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Available courses

Introduction to DBMS concepts, data models, and database design principles.
Understanding of relational data models, entity-relationship modeling, and SQL.
Study of data structures, file organization, indexing, and query processing.
Learning transaction management, ACID properties, concurrency control, and recovery.
Application of data normalization and database design to ensure data integrity.
Hands-on practice with SQL queries and managing database systems.
Overview of advanced topics like multi-tier architectures and web-based databases

Introduction to Python: What Python is, its uses, and why it's popular for readability and ease of learning.
Basics: Installing Python, running Python code, and understanding syntax.
Core Concepts: Variables, data types, expressions, conditionals, loops, and functions including parameters, return values, and scopes.
Advanced Concepts: Object-oriented programming (OOP), modules and packages, error handling, and decorators.
Practical Applications: Automating repetitive tasks, data processing (CSV, JSON, Excel), web scraping, API interaction, and UI testing.
Projects: Building real-world projects like command-line utilities, websites with Django, and simple machine learning applications.

Introduction to Tableau: Understanding the interface, installing Tableau, and connecting to various data sources.
Fundamentals of Visualization: Creating basic charts, maps, and tables; understanding design principles and best practices in visual analytics.
Advanced Visualization Techniques: Creating calculated fields, using table calculations, and designing dual-layer maps.
Dashboard and Storytelling: Combining visualizations into dashboards, building KPIs, and crafting compelling data stories.
Practical Projects: Hands-on data exploration, creating dashboards, and presenting narratives based on data analysis.