View Resources

Course Overview

This course is designed to provide comprehensive training in SQL for data science. Students will learn essential SQL concepts and techniques necessary for data manipulation, analysis, and integration with data science projects. Each topic is accompanied by tutorials and practical exercises to reinforce learning and prepare students for real-world applications.

Course Structure

Introduction to SQL

• Overview of relational databases
• Understanding SQL and its relevance to data science
• Tutorial: Setting up SQL environment, basic SQL queries

Querying Data

• SELECT statement and its variations
• Filtering data with WHERE clause
• Sorting data using ORDER BY clauseLimiting results with LIMIT clause
• Tutorial: Writing SELECT queries, filtering and sorting data

Data Manipulation

• CRUD Operations
• Modifying data in tables
• Maintaining data integrity with transactions
• Tutorial: Performing data manipulation operations

Aggregation Functions

• Understanding GROUP BY clause
• Using aggregate functions (SUM, AVG, COUNT, MIN, MAX)
• Filtering grouped data with HAVING clause
• Tutorial: Aggregating data using GROUP BY and aggregate functions

Joins and Subqueries

• Performing inner joins and understanding different join types
• Using subqueries and nested queries for complex data retrieval
• Common Table Expressions (CTEs) for better query organization
• Tutorial: Writing join queries, working with subqueries

Data Analysis with Window Functions

• Introduction to window functions
• Partitioning data with PARTITION BY clause
• Ordering data with ORDER BY clauseAnalytic functions (ROW_NUMBER, RANK, DENSE_RANK, etc.)
• Tutorial: Applying window functions for data analysis

Advanced SQL Techniques

• Working with multiple tables and complex data structures
• Using CASE statements for conditional logic
• Creating and managing views for data abstraction
• Tutorial: Handling complex queries, creating views

Optimizing SQL Performance

• Understanding indexing strategies for performance optimization
• Techniques for query optimization and tuning
• Analyzing execution plans for better performance
• Tutorial: Optimizing SQL queries for performance

Practical Applications in Data Science

• Integrating SQL with Python/R for data analysis
• SQL in data preprocessing, cleaning, and transformation
• Feature engineering using SQLBuilding predictive models with SQL
• Tutorial: Implementing SQL in data science projects

Target Audience

The course “Professional Course: SQL for Data Science” is intended for:

• Data science professionals
• Database administrators
• Students and aspiring data professionals
• Business analysts and data analysts
• Anyone interested in learning SQL for data science