Introduction to SQL for Data Analysis
Tags: datascience, sql, data, analytics
Published: 2023-02-19 09:51:58

Structured Query Language (SQL) is a programming language used for managing and manipulating relational databases. It is one of the most popular languages used for data analysis and is used by data analysts, data scientists, and database administrators alike. SQL can be used to store, access, and extract massive amounts of data in order to carry out the data science process smoothly.

This article aims to provide an introduction to SQL for data analysis. We will cover the basics of SQL, including its syntax, data types, and operators. 
We will also discuss how to create, modify, and delete tables in SQL, and how to retrieve data from these tables using queries.
Finally, we will provide an overview of some of the more advanced SQL features that can be used for more complex data analysis tasks.

### SQL Basics

SQL is a declarative language, meaning that users define what they want the database to do, and the database management system (DBMS) determines the best way to perform the operation. 
SQL statements consist of one or more clauses, each of which specifies a particular action to be taken. The most common clauses are SELECT, INSERT, UPDATE, DELETE and CREATE. We will look at them in detail later in this article.

SQL syntax is relatively simple, with commands composed of keywords and parameters that are often enclosed in parentheses or quotes. It is not case-sensitive, but it is common practice to write SQL keywords in uppercase.

### SQL Data Types

SQL supports several data types, including;
 - Numeric (integers, decimals, and floats)
 - Character (strings of text)
 - Date/time
 - Boolean types (values of true or false)

### SQL Operators

Operators are used to performing operations on SQL data. There are several types of operators, including; 

- Arithmetic - Addition, subtraction, multiplication, and division
- Comparison - Equal to, not equal to, greater than, and less than
- Logical - AND, OR, and NOT 
- String operators - Concatenation and pattern matching.

### Creating and Modifying Tables

One of the primary functions of SQL is to create, modify, and delete tables. Tables are used to organize data into rows and columns, making it easier to analyze and manipulate the data.
We use the following command; 
```
CREATE TABLE table_name(
column1 datatype,
column2 datatype,
.....
columnN datatype,);
```
So to create an employees Table with various columns we would use the following statement

```
CREATE TABLE employees (
  employee_id INT PRIMARY KEY,
  first_name VARCHAR(50),
  last_name VARCHAR(50),
  salary DECIMAL(10,2),
  hire_date DATE
);

```
This statement creates a table with five columns, each with a different data type. The "employee_id" column is specified as the primary key, which means that it uniquely identifies each row in the table.

Once a table has been created, it can be modified using the `ALTER TABLE` statement. 

```
ALTER TABLE table_name {ADD|DROP|MODIFY} column_name {data_ype};
```

This statement allows users to add or remove columns, change the data type of a column, or modify the constraints on the table.

### Retrieving Data from Tables

Users must use the `SELECT` statement to retrieve data from a table. 

```
SELECT column1, column2....columnN
FROM   table_name;
```
The `SELECT` statement specifies the columns to be retrieved and the table from which to retrieve the data. 
For example, to retrieve all of the data from the "employees" table, we would use the following SQL statement:

```
SELECT * FROM employees;
```

Users can also specify specific columns to retrieve and use `WHERE` clauses to filter the data. For example,

```
SELECT first_name, last_name
FROM employees
WHERE salary > 50000;
```
This statement retrieves only the "first_name" and "last_name" columns for employees whose "salary" is greater than $50,000.

### Advanced SQL Features

SQL also includes several advanced features that can be used for more complex data analysis tasks, we have discussed this at length in the second part of this article, [essential-sql-commands-for-data-science](https://dev.to/ckawara/essential-sql-commands-for-data-science-ph). These features include:

- Joins: They are used to combine data from multiple tables based on a common column or key. There are several types of joins, including _inner joins_, _outer joins_, and _self-joins_.
- Subqueries: Subqueries are used to nest one query inside another query. They can be used to retrieve data that meets specific criteria or to create temporary tables for use in other queries.
- Aggregation functions: Aggregation functions are used to perform calculations on a set of data, such as finding the average, maximum, or minimum value in a column. Common aggregation functions include SUM, AVG, MAX, MIN, and COUNT.
- Window functions: Window functions are used to perform calculations on a subset of data within a more extensive data set. They can be used to calculate running totals, rankings, and other complex calculations.


### Conclusion

SQL is a powerful tool for managing and manipulating large amounts of data. Its simple syntax and flexible data types make it an ideal language for data analysis tasks.
Mastering SQL as an analysis tool is essential for anyone interested in data analysis. To master SQL it is necessary to know the basics of SQL first. Now There are many resources available for learning SQL, and hopefully the explanation above can help you get started.

Till next time, happy coding!✌️