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SQL for Machine Learning and Data Scientists

  • Difficulty Level : Expert
  • Last Updated : 09 Jul, 2021

In this article, SQL and its applications in Data Analysis and Machine Learning are discussed. We will also discuss SQL’s various applications and its future prospects in Database management.

Overview :
SQL(Structured Query Language) is used to manage relational databases, and It is used to perform fetching rows, database creation, modifying databases, deleting databases, and also reading, writing data. It is extremely useful in managing structured data, i.e. data that is used to represent relations among entities and variables. SQL is predicated on relational algebra and tuple relational calculus. MS Access, MySQL is a standard database language.

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SQL for Data Analysis :
SQL and SQLite have significant importance in data analysis techniques. SQL can be used for different purposes like data analysts can use it for analyzing data and data insights can help in decision-making.  SQL can help end-users to understand more complex data storage systems because of its ability to interact directly with built-in languages used in SQL. Due to this, it helps data scientists and analysts a lot in their work to get access to SQL to understand such a vast variety of data. 

Use of SQL in Machine Learning :
SQL servers have released great features that help to run Python and R language scripts with relational data. SQL servers have kept releasing new features with time like data partitioning which helps to keep all the work in one place and get the advantage of making smaller files and objects for managing them. Data Partitioning helps to increase our efficiency to work with the help of normalized tables while analyzing the data flow and retrieving data by SQL statements.



Technique used by SQL for Data Partitioning to store more data in less space

SQL Applications & Operations :
Applications of SQL include setting and running analytical queries, transaction processing, retrieving subsets of information within a database for analytics applications, writing data integration scripts, and adding, updating, and deleting rows and columns of data in a database. These SQL operations are applicable to a wide variety of operations.

Operators –
 The SQL operators are used within the WHERE clause of the statement. This part of the statement is used to filter the data in appropriate conditions. There are six types of SQL operators are as follows.

  1. Arithmetic – 
    It includes basic add, subtract, multiply and divide operators.
     
  2. Bitwise – 
    It includes the Bit-wise AND, OR & Exclusive OR operators.
     
  3. Comparison – 
    It includes operators that compare equality (equal to, greater than, less than).
     
  4. Compound – 
    It includes operators with signs like +=, -=, *= etc.
     
  5. Logical – 
    It includes operators like AND, ANY, BETWEEN, NOT, OR that create logic for each condition.
     
  6. String – 
    It is used to compare to strings using == signs.

SQL Commands with different functions :
SQL commands are used as instructions to access the data from the database. It is used to perform various functions like creating a table and performing various functions inside it like dropping, modifying its size, setting permission for users. It can also be used for accessing queries of data, specific tasks, and functions. 

SQL Commands with their description

SQL With Other Language Scripts :
SQL offers various features with the combination of other languages like R, Python, and Power-Shell scripts. Python’s huge number of libraries like SciPy & Pandas help express a much more convenient way to perform regression analysis algorithms rather than doing the same function in SQL alone.  Hence, other scripting languages make it even easier to implement data analysis and regression algorithms in SQL.

Conclusion :
SQL is quite a massive technology, and it has quite a lit future as it is continuously developing new features to expand itself in every field. The future aspects of SQL are not limited to Computer Science but also include finance, healthcare, public services, and in short everywhere. At the end of the day, every organization requires a database for managing their customer’s data. So, there are never-ending reasons why we should choose SQL for fast and efficient data analysis.

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