This module introduces the foundational concepts of working with relational database tables, including how to define table structures, select appropriate data types, and enforce data integrity through constraints. Learners will gain practical skills in performing CRUD operations and designing robust tables for real-world datasets.

This module introduces efficient methods for importing and exporting data in PostgreSQL using the COPY and \COPY commands. Learners will discover how these commands streamline data transfer between databases and external files, optimizing real-world data management tasks.

This module introduces learners to integrating Python with databases, focusing on executing SQL queries and manipulating data using libraries such as SQLAlchemy and pandas. Participants will gain hands-on experience in setting up Python environments, connecting to PostgreSQL, and transforming data for analysis and visualization.

This module introduces key SQL techniques for retrieving and organizing data, including the use of SELECT statements, filtering with WHERE, and sorting with ORDER BY. Learners will gain practical skills to extract relevant information from databases and refine query results using conditional logic.

This module guides learners through essential techniques for modifying and transforming data within relational databases. You will practice updating and deleting records, applying built-in and user-defined functions, and automating changes with triggers to reflect evolving business needs. By the end, you'll be able to efficiently manage and manipulate data to support real-world scenarios.

This module introduces advanced SQL techniques for combining and transforming data from multiple tables. Learners will explore subqueries, common table expressions (CTEs), various types of joins, and set operations to efficiently define new datasets for analysis. By the end, you'll be able to preprocess and merge data to support complex analytical tasks.

This module introduces SQL aggregation functions and demonstrates how to categorize and summarize data using the GROUP BY clause. Learners will also discover how to filter aggregated results with the HAVING clause to perform more targeted data analysis. Practical exercises use real-world retail data to reinforce these concepts.

This module introduces you to scalar, aggregate, and window functions for advanced data analysis in SQL. You will learn how to use window functions to rank, partition, and analyze data trends over time, including defining custom window frames for deeper insights. Practical exercises will help you apply these concepts to real-world business scenarios.

This module explores techniques for optimizing SQL query performance in PostgreSQL by examining different scanning methods and index types. Learners will discover how and when to use sequential, index, and hash scans to improve data retrieval efficiency. Practical examples illustrate effective index usage and common pitfalls in query planning.

This module introduces techniques for handling complex data structures in SQL, focusing on parsing and manipulating JSON and array data types. Learners will gain practical skills in accessing, modifying, and analyzing non-relational data within a relational database context.

This module introduces techniques for working with dates, text, and geospatial data, enabling you to handle and analyze non-numeric information effectively. You will learn to transform and summarize date fields, process and manipulate text for business insights, and perform basic geospatial calculations to enhance data-driven decision making.

This module introduces key inferential statistics concepts, including point estimates, confidence intervals, correlation, and simple linear regression, all within the context of SQL data analysis. Learners will gain practical skills to make data-driven predictions and assess relationships between variables using SQL queries.

This module guides learners through the end-to-end process of data analytics using SQL, from understanding dimensional modeling to loading and analyzing data in a star schema. Learners will practice implementing SQL workflows, ensuring data quality, and preparing data for business intelligence tools.