Essential SQL Commands for Data Science: A Beginner's Guide

Any aspiring data scientist should, at the very least, be able to understand the foundational tool of SQL and how it can be used to retrieve, manipulate, and analyze data within relational databases. Basic SQL commands can unlock valuable insights from data, represent the first step toward mastering the art of more advanced data analysis techniques.

In this tutorial, we are going to learn the SQL commands most useful for data science. These are the perfect SQL commands for beginners. If you want to go deep into SQL, then a data science course in Hyderabad offers can teach you SQL using real-world data and give you practical experience of it.

1. SELECT: Retrieving Data from a Database

The SELECT statement is the backbone of SQL queries. It allows you to retrieve certain rows of data in a database by choosing specific columns.

Example

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SELECT name, age FROM students;

This statement fetches only the name and age columns for the students table.

Tip Use SELECT * to fetch all columns.

2. WHERE: Filtering Data

The WHERE clause allows you to filter the records based on conditions so you could only receive data meeting certain criteria.

Example

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SELECT * FROM employees WHERE department = 'Sales';

This will fetch all the records from the employees table where the department is "Sales."

Tip: Use WHERE along with operators like =, <>, >, <, BETWEEN and LIKE to get higher filtering precision.

3. ORDER BY: Sorting Data

ORDER BY Orders your SQL result set in ascending (ASC) and descending (DESC) order.

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SELECT name, salary FROM employees ORDER BY salary DESC;

This statement retrieves the names and salaries of employees, listed by salary in descending order.

4. LIMIT: Limit the Results

The LIMIT clause limits the number of rows returned for a query. Maybe that comes handy sometimes when you have to deal with huge tables.

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SELECT * FROM sales LIMIT 10;

This query will only bring the first 10 records from the table of sales.

5. AGGREGATE FUNCTIONS: Adding, Counting, Averaging, etc.

SQL supports a few aggregate functions that are very useful for summarizing data and analyzing it.

SUM(): summing up all values in a column.

COUNT(): returning the number of records.

AVG(): returns the average.

MAX() and MIN(): returns the highest/lowest values.

Conclusion

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SELECT COUNT(*) AS total_sales, AVG(amount) AS average_amount FROM sales;

It makes available to you the aggregate number of sales, which is the average number involved in each sale.

. GROUP BY: Summing Over Groups

GROUP BY requires aggregate functions to gather all your data by one column or more; its applications enable you to derive sums for each group of collected data.

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Tip: Use GROUP BY together with HAVING to filter on groups based on computed values.

7. JOIN: Combining Data from Multiple Tables

JOIN lets you combine rows from two or more tables based on related columns, so that you might analyze across a table.

INNER JOIN: It returns rows where the values in both a table can match.

LEFT JOIN: It returns all the rows of the left table and matched rows from the right table.

RIGHT JOIN : Returns all rows from the right table and matched rows from the left table.

FULL JOIN : Returns rows when there is a match in one of the tables.

Example (INNER JOIN):

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SELECT employees.name, departments.department_name

FROM employees

INNER JOIN departments ON employees.department_id = departments.id;

This statement fetches names of employees and their respective department names based on joining the employees and departments tables.

8. CASE: Conditional Logic in Queries

It has a SQL version of an if-else statement and is called the CASE statement. This lets you add conditional logic to your queries as well.

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SELECT name,

CASE

WHEN score >= 90 THEN 'A'

WHEN score >= 80 THEN 'B'

ELSE 'C'

END

The INSERT INTO statement can add new rows to a table.

Here, we have an example:

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INSERT INTO students (name, age, grade) VALUES ('Alice', 23, 'A');

This query inserts a new record of a student into the students table.

10. UPDATE: Updating Existing Records

The UPDATE statement can update existing records in a table if a condition is met.

Here, we have an example:

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UPDATE employees SET salary = salary * 1.1 WHERE department = 'Sales';

This statement inflates the salaries of all persons in the Sales staff by 10%.

11. DELETE FROM: Deleting a Database

DELETE FROM removes entries from a table. When using DELETE without an applied WHERE, it removes every entry from the database.

Always back up your data before running queries that update or delete records.

The use of comments (-- comment) while inputting SQL queries renders them readable.

Solve daily: The best way to master SQL commands is hands-on experience, using real datasets.

Conclusion

These are core SQL commands that make up the foundation of how you can analyze your data and do data science. Once you have mastered these commands, you would know exactly how to query, filter, join, and manipulate your data in the most effective ways possible; this will lay a strong foundation for more advanced analysis. Whether you are doing exploratory data analysis or preparing your data to bring it ready for machine learning, these SQL commands are worth every penny for the professional data scientist.

If you would want to learn more, then enroll yourself in one of the available Hyderabad data science courses that will provide detailed training on SQL and other crucial skills required for data scientists.