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SQL Notes for Data Analytics

This repository contains my notes on SQL for data analytics purposes. These notes serve as a quick reference guide and cover essential concepts and statements used in SQL for data analysis tasks.

Table of Contents

  1. Introduction to SQL for Data Analytics
  2. Big 6 Statements
  3. Operators in SQL
  4. Order of Execution in SQL
  5. SQL Functions

Introduction to SQL for Data Analytics

SQL (Structured Query Language) is a powerful language used for querying and manipulating relational databases. In the context of data analytics, SQL plays a crucial role in extracting insights from data. This section provides an overview of SQL's applications in data analysis tasks, covering the basics of SQL syntax, common use cases, and the importance of SQL in data analytics.

Big 6 Statements

The "Big 6" statements in SQL form the foundation for data retrieval, filtering, aggregation, and sorting. Understanding these statements is essential for effective data analysis. The Big 6 statements are:

  • SELECT: Retrieves specific columns or expressions from one or more tables.
  • FROM: Specifies the table or tables from which data is retrieved.
  • WHERE: Filters data based on specified conditions.
  • GROUP BY: Groups rows based on one or more columns for aggregate calculations.
  • HAVING: Filters grouped data based on specified conditions.
  • ORDER BY: Sorts the result set based on one or more columns.

Operators in SQL

SQL provides various operators for performing operations on data. These operators allow you to manipulate, compare, and combine values in your queries. Some commonly used operators in SQL include:

  • Arithmetic operators: +, -, *, /
  • Comparison operators: =, <, >, <=, >=, <>
  • Logical operators: AND, OR, NOT
  • String operators: CONCAT, LIKE
  • NULL-related operators: IS NULL, IS NOT NULL
  • Set operators: UNION, INTERSECT, EXCEPT

Order of Execution in SQL

When executing a SQL query, the order of execution follows a specific sequence. Understanding this order helps in designing and optimizing queries. The typical order of execution in SQL is as follows:

  1. FROM: Specifies the tables involved in the query.
  2. WHERE: Filters the rows based on specified conditions.
  3. GROUP BY: Groups the rows according to the specified columns.
  4. HAVING: Filters the grouped data based on specified conditions.
  5. SELECT: Retrieves the specified columns or expressions.
  6. DISTINCT: Removes duplicate rows from the result set (after SELECT).
  7. ORDER BY: Sorts the result set based on specified columns.

Please note that this is a general sequence, and the database optimizer may optimize the execution order in some cases. Additionally, the DISTINCT keyword, if used, is typically applied after the SELECT statement to remove duplicate rows from the result set.

SQL Functions

SQL functions are built-in operations that allow you to perform calculations, transformations, and manipulations on data within your queries. They provide a wide range of functionality to enhance your data analysis tasks. Here are some commonly used SQL functions:

Aggregate Functions

Aggregate functions operate on a set of values and return a single value as the result. They are commonly used for calculating summary statistics. Examples include:

  • SUM: Calculates the sum of values in a column.
  • COUNT: Counts the number of rows or non-null values in a column.
  • AVG: Calculates the average of values in a column.
  • MIN: Returns the minimum value in a column.
  • MAX: Returns the maximum value in a column.

String Functions

String functions allow you to manipulate and analyze string data. They are used for tasks such as concatenation, substring extraction, case conversion, and length calculation. Examples include:

  • CONCAT: Concatenates two or more strings together.
  • SUBSTRING: Extracts a portion of a string based on specified start and length.
  • UPPER: Converts a string to uppercase.
  • LOWER: Converts a string to lowercase.
  • LENGTH: Returns the length of a string.

Date and Time Functions

Date and time functions enable operations on date and time data. They are useful for extracting specific parts of a date or time, performing date arithmetic, formatting dates, and retrieving the current date and time. Examples include:

  • DATEPART: Extracts a specific part (year, month, day, etc.) from a date or time value.
  • DATEADD: Adds a specific time interval to a date or time value.
  • FORMAT: Formats a date or time value into a specific string format.
  • DATEDIFF: Calculates the difference between two dates or time values.
  • GETDATE: Retrieves the current date and time.

Mathematical Functions

Mathematical functions perform calculations on numeric data. They are used for tasks such as absolute value calculation, rounding, power operations, and square root calculation. Examples include:

  • ABS: Returns the absolute value of a numeric value.
  • ROUND: Rounds a numeric value to a specified number of decimal places.
  • POWER: Raises a numeric value to a specified power.
  • SQRT: Calculates the square root of a numeric value.

Conditional Functions

Conditional functions evaluate conditions and return values based on the result. They are used for tasks such as conditional logic and handling null values. Examples include:

  • CASE: Evaluates conditions and returns a value based on the result.
  • COALESCE: Returns the first non-null value from a list of arguments.

Conversion Functions

Conversion functions are used for converting data types from one format to another. They allow you to perform conversions between different data types, as well as formatting specific data representations. Examples include:

  • CAST: Converts a value from one data type to another.
  • CONVERT: Converts a value from one data type to another (database-specific).
  • TO_CHAR: Converts a value to a character string (database-specific).
  • TO_DATE: Converts a string to a date value (database-specific).
  • TO_NUMBER: Converts a string to a numeric value (database-specific).

Statistical Functions

Statistical functions are used for statistical analysis of data. They allow you to calculate metrics such as standard deviation and variance. Examples include:

  • STDDEV: Calculates the standard deviation of a set of values.
  • VARIANCE: Calculates the variance of a set of values.

Set Functions

Set functions are used for combining or comparing result sets. They include operations

Feel free to explore the different sections of these SQL notes and expand your understanding of SQL for data analytics.

Happy learning and analyzing!