SQL for Data Science course Syllabus

A practical and essential course for anyone starting a career in data analytics using SQL. This course provides a clear, structured introduction to SQL with a focus on real-world data analysis tasks. You'll learn to write queries, manipulate data, and extract insights from relational databases. The course is beginner-friendly and designed to build foundational skills through hands-on practice. Estimated total time: 10–14 hours.

Module 1: Introduction to SQL and Databases

Estimated time: 2 hours

• Understand relational database structure
• Identify tables, rows, and columns
• Explain primary keys and relationships
• Write basic SELECT statements

Module 2: Filtering and Aggregating Data

Estimated time: 3 hours

• Use WHERE clause for filtering data
• Sort results with ORDER BY
• Group data using GROUP BY and HAVING
• Apply aggregate functions: COUNT, SUM, AVG, MIN, MAX

Module 3: Working with Multiple Tables

Estimated time: 3 hours

• Perform INNER JOIN operations
• Use LEFT JOIN to include unmatched rows
• Combine data from related tables
• Apply subqueries for advanced filtering

Module 4: Practical Data Analysis Applications

Estimated time: 2 hours

• Solve real-world data problems with SQL
• Extract insights for reporting
• Support decision-making with query results

Module 5: Data Cleaning and Transformation

Estimated time: 2 hours

• Handle missing or inconsistent data
• Use SQL functions for data transformation
• Prepare data for analysis and reporting

Module 6: Final Project

Estimated time: 3 hours

• Design and execute a complete analysis workflow
• Write queries to answer business questions
• Submit a cleaned dataset with summary insights

Prerequisites

• Basic computer literacy
• Familiarity with spreadsheets
• No prior programming experience required

What You'll Be Able to Do After

• Write SQL queries to retrieve and analyze data from relational databases
• Use SELECT statements with filtering, sorting, and aggregation
• Apply JOIN operations to combine data from multiple tables
• Understand subqueries and nested queries
• Perform data cleaning and transformation tasks using SQL