Hello readers, we have another blog on data analysis today. This time, we are talking about SQL. Whether it is data cleaning, its interpretation, manipulation, or analysis, SQL has a big role to play in the world of data science. It is a must to learn a language for all aspirants pursuing the Data Analytics Course in Delhi who want to be analysts.
This Structured Query Language helps in managing the overall workflow of extracting unstructured data and converting it into a well-structured one. This data further gets stored in files and is processed by a DBMS (database management system).
There are a lot more points that define the importance of SQL for Data Analysis. In this blog post, we will walk through it.
So, let’s start straight with a quick introduction to this language.
What is SQL?
SQL was developed in 1970 by one of IBM’s researchers named Raymond Boyce and Donald Chamberlin. It’s a Structured Query Language for developers and data analysis professionals to manage stored data.
In simple words, SQL means Structured Query Language, which talks to databases. It asks questions in the form of queries and also gives instructions for optimizing raw information into well-organized data.
Let’s dive deep into SQL core functions to understand its job better.
Key Functions of SQL
1. SELECT: This function picks up specific data from database tables.
Example: Let’s assume we have information on some school students and want to see the total no of students who got 80 marks out of 100. So, in this case, we can use the SELECT function to get the data of the listed students with marks above 80.
2. JOIN: The next important function is JOIN, which combines data from multiple tables. But there is a condition that there needs to be some common things or similarities.
Example: Let’s take the students’ example again. Now we can use this new function to get students’ names from one table and combine them with students’ marks from another table.
3. GROUP BY: This function arranges available data into different groups. For example, we can group students based on their average marks obtained in each subject.
4. HAVING: We use this HAVING function for more specific filtration of groups made using the GROUP BY function. For example, we can show those subjects in which students got average marks above 60.
5. CREATE: This function, as its name suggests, adds a new table. For example, we can add a new table for inserting students’ attendance data.
6. INSERT, DELETE, UPDATE: These are some more functions that add, remove, and update old stored data. For example, we can use these functions to add a new student, remove any student’s record, or update an existing student’s record.
Additional Essentials in SQL for Data Analysis
Now, we have a good understanding of the core functions of SQL, it’s time to move forward to understand its basic building blocks. These are datatypes, operators, and command categories.
Let’s talk about them one by one.
SQL Datatypes
Data types are like the guides for the database. They tell the database whether we are inserting numbers or text. SQL has three main datatypes named Numeric, Date & time, and string.
- Numeric Datatype is the first one, and it is for adding numbers like salary, age, price, etc.
- Date & Time Datatype is another type for storing dates and times, like birthdate, joining date, etc.
- String Datatype is the last type for storing text and characters like names, addresses, etc.
SQL Operators
Operators are special symbols or words like +, *, =, >, or AND. These special operators help in applying both mathematical and logical operations in the database.
Some key operators are listed below:
- Arithmetic Operators come on top as we use them for doing basic math operations like add (+), subtract (−), multiply (*), and divide (/) in the data.
- Relational Operators are equal (=), greater than (>), and less than (<). They compare values with each other.
- Logical Operators are AND, OR, and NOT. We use them for joining different conditions.
- Assignment Operator is (=) which gives a value to something.
SQL Command Categories
Now, we will go through some useful SQL commands that help in analyzing data properly. For better understanding, we have divided these commands into different categories.
These categories are:
- Data definition language: It is one type which have different commands for defining database structure properly. Create, drop, alter, and truncate come under it.
- Data manipulation language: It has commands like insert, update, and delete.
- Data control language: It has GRANT commands for granting access to data.
- Transaction control language: It comes with commands like rollback, savepoint, and commit database transactions.
- Data query language: It has (select) as its main command.
Importance of SQL in Data Analysis
The output of data analysis relies mainly on well-organized data, and SQL plays a vital role in this process. Let’s understand its importance through the following points:
1. A Structured Query Language
SQL is an essential programming language for data analysts to work with raw data. It gives options to add, update, or remove the data easily. Additionally, we can run queries to find patterns and trends, which is useful in predictions and decision-making.
2. Handling Large Datasets
SQL has (RDBMS) relational database management system and (DBMS) database management system. RDBMS stores data in tables, while DBMS stores data in files.
Both of these database management systems are capable of managing large volumes of data. They help in storing, manipulating, and organizing large datasets easily.
3. Integration with Other Tools
SQL works smoothly with other tools for data analysis and programming languages like Python and the R language. It provides the best workflow and helps analysts to get better insights for decision-making.
4. Support for Complex Queries
SQL supports advanced queries like joins and subqueries to combine data from multiple tables. This helps in simplifying complex information for analysis.
Real-World Application of SQL in Data Analysis
SQL is widely used in real-world scenarios and helps in making better business decisions. Some of its common applications include:
1. Business Intelligence Reporting
Data analysts use SQL databases and connect them with Power BI and Excel for intelligent reporting. This is how things work in industry. SQL acts like a bridge between raw and refined reports. You can learn to work with Power BI and Excel in our special courses in data science and analytics.
2. Customer Segmentation
Businesses are using SQL to turn raw data of customers’ behaviour and preferences into useful insights. In this way, businesses are able to make better marketing strategies.
3. Academic Researcher
Researchers rely on SQL to analyse large datasets in different areas like health, climate, and business. They easily discover patterns and make accurate predictions based on them.
4. Financial Analysis
In the finance sector, data professionals use SQL to study revenue, expenses, profits, and losses. This way, they help businesses in making financial decisions such as budget planning and cost control.
Conclusion on the Importance of SQL in Analyzing Data
So, indeed, SQL plays an important role in the process of analysing data. It does many things, from storing large datasets in tables to organizing them into groups and further sharing the same data with tools like Power BI, Excel, Tableau, etc. In short, learning SQL is no doubt a beneficial step you can take to enhance your skills as a data analyst.
