What is SQL applied for in data analytics?

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SQL (Structured Query Language) is one of the essential tools for data analytics. It's applied to interact with relational databases — which are popular locations where data gets stored — and supports analysts to retrieve, manipulate, and analyze data.
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What SQL Is Applied for in Data Analytics:
1. Accessing Data
Example: "Display all 3-month sales."

2. Filtering and Sorting
You can filter rows, sort data, or find certain values using SQL.
Example: "Display New York customers who spent over $500."

3. Aggregating Data

4. Joining Tables
SQL enables you to merge information from different tables using JOINs.
Example: "Merge customer data with their orders and payment records."

5. Data Cleaning and Transformation
Alter data with SQL functions (e.g., date formatting, text modifications).
Example: "Convert all customer names to title case."

6. Creating Reports
Utilize SQL to get data ready for dashboards or reporting tools such as Tableau, Power BI, or Excel.
Common SQL Commands in Analytics:
SELECT: pull data
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WHERE: filter rows
GROUP BY: aggregate data
JOIN: join tables
ORDER BY: sort data
LIMIT: limit number of results

CASE: construct conditional logic (such as IF statements)
 Why SQL is Important in Analytics:
It's the go-to language for operating most databases.
It enables quick, accurate data retrieval from big data sets.

Summary
SQL is utilized in data analytics to query, clean, reshape, and analyze the stored data within databases.
It's a requirement for any professional handling structured data and a valuable skill for converting raw data into insights.
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