful data scientists usually learn multiple programming languages such as Python, R and SQL and gain experience in different data analytics soft wares such as Tableau, Alteryx and PowerBI. Having these tools at their disposal makes them more equipped to gain insights for a larger variety of business problems. Through broadening their knowledge in this manner, they have also primed themselves into developing learning skills that will help them more easily pick up new tools as they gain popularity, allowing them to be more flexible as the data science field evolves.
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