SQL language deals with database management, while data is a crucial aspect of machine learning, deep learning, as well as data analysis. For small databases, SQL language may be used for doing a data analysis. In this case, the data analysis will provide an opportunity to analyze the possible data trends and patterns in the database. It is writing about methods of identifying the pattern or trend used in the analysis of data. Numerous ways to address the data analysis problem can be solved via SQL but today we will understand a couple of them.

## What is Data Analysis?

As for data analysis, we can analyze the data in detail. We can determine patterns, trends and structures in the data. Data analysis is tremendously important for other models to predict the outcomes. The results are data-dependent, so a close look at those data is required. We can use Python language, R programming or Haskell to do data analysis. Or we could also perform the task with this SQL language. So today we’ll look at some techniques for analyzing the trends and patterns of databases using SQL language.

## Techniques To Perform Data Analysis Using SQL

There are various techniques available in SQL to perform data analysis. The techniques are simple and easy. Let’s see one by one.

### 1. Retrieving Data From the Database for Analysis

The first approach to carrying out data analysis of the small database we can use is the retrieving technique. With this technique, we pull the data out of the tables. We will introduce conditions, retrieve unique characters or sort the data from different columns. Methods of retrieving data are plentiful, such as the SELECT statement, DISTINCT keyword and WHERE clause.

**SELECT Statement**: The SELECT statement will extract the data from the columns. The SELECT statement is a very commonly used technique in SQL language. The simple syntax and short query of the SELECT statement speed up the overall process.

```
SELECT column_1, column_2
FROM table_1;
```

Code language: SQL (Structured Query Language) (sql)

This code will help you retrieve data from multiple columns. You must give table names to columns.

**DISTINCT Keyword**: DISTINCT is a keyword to remove duplications from tables. This is how to clean up the database. The use of the DISTINCT keyword requires a column with table names.

```
SELECT DISTINCT column_1
FROM table_1;
```

Code language: SQL (Structured Query Language) (sql)

**WHERE Clause:**The WHERE clause arranges the values by conditions. In SQL language, this is a simple way to sort the databases. These multiple conditions can be applied to the tables ‘columns.

```
SELECT *
FROM table_1
WHERE column_1 = 'value_1';
```

Code language: SQL (Structured Query Language) (sql)

### 2. Aggregation Techniques for Smaller Databases

The second technique involves the summation and aggregation of different values from the database. These techniques involves some functions like COUNT(), SUM(), AVG(), MIN(), and MAX(). All these functions perform various actions on database values so that we can perform data analysis easily on smaller databases.

**COUNT() Function**: COUNT() is a function that counts the number of rows present in specific tables within your database. This is a simple and easy technique.

```
SELECT COUNT(*)
FROM table_1;
```

Code language: SQL (Structured Query Language) (sql)

**MIN(), MAX(), AVG(), and SUM() Function:**MIN(), MAX(), AVG(), and SUM() function is used perform basic calculations on smaller databases. Basic mathematical calculations are really important to perform data analysis on the database.

```
SELECT SUM(column_1), AVG(column_2), MIN(column_3), MAX(column_4)
FROM table_1;
```

Code language: SQL (Structured Query Language) (sql)

The aggregation technique plays a very important role in data analysis because it gives collective values for graphs, charts, patterns, and trends.

### 3. Sorting Values for Data Analysis

In the domain of data analysis, sorting is essential to perform various techniques on the database. The sorted values help to calculate and predict the results. The calculations are easy and simple if we implement this technique. To sort the values from the tables, we are using the ORDER BY Clause.

**ORDER BY Clause**: The ORDER BY Clause arranges values according to the conditions provided. The ORDER BY Clause takes conditions/values and sorts the entire column. Therefore this technique is easily applicable to different models for data analysis.

```
SELECT *
FROM table_1
ORDER BY column_1 condition_1;
```

Code language: SQL (Structured Query Language) (sql)

### 4. Aggregation and Grouping for Data Analysis

The GROUP BY clause helps to combine the ROWS with the same value. This technique is used with aggregation functions and other functions used for mathematical calculations. THE GROUP BY clause is widely used in different models to plot graphs, charts, trends, and patterns. The data analysis can be done with this type of functions and clauses.

```
SELECT column_1, COUNT(*)
FROM table_1
GROUP BY column_1;
```

Code language: SQL (Structured Query Language) (sql)

In this basic syntax of GROUP BY Clause, we are writing the column name, table name, and aggregation function – COUNT(). The combined use of these functions always impacts the speed and accuracy of the data analysis models.

### 5. Use of Logical Operators for Data Analysis

In some complex numerical/ mathematical operations we need to apply multiple conditions to the database. The conditions are applied using the WHERE clause in the SQL language. To implement the multiple conditions, we are combining logical operators with the WHERE clause. Three logical operators AND, OR, and NOT, are combined with the WHERE clause. Let’s see a basic syntax for this technique.

```
SELECT *
FROM table_1
WHERE column_1 = 'condition_1' AND column_2 > condition_2;
```

Code language: SQL (Structured Query Language) (sql)

In this example, we have implemented the AND logical operator with the WHERE clause. Similarly, you can implement the OR and NOT operator with the WHERE clause.

### 6. Use of Subqueries for Data Analysis

Subqueries are like SQL loops. The subqueries are easy to use. Different operations that use the subqueries include filtration, sorting and extraction of data from a table. The subqueries are quite simple and easy to solve. By implementing subqueries, we can use many columns of information at the same time.

```
SELECT *
FROM your_table
WHERE column_name IN (SELECT column_name FROM another_table WHERE condition);
```

Code language: SQL (Structured Query Language) (sql)

### 7. Use of JOINs for Data Analysis

There are many kinds of JOINs in the SQL language, and they fulfil an important function when analyzing data. But JOINs are easy to use, combining data from different columns of several tables. With this method, data is analyzed according to various columns. This data analysis can be done using small datasets, with various components.

```
SELECT *
FROM table_1
INNER JOIN table_2 ON table_1.column_1 = table_2.column_1;
```

Code language: SQL (Structured Query Language) (sql)

### 8. Handling Data Modifications for Data Analysis Using SQL

Updating or deleting data from the table with SQL language is simple. A number of functions are known to do this. The given syntax illustration uses SET, WHERE and UPDATE clauses to amend the data values from a table. The second thing we can do is use DELETE to remove the data point from the table.

```
UPDATE your_table
SET column_name = 'new_value'
WHERE condition;
```

Code language: SQL (Structured Query Language) (sql)

```
DELETE FROM your_table
WHERE condition;
```

Code language: SQL (Structured Query Language) (sql)

### 9. Functions for Data Analysis

SQL language provides a number of functions for mathematical operations, ranging from calculating average to standard deviation and variance. The data analysis calls for calculations from the table values. As a result, these functions save time and produce correct answers. These functions are so simple that we only need to provide columns on which the function must act. As for syntax, let’s take an example.

```
SELECT AVG(column_1), STDEV(column_2), VARIANCE(column_3)
FROM table_1;
```

Code language: SQL (Structured Query Language) (sql)

### 10. LIMIT Clause for Data Analysis

It is also important in data analysis that the representation of data. The SQL language also has functions to filter the data. SQL has a LIMIT clause. The LIMIT clause helps restrict the number of columns.

```
SELECT *
FROM table_1
LIMIT 10;
```

Code language: SQL (Structured Query Language) (sql)

These are some techniques that are used to perform data analysis on small datasets using SQL language. These techniques are easy to understand. You can try these examples with your datasets and perform data analysis.

## Summary

In this article, we explain 10 techniques that can be used to perform data analysis on small datasets, along with syntax examples. These techniques involve grouping of data, some clauses/functions to perform mathematical operations on datasets, and in-built methods. All of these techniques aid in analyzing data to solve intricate problems. The SQL language simplifies complex operations by implementing various techniques.

## Reference

Do read the SQL language manual for more details and techniques.