With the rise of DataScience in today's world at Organisations, it is becoming a great question to choose a language so that maximum efficiency can be attained with potential great insights into the data, and when talking about Data how can anyone forget about SQL pronounced as "ess-cue-el" or "sequel". SQL is a Structured Query Language, domain-specific, used primarily for managing and organizing data in RDBMS (Relational Database Management System ). It is to be noted that with requirements the requirement for language changed based on features scalability and many more to be understood at first before using any language for a particular task. Below are few points to be taken while you choose SQL for DataScience.
The reasons to choose SQL for DataScience are,
- Ability to integrate with Scripting Language
- Ability to manage the huge volume of data
- Concise Commands
- Dataset Explainability
- Easy to learn and Understand
Ability to integrate with Scripting Language
As a data scientist you have to make sure that the data is understood properly and thanks to the feature that involves an ability to integrate with other languages be it Python or R . Thus making sure that whenever you are dealing with huge data SQL gives a hand to integrate with your preferred language so that your task can be informative and well understandable for others. The ability to integrate with Scripting language makes a huge contribution in having a good experience.
Ability to manage the huge volume of data
When we talk about SQL how can we forget about the empowerment to handle huge data by SQL? The ability to manage the huge volume of data makes a prior choice in choosing the option as DataScience practice. It contributes and removes the issue about the data size, thus enabling a better grip over data and better manageability. Thus making SQL one of the choices when we are talking about data handling.
Concise Commands
The Concise Commands are a blessing for Data Science Enthusiast, it does not only helps to understand data manipulation step by step but as a starter, it helps to understand the reason to use the command. The concise commands not only helps user but also helps to understand data more detailed and in an optimized way so that every detailed insight is not neglected. Concise Commands not only save time but also helps to get a better experience when performing operations.
Dataset Explainability
With the power of Concise commands, the future that comes automatically is the dataset explainability, it helps users to understand the dataset in the most detailed way possible so that the proper model, algorithms can be applied. Which on emphasizing can lead to proper management of complexity of the code and also code productivity.
Easy to learn and Understand
It is one of the very important reasons and points that the language should be easy to learn and understand so that even a starter with less time can understand the operations or even start to learn and do the requirement as soon as possible. The possibility to learn and understand makes SQL one of the top prior language that a beginner looks upon when starting his or her project, and this benefit makes SQL one of the choices to choose from.
Conclusion
The choosing of a language depends upon the outcome be is looking for the result as output and future scalability of the model builder. It is to be taken into consideration that the above point is not limited, one may find various others which may add to the requirement and choosing a language over the other. Thus choosing a language is very much important for a better outcome and optimized result as output.