Does a Data Scientist Need to Know SQL? … or is something else better for your data needs?
You could put 10 people in a room, and most likely get 5 Data Scientists to say that they need SQL to work, and the other half will reply that there are other methods for wrangling their data. So, what do you use as a Data Scientist? Can you use something else? Is it a Data Scientist’s responsibility? Below, I will be discussing the role of SQL in Data Science, as well as the alternatives to the structured query language.
You may need to know SQL ultimately depending on your company and Data Science team. Some teams have several Data Engineers and Data Analysts, along with Machine Learning Engineers, while some teams can be composed of just one Data Scientist. You most likely know the answer to this question that is if you need to know SQL as a Data Scientist. However, it is interesting and important to discuss why you do need it, why you do not need it, and when you do not need it. Also, it can be helpful to discs this question for incoming Data Scientists to become familiar with expectations as they enter the workforce. Here is why I use SQL:
Although Data Science can be seen as this career where you focus solely on Python and R, as well as complex Machine Learning algorithms, it may be difficult to perform your Data Science process as a team without utilizing the benefits of SQL in some way. However, there is still a time when you do not need it completely depending on your specific role in Data Science.
The time when you can refer to alternative methods is if you have some help from a Data Engineer or Data Analyst for example, as well as other people in similar positions. Additionally, you may find yourself not requiring SQL when the role of SQL querying is simply not a part of your specialized role because you are focusing more on Data Science model development — like testing various Machine Learning algorithms against one another on data you already received.
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