SQL Syllabus

Beginner Level

1. Introduction to SQL

• Overview of Databases
  • Types of databases (Relational vs. Non-relational)
  • Introduction to SQL and its importance in data science

2. Setting Up the Environment

• Database Management Systems (DBMS)
  • Installing PostgreSQL/MySQL/SQLite
  • Introduction to database tools (pgAdmin, MySQL Workbench)

3. Basic SQL Syntax

• Data Types and Operators
  • Understanding different data types (int, varchar, date, etc.)
  • Basic operators (arithmetic, comparison, logical)

4. CRUD Operations

• Creating Tables
  • CREATE TABLE syntax
  • Constraints (PRIMARY KEY, FOREIGN KEY, UNIQUE)
• Reading Data
  • SELECT statements
  • Filtering data using WHERE clause
  • Sorting results using ORDER BY
• Updating Data
  • UPDATE statement
• Deleting Data
  • DELETE statement

5. Functions and Aggregations

• Built-in Functions
  • String functions (e.g., CONCAT, SUBSTRING)
  • Date functions (e.g., NOW, DATEDIFF)
• Aggregation Functions
  • COUNT, SUM, AVG, MIN, MAX
  • Grouping data using GROUP BY

6. Introduction to Joins

• Joining Tables
  • Understanding different types of joins (INNER, LEFT, RIGHT, FULL)
  • Practical examples of joining data from multiple tables

Intermediate Level

1. Advanced SQL Queries

• Subqueries
  • Using subqueries in SELECT, FROM, and WHERE clauses
• Common Table Expressions (CTEs)
  • Using CTEs for better query organization

2. Window Functions

• Understanding Window Functions
  • ROW_NUMBER, RANK, DENSE_RANK, NTILE
  • Calculating running totals and moving averages

3. Data Manipulation Techniques

• Data Insertion and Bulk Operations
  • INSERT, INSERT INTO ... SELECT
  • Bulk insert techniques

4. Indexing and Performance

• Introduction to Indexes
  • Understanding indexes and their types
  • Creating and managing indexes for performance optimization

5. Database Normalization

• Normalization Principles
  • First, Second, and Third Normal Forms
  • Denormalization and its applications in data science

6. Error Handling and Transactions

• Understanding Transactions
  • BEGIN, COMMIT, ROLLBACK
  • Implementing error handling using try-catch

Advanced Level

1. Advanced Joins and Set Operations

• Complex Joins
  • Self-joins and cross joins
• Set Operations
  • UNION, INTERSECT, EXCEPT

2. Advanced Window Functions

• Partitioning Data
  • Using PARTITION BY and ORDER BY with window functions

3. SQL for Data Analysis

• Analytical Queries
  • Implementing data analysis techniques using SQL
  • Case studies and practical applications

4. Stored Procedures and Functions

• Creating Stored Procedures
  • Syntax and structure of stored procedures
• User-Defined Functions (UDFs)
  • Creating and using UDFs

5. Performance Tuning and Optimization

• Query Optimization Techniques
  • Analyzing query performance
  • Understanding execution plans

6. Security and Access Control

• Database Security Basics
  • User roles and permissions
  • Best practices for securing SQL databases

7. Integration with Data Science Tools

• Using SQL with Python/R
  • Connecting to databases from Python using libraries (e.g., pandas, SQLAlchemy)
  • Data extraction and manipulation for data science tasks