Python for Data Analytics & AI – Become Master in Live Program

Accounts Expert

Foundation &amp; Setup: Understand data, databases, and SQL concepts &mdash; covering relational vs. non-relational databases, SQL vs. Excel vs. Python, career paths, and environment setup.
<ul>
	<li>Basic Queries: Retrieve and sort data from tables using core query commands to control what and how much data is returned.</li>
	<li>Filtering Data: Narrow down records using conditions, multiple filters, value ranges, and pattern matching techniques.</li>
	<li>Data Definition Language (DDL): Create and manage database structures &mdash; building, modifying, and removing tables as needed.</li>
	<li>Constraints: Enforce data accuracy and integrity to ensure consistency across all database tables.</li>
	<li>Data Manipulation Language (DML): Add, modify, and remove records within database tables.</li>
	<li>Database Relationships: Design connected tables using One-to-One, One-to-Many, and Many-to-Many relationship structures.</li>
	<li>Normalization: Eliminate data redundancy by restructuring tables through three levels of normal forms.</li>
	<li>Joins: Combine data from multiple tables using various join types to produce unified, meaningful results.</li>
	<li>Set Operators: Merge and compare results from multiple queries into single, consolidated outputs.</li>
	<li>SQL Functions: Transform and manipulate data using String, Numeric, Date/Time, NULL-handling, and Conversion functions.</li>
	<li>Conditional Logic: Apply decision-making within queries for customer segmentation and sales performance categorization.</li>
	<li>Aggregate Functions: Summarize and group large datasets to produce meaningful business metrics and totals.</li>
	<li>Window Functions: Perform advanced row-level analytics &mdash; ranking, comparisons, and running totals &mdash; without losing row detail.</li>
	<li>Advanced SQL: Build complex, multi-step query logic using Subqueries, CTEs, Views, and Temporary Tables.</li>
	<li>Transactions: Maintain data safety and integrity during multi-step database operations with commit and rollback control.</li>
	<li>Stored Procedures &amp; Triggers: Automate and reuse SQL logic through parameterized procedures and event-driven triggers.</li>
	<li>Query Optimization: Improve performance using indexes, execution plans, partitioning, and performance tuning best practices.</li>
	<li>SQL with AI: Use AI tools to write, analyze, and optimize SQL queries more efficiently and productively.</li>
	<li>Final Project + Bonus: Real-world e-commerce data analysis covering sales, customers, and products &mdash; plus 50 interview queries, Excel-SQL comparison, and Power BI integration. 
	 
	&nbsp;</li>
</ul>

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Python for Data Analytics & AI – Become Master in Live Program

SQL Mastery Program: From Beginner to Database Architect

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