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Structured Query Language (SQL)

Course Description

4.8 (28084)

Learners

32817

What’s included in this Course

 2 months duration hands-on practice

 Live project training

 Interview Preparations

 Daily Assignment

 Online & Class Room Training

 500+ Question for Exercise

Overview:

SQL (Structured Query Language)

SQL (Structured Query Language) is a powerful tool used in data analysis for querying and manipulating data stored in relational databases. It enables data analysts to: Access and extract data: SQL allows analysts to retrieve data from different tables within a database, making it accessible for analysis.

SQL for data analysis is an essential skill for data professionals. It underpins the processes that drive insights and inform business strategies in the digital age. Its application across various industries underscores SQL’s versatility and power in extracting meaningful information from data. Despite its limitations, speed, precision, and scalability advantages make SQL an invaluable tool in the data analyst’s arsenal.

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