SQL in Data Science
Like working with data? Do u like find the trends even before they happen? Or are looking for becoming a more valuable asset in your workplace? If any of the scenarios happen, then you have landed at the right place to find out why.
Do you know that the maximum amount of data of human history has been posted on the internet within recent few years, and there are a lot and a lot of information provided online? Have you wondered why? The reason for this is the evolution of data science, big data and data mining and many other data science aspects, which are helping us to find the answers. It’s a technological world this is becoming very clear going forward that why we are experiencing this, we use more social media than ever, we give more information, by signing up more accounts, do a survey, questionnaires cooperating with a different type of business. There is so much data available with the corporate world, then it was earlier 10-15 years ago. We are really in a very interesting period, where there is so much information and so many aspects of data are evolving and we can gain valuable insights from them.
Structured Query Language is what SQL stands for, it is used to question, update or modifying data. Managing data in databases, generating a table with different variable and to put value in those tables. It is made of small syntax which we have to deal with to get started with learning SQL. Very user-friendly, it’s not that difficult to learn. The sequel is data fetching language, it is important at the initial stage when you get a dataset and start investigating data by visualizing it, start to organize it, identifying missing values, formatting editing, etc. Its allow you to play around with datasets; it’s another very important tool in the data science toolkit so it needs the focus of learning.
Before starting to analyze the data, which is obviously collected for different source, can’t be expected to be clean. SQL helped in data munging or wrangling, which means getting the data in more structure format to start working with it.
These are the main functions that can conduct by SQL-
Data Aggregations – Useful to understanding the data, representing it as summary.
Ranking Functions- To rank values in dataset and doing a top end analysis. For example- Yu can rank the customers who have done the maximum business with you.
Bucketing the Data- Used for better prediction and results in section.
Statistical Functions and
Windowing Functions.
SQL is still pushing its boundaries and there are a lot more aspects of SQL to learn. With endless possibilities and vast range, this language is another most used tools by Data Scientists.
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