Data Analysis With MYSQL

Course Objectives

Work with the principles of data engineering and relational database management.

Design and implement efficient data models using MySQL.

Develop proficiency in writing basic to advanced SQL queries for data manipulation.

Optimize database performance through indexing and query optimization.

Gain expertise in managing transactions and ensuring data integrity in MySQL.

Administer MySQL databases, including user management, backups, and security.

Utilize MySQL for handling semi-structured data and integrating cloud solutions

Apply data engineering best practices through hands-on projects and case studies.

Modules

Introduction to Data Engineering and Relational Databases

This module provides an overview of data engineering roles, responsibilities, and the principles behind relational databases. It introduces MySQL, covering installation, setup, and the architecture of relational database management systems (RDBMS). Basic database concepts like tables, columns, rows, and data types are also discussed to establish a solid foundation.

Database Design and Normalization

Focuses on the principles of effective database design, including entity-relationship (ER) modeling and normalization to ensure data integrity and minimize redundancy. It introduces techniques for creating well-structured databases and discusses denormalization strategies for optimizing performance. Real-world schema design examples are included to illustrate best practices.

SQL Basics for Data Engineering

Covers fundamental SQL commands for data retrieval and manipulation, such as SELECT, INSERT, UPDATE, and DELETE. Students learn how to filter, sort, and aggregate data using clauses like WHERE, ORDER BY, and GROUP BY. This module aims to build essential SQL skills for querying databases efficiently.

Advanced SQL Techniques:

Introduces more complex SQL functionalities, including multiple types of JOINs, subqueries, window functions, and Common Table Expressions (CTEs). The module delves into recursive queries and advanced data retrieval techniques to handle complex datasets, stored procedures, triggers enabling students to perform sophisticated data analysis tasks..

Indexing and Query Optimization

Explores how to improve query performance using indexing, execution plans, and query optimization techniques. Students learn to create and manage different types of indexes, analyze query performance, and apply strategies to optimize slow-running queries, making database operations more efficient.

MySQL in the Cloud and Scalability

Focuses on deploying MySQL databases in cloud environments such as AWS, Google Cloud, and Azure. Topics include scaling databases using sharding and replication, setting up high availability, and best practices for cloud-based data management. Backup and disaster recovery strategies specific to cloud environments are also discussed.

Course Format

Hands-On Labs: Weekly practical exercises to reinforce learning Assignments: Weekly assignments based on lecture and lab content