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SQL(Structure Query Language):

Structured Query Language (SQL) is a powerful and widely used programming language designed for managing and manipulating relational databases. It provides a standardized way to interact with databases, allowing users to store, retrieve, update, and delete data efficiently. SQL is essential for data-driven applications, as it enables seamless communication between databases and software applications. It is used extensively in web development, data analytics, business intelligence, and enterprise software systems.

SQL consists of various commands categorized into different types, such as Data Definition Language (DDL), Data Manipulation Language (DML), and Data Control Language (DCL). DDL commands, such as CREATE, ALTER, and DROP, help in defining and modifying database structures. DML commands, including SELECT, INSERT, UPDATE, and DELETE, are used to manipulate data within tables. DCL commands, such as GRANT and REVOKE, are used to manage user permissions and access control. These commands make SQL a versatile and powerful tool for database management.

Course

4.8 (8084)

Learners

10817

MNC's Expert Trainer

Exp. 15+Yrs.

Upskill with

Internship

What’s included in this Course

2 months duration hands-on practice

Live project training

Interview Preparations

150+ Assignments

Online & Offline Training

500+ Questions for Exercise

Schedule Your Free Trial Class

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The Value of SQL Certification for Companies

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SQL certification ensures that professionals have a strong grasp of database management principles, optimizing their ability to handle large volumes of structured data. Companies that invest in SQL-certified employees gain a competitive edge, as these professionals possess the technical expertise required to maintain, query, and manipulate data efficiently.

One of the biggest advantages of hiring SQL-certified professionals is improved database performance and security. Certified employees are trained in best practices for database optimization, indexing, and query performance tuning. They also understand how to implement security measures, such as access controls and data encryption, ensuring that sensitive company information is protected from unauthorized access or breaches. This level of expertise reduces downtime and improves overall system efficiency.

Detailed Course Content

  • What is SQL?
  • Role of SQL in Data Analytics
  • Types of Databases (Relational vs. Non-Relational)
  • Understanding RDBMS (MySQL, PostgreSQL, SQL Server, etc.)
  • SQL Installation & Setup (MySQL/PostgreSQL)
  • Basics of Tables, Records, and Fields
    Understanding SELECT Statement
  • Filtering Data with WHERE Clause
  • Sorting Data with ORDER BY
  • Using DISTINCT to Remove Duplicates
  • Using LIMIT and OFFSET for Data Sampling
    Arithmetic Functions (SUM, AVERAGE, MIN, MAX)
  • Handling NULL Values
  • Applying Logical Operators (AND, OR, NOT)
  • Pattern Matching with LIKE and Wildcards
  • Aggregate Functions (COUNT, SUM, AVG, MIN, MAX)
  • String Functions (UPPER, LOWER, CONCAT, TRIM, SUBSTRING)
  • Date & Time Functions (NOW, DATEADD, DATEDIFF, EXTRACT)
  • Mathematical Functions (ROUND, CEIL, FLOOR)
  • GROUP BY Clause for Summarization
  • Using HAVING vs. WHERE for Filtering Groups
  • Advanced Aggregation with ROLLUP & CUBE
  • Practical Data Analysis with Aggregations
  • Understanding Database Relationships
  • Types of Joins:
    • 🔹INNER JOIN
    • 🔹LEFT JOIN
    • 🔹RIGHT JOIN
    • 🔹FULL OUTER JOIN
    • 🔹CROSS JOIN
      • 🔹 Using UNION, UNION ALL, INTERSECT & EXCEPT
  • Using Subqueries for Nested Queries
  • Correlated vs. Non-Correlated Subqueries
  • Introduction to CTEs (WITH Clause)
  • Recursive CTEs for Hierarchical Data
  • Introduction to Window Functions
  • RANK, DENSE_RANK, ROW_NUMBER
  • LEAD & LAG for Trend Analysis
  • PARTITION BY for Segmented Analysis
  • Moving Averages & Cumulative Sums
  • Identifying & Handling Missing Data
  • Removing Duplicates
  • Data Type Conversion (CAST, CONVERT)
  • Data Normalization & Standardization
  • Understanding Indexes & Their Impact on Performance
  • Clustered vs. Non-Clustered Indexes
  • Optimizing Queries with EXPLAIN & ANALYZE
  • Using Views & Materialized Views
  • Creating Analytical Reports with SQL
  • Data Extraction for Dashboards (Power BI, Tableau)
  • Using SQL with Python & Pandas for Analysis
  • Performing Exploratory Data Analysis (EDA)
  • Solving a Real-World Business Problem
  • Writing Optimized SQL Queries for Data Insights
  • Presenting SQL-Based Data Reports
  • End-to-End Data Analytics Project
  • Interview Questions