Essential SQL Commands for Data Science
Tags: programming, database, sql
Published: 2023-03-13 18:36:37

**INTRODUCTION**

Structured Query Language, commonly known as SQL, is a language used to communicate with relational database management systems (RDBMS). It is the standard language used for managing, retrieving, and manipulating data stored in RDBMS. SQL was first developed in the 1970s, and since then, it has undergone several advancements, making it one of the most widely used database languages in the world.

**History of SQL**
SQL was first developed in the 1970s by IBM researchers Donald D. Chamberlin and Raymond F. Boyce. The purpose of creating SQL was to provide a more efficient way of querying databases than the existing methods, such as COBOL and FORTRAN. SQL was first implemented in the IBM System R project, and it was later adopted by other database management systems such as Oracle, MySQL, Microsoft SQL Server, and PostgreSQL.

Over the years, SQL has undergone several advancements, with new features being added to enhance its functionality.

Structured Query Language, or SQL, is a programming language that is commonly used for managing and manipulating data in relational databases. SQL is an essential tool for data professionals, including data scientists, database administrators, and business analysts. 

In this article, we will explore SQL and its various applications,the different types of SQL commands,querying databases, data manipulation, data analysis, how to create and manipulate databases. 


**Section 1: Querying Databases**

Querying databases is an essential skill for anyone working with data. The ability to retrieve data from a database based on specific criteria is crucial for data analysis, reporting, and decision-making. Structured Query Language, or SQL, is the language used to query databases. SQL is a standardized language used by many relational database management systems (RDBMS), including Oracle, Microsoft SQL Server, and MySQL.

The SELECT statement is the most fundamental SQL command for querying databases. The SELECT statement retrieves data from a database based on specific criteria. The statement begins with the SELECT keyword, followed by a list of columns to be retrieved from the database. The FROM keyword is used to specify the table(s) from which to retrieve data. The WHERE clause is used to filter the data based on specific conditions. Here is an example of a SELECT statement:

SELECT column1, column2, column3 FROM table_name WHERE column1 = 'value';

In this example, the statement retrieves data from columns 1, 2, and 3 in the table named table_name where the value in column 1 equals the string 'value'. The asterisk (*) can also be used in place of the column list to retrieve all columns from the specified table.

In addition to the SELECT statement, SQL provides many other commands to query databases. The GROUP BY clause is used to group data based on one or more columns. This is useful for calculating aggregate functions like COUNT, AVG, MAX, and MIN. The HAVING clause is used to filter data based on aggregate functions. Here is an example of a GROUP BY statement:

SELECT column1, COUNT(column2) FROM table_name GROUP BY column1;

In this example, the statement groups the data in table_name by column 1 and counts the number of occurrences of each unique value in column 2 for each group.

The JOIN keyword is used to combine data from multiple tables. JOINs are used when data is stored in separate tables that are related to each other. The most common type of JOIN is the INNER JOIN, which retrieves only the rows where there is a match in both tables. Here is an example of an INNER JOIN statement:

SELECT column1, column2, column3 FROM table1 INNER JOIN table2 ON table1.column1 = table2.column1;

In this example, the statement joins the data from tables 1 and 2 based on the value in column 1, retrieving data from columns 1, 2, and 3 from both tables.

SQL also provides other commands for querying databases, including subqueries, UNIONs, and EXCEPTs. Subqueries are queries within queries and are used to retrieve data from one table based on the results of another query. UNIONs are used to combine the results of two or more SELECT statements into a single result set. EXCEPTs are used to retrieve data from one table that is not in another table.

In addition to the basic SQL commands, there are many techniques and best practices for querying databases. One important technique is to use indexes to speed up queries. Indexes are data structures that allow the database to quickly find specific rows based on the values in specific columns. Another technique is to optimize the database schema to reduce the number of JOINs required to retrieve data. This can be achieved by denormalizing the database, which involves duplicating data in multiple tables to reduce the need for JOINs.

In conclusion, querying databases is an essential skill for anyone working with data. SQL is the language used to query databases, and it provides many commands and techniques for retrieving data based on specific criteria. By mastering SQL, data professionals can effectively analyze data, create reports, and make data-driven decisions.

**Section 2: Data analysis**

SQL is a powerful tool for data analysis. It allows data professionals to extract and manipulate large amounts of data quickly and efficiently. SQL is commonly used in data analysis because it can handle complex queries that are not easily achievable using traditional spreadsheets or other data analysis tools. In this section, we will discuss some SQL commands and techniques that are commonly used in data analysis.

SELECT Statement
The SELECT statement is the most basic and essential SQL command for data analysis. The SELECT statement allows you to retrieve specific data from a database by specifying the columns you want to retrieve and the table you want to retrieve it from. The syntax for the SELECT statement is:

SELECT column1, column2, ... FROM table_name;

WHERE Clause
The WHERE clause is used in conjunction with the SELECT statement to filter data based on specific criteria. The WHERE clause allows you to select only the rows that meet certain conditions. The syntax for the WHERE clause is:

SELECT column1, column2, ... FROM table_name WHERE condition;

GROUP BY Clause
The GROUP BY clause allows you to group data based on one or more columns. It is used in conjunction with aggregate functions such as COUNT, AVG, MIN, and MAX to summarize data. The syntax for the GROUP BY clause is:

SELECT column1, COUNT(column2) FROM table_name GROUP BY column1;

HAVING Clause
The HAVING clause is used in conjunction with the GROUP BY clause to filter data based on aggregate functions. The HAVING clause allows you to select only the groups that meet certain conditions. The syntax for the HAVING clause is:

SELECT column1, COUNT(column2) FROM table_name GROUP BY column1 HAVING condition;

ORDER BY Clause
The ORDER BY clause allows you to sort the results of a query in ascending or descending order. The syntax for the ORDER BY clause is:

SELECT column1, column2, ... FROM table_name ORDER BY column1 ASC/DESC;

JOIN Clause
The JOIN clause is used to combine data from multiple tables. JOINs are used when data is stored in separate tables that are related to each other. The most common type of JOIN is the INNER JOIN, which retrieves only the rows where there is a match in both tables. The syntax for the INNER JOIN is:

SELECT column1, column2, ... FROM table1 INNER JOIN table2 ON table1.column1 = table2.column1;

Subqueries
A subquery is a query within a query. Subqueries are used to retrieve data from one table based on the results of another query. The syntax for a subquery is:

SELECT column1 FROM table1 WHERE column2 IN (SELECT column2 FROM table2 WHERE condition);

Window Functions
Window functions are used to perform calculations across rows of data. Window functions are useful when you need to calculate moving averages, running totals, or other calculations that involve multiple rows. The syntax for window functions is:

SELECT column1, AVG(column2) OVER (PARTITION BY column3 ORDER BY column4 ROWS BETWEEN 1 PRECEDING AND 1 FOLLOWING) FROM table_name;

Conclusion
SQL is an essential tool for data analysis. The SELECT statement, WHERE clause, GROUP BY clause, HAVING clause, ORDER BY clause, JOIN clause, subqueries, and window functions are some of the most commonly used SQL commands and techniques for data analysis. By mastering SQL, data professionals can effectively analyze large amounts of data and make data-driven decisions.

**Section 3:How to create and manipulate databases**

Creating and manipulating databases is an essential skill for data professionals. Databases are used to store, organize, and retrieve large amounts of data efficiently. In this article, we will discuss the steps involved in creating and manipulating databases using SQL.

Creating a Database
The first step in creating a database is to determine the type of database management system (DBMS) you want to use. The most popular DBMSs are MySQL, Oracle, Microsoft SQL Server, and PostgreSQL. Once you have chosen a DBMS, you can create a new database using SQL.

To create a new database in MySQL, for example, you would use the following command:

CREATE DATABASE database_name;

To create a new database in Oracle, you would use the following command:

CREATE DATABASE database_name
DATAFILE '/u01/app/oracle/oradata/orcl/pdbseed/system01.dbf'
SIZE 500M AUTOEXTEND ON;

To create a new database in Microsoft SQL Server, you would use the following command:

CREATE DATABASE database_name;

To create a new database in PostgreSQL, you would use the following command:

CREATE DATABASE database_name;

Manipulating a Database
Once you have created a database, you can manipulate it using SQL commands. The most common SQL commands for manipulating databases are:

Creating Tables
To create a table in a database, you would use the following command:
CREATE TABLE table_name (
column1 datatype,
column2 datatype,
column3 datatype,
...
);

For example, to create a table called customers with columns for name, email, and phone number, you would use the following command:

CREATE TABLE customers (
name VARCHAR(50),
email VARCHAR(50),
phone VARCHAR(15)
);

Inserting Data
To insert data into a table, you would use the following command:
INSERT INTO table_name (column1, column2, column3, ...) VALUES (value1, value2, value3, ...);

For example, to insert a new customer into the customers table, you would use the following command:

INSERT INTO customers (name, email, phone) VALUES ('John Smith', 'john.smith@example.com', '555-555-5555');

Updating Data
To update data in a table, you would use the following command:
UPDATE table_name SET column1 = value1, column2 = value2, ... WHERE condition;

For example, to update the phone number for a customer with the name John Smith, you would use the following command:

UPDATE customers SET phone = '555-123-4567' WHERE name = 'John Smith';

Deleting Data
To delete data from a table, you would use the following command:
DELETE FROM table_name WHERE condition;

For example, to delete all customers with an email ending in '@example.com', you would use the following command:

DELETE FROM customers WHERE email LIKE '%@example.com';

Conclusion
Creating and manipulating databases using SQL is an essential skill for data professionals. To create a database, you need to choose a DBMS and use SQL commands to create a new database. To manipulate a database, you need to use SQL commands to create tables, insert data, update data, and delete data. By mastering SQL, data professionals can effectively manage large amounts of data and make data-driven decisions.

**Section 4: Data manipulation**

Data manipulation is the process of transforming and modifying data to make it more useful and relevant for analysis. It involves a range of techniques and operations that are used to clean, aggregate, merge, and transform data. Data manipulation is a critical step in data analysis, as it ensures that data is accurate and consistent and can be effectively used for modeling, reporting, and decision-making.

We will discuss some common data manipulation techniques and operations using SQL.

Cleaning Data
Data cleaning involves removing or correcting errors, inconsistencies, and missing values in the data. This is important because inaccurate or incomplete data can lead to incorrect analysis and decision-making. Common data cleaning techniques include removing duplicates, correcting misspellings, and imputing missing values.
To remove duplicates from a table, you can use the DISTINCT keyword in the SELECT statement. For example:

SELECT DISTINCT column1, column2 FROM table_name;

To correct misspellings, you can use the REPLACE function. For example:

UPDATE table_name SET column1 = REPLACE(column1, 'old_value', 'new_value');

To impute missing values, you can use the COALESCE function. For example:

SELECT column1, COALESCE(column2, 0) AS column2 FROM table_name;

Aggregating Data
Data aggregation involves combining and summarizing data to provide insights into trends and patterns. Common aggregation operations include counting, summing, averaging, and grouping data.
To count the number of rows in a table, you can use the COUNT function. For example:

SELECT COUNT(*) FROM table_name;

To sum the values in a column, you can use the SUM function. For example:

SELECT SUM(column1) FROM table_name;

To group data by a specific column, you can use the GROUP BY clause. For example:

SELECT column1, COUNT(*) FROM table_name GROUP BY column1;

Merging Data
Data merging involves combining data from multiple sources to create a single dataset. This is often necessary when working with data from different departments or systems within an organization. Common merging operations include joining, merging, and appending data.
To join two tables based on a common column, you can use the JOIN clause. For example:

SELECT column1, column2, column3 FROM table1 JOIN table2 ON table1.column1 = table2.column1;

To merge two datasets based on a common column, you can use the MERGE statement. For example:

MERGE INTO table1 USING table2 ON table1.column1 = table2.column1 WHEN MATCHED THEN UPDATE SET table1.column2 = table2.column2 WHEN NOT MATCHED THEN INSERT (column1, column2) VALUES (table2.column1, table2.column2);

To append data to an existing table, you can use the INSERT statement. For example:

INSERT INTO table_name (column1, column2, column3) SELECT column1, column2, column3 FROM other_table_name;

Transforming Data
Data transformation involves modifying the structure or format of data to make it more useful for analysis. Common transformation operations include splitting, combining, and pivoting data.
To split a column into multiple columns, you can use the SUBSTRING function. For example:

SELECT SUBSTRING(column1, 1, 4) AS column2, SUBSTRING(column1, 5, 2) AS column3 FROM table_name;

To combine multiple columns into a single column, you can use the CONCAT function. For example:

SELECT CONCAT(column1, ' - ', column2) AS column3 FROM table_name;

To pivot data, you can use the PIVOT clause. For example:

SELECT column1, [value1], [value2], [value3] FROM table_name PIVOT (

**Section 5:SQL Commands**

SQL (Structured Query Language) is a powerful tool for managing and manipulating large sets of data in relational databases. Here are some essential SQL commands that every database developer or analyst should know:

SELECT: This is the most basic SQL command and is used to select data from a table. For example, if you want to select all columns from a table called "customers", you would use the following command:
SELECT * FROM customers;

WHERE: This command is used to filter data based on a condition. For example, if you only want to select customers from a specific city, you would use the following command:
SELECT * FROM customers WHERE city = 'New York';

INSERT INTO: This command is used to insert new data into a table. For example, if you want to add a new customer to the "customers" table, you would use the following command:
INSERT INTO customers (name, city, age) VALUES ('John Doe', 'Chicago', 30);

UPDATE: This command is used to update existing data in a table. For example, if you want to update the age of a customer with a specific ID, you would use the following command:
UPDATE customers SET age = 31 WHERE id = 1;

DELETE: This command is used to delete data from a table. For example, if you want to delete a customer with a specific ID, you would use the following command:
DELETE FROM customers WHERE id = 1;

CREATE TABLE: This command is used to create a new table in a database. For example, if you want to create a new table called "orders", you would use the following command:
CREATE TABLE orders (id INT PRIMARY KEY, customer_id INT, product_name VARCHAR(50), price DECIMAL(10,2));

ALTER TABLE: This command is used to modify an existing table. For example, if you want to add a new column called "order_date" to the "orders" table, you would use the following command:
ALTER TABLE orders ADD COLUMN order_date DATE;

DROP TABLE: This command is used to delete a table from a database. For example, if you want to delete the "orders" table, you would use the following command:
DROP TABLE orders;

Conclusion
These are just a few of the essential SQL commands for managing and manipulating relational databases. There are many more commands available in SQL that can help you perform more complex operations and analysis on your data.