SQL

Course Outline

Module 1: Introduction to SQL and Relational Databases

• Overview of relational database concepts
• Understanding tables, rows, and columns in the context of LMS databases
• Introduction to SQL and its applications in data analytics
• Basic SQL commands (SELECT, WHERE, ORDER BY)
• Hands-on Practice: Writing basic SQL queries to retrieve LMS data

Module 2: Filtering and Sorting LMS Data

• Using WHERE clause for data filtering (basic operators, LIKE, IN, BETWEEN)
• Sorting data with ORDER BY
• Working with NULL values in LMS data
• Hands-on Practice: Filtering and sorting data on student engagement and grades

Module 3: Aggregating and Grouping Data for Insights

• Using aggregate functions (COUNT, SUM, AVG, MIN, MAX)
• Grouping data with GROUP BY to gain insights into student behaviors and performance
• HAVING clause for filtering grouped data
• Hands-on Practice: Grouping LMS data to analyze completion rates, attendance, and engagement

Module 4: Joining Tables in LMS Databases

• Understanding relationships between tables (e.g., students, courses, instructors)
• Types of joins (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN)
• Joining multiple tables to gather comprehensive insights
• Hands-on Practice: Querying across tables to analyze student progress, grades, and course activity

Module 5: Advanced SQL Functions and Data Cleaning

• String functions (e.g., CONCAT, LENGTH, SUBSTRING) for text data processing
• Date and time functions to analyze trends over time
• Data cleaning techniques (removing duplicates, standardizing values)
• Hands-on Practice: Cleaning and preparing LMS data for further analysis

Module 6: Subqueries and Nested Queries

• Introduction to subqueries and their uses in complex data retrieval
• Correlated subqueries to find specific insights (e.g., top-performing students)
• Using subqueries in WHERE and FROM clauses
• Hands-on Practice: Writing nested queries for advanced LMS data analysis

Module 7: Using SQL for Reporting and Visualization Preparation

• Creating and saving reusable queries for reporting
• Using SQL views to organize and simplify data access
• Preparing data in SQL for visualization tools (e.g., exporting to CSV)
• Final Project: Generating a report on LMS metrics and insights using SQL