📍 Module 1: Introduction to Data Analytics

• What is Data Analytics?

• Data Analyst vs Data Scientist vs BI Analyst

• Data Lifecycle & Real-World Use Cases

• Skills & Tools Required for Data Analysts

• Career Opportunities in Analytics

📍 Module 2: Excel for Data Analysis

• Excel Essentials for Analysts

• Formulas, Pivot Tables, and Charts

• Data Cleaning Techniques

• Lookup Functions: VLOOKUP, XLOOKUP

• Dynamic Dashboards with Form Controls

• Excel Productivity Tips & Shortcuts

📍 Module 3: SQL for Data Analytics

• Introduction to Databases & SQL

• SELECT, WHERE, GROUP BY, ORDER BY

• Joins, Subqueries & Set Operators

• Aggregate Functions & Data Filtering

• Window Functions and CTEs

• Exporting SQL Data for Reporting

📍 Module 4: Power BI for Visualization

• Power BI Desktop Introduction

• Importing Data from Excel/SQL

• Data Transformation in Power Query

• Data Modeling & Relationships

• DAX for Calculations

• Building Interactive Dashboards

• Sharing Reports on Power BI Service

📍 Module 5: Python for Data Analysis (Optional Advanced Module)

• Python Basics: Variables, Data Types, Loops

• Using Pandas for DataFrames

• Data Cleaning & Manipulation with Pandas

• Data Visualization with Matplotlib & Seaborn

• Introduction to Jupyter Notebooks

📍 Module 6: Data Cleaning & Preprocessing

• Handling Null, Duplicate, and Outlier Data

• Standardization & Normalization

• Data Types & Formats

• Best Practices in Data Preparation

• Real-World Data Cleaning Exercises

📍 Module 7: Exploratory Data Analysis (EDA)

• Understanding Data Distributions

• Descriptive Statistics

• Trend Analysis

• Correlation & Causation

• EDA using Excel, SQL & Python

📍 Module 8: Real-Time Projects

• Sales Performance Dashboard

• HR Attrition Report

• Marketing Campaign Analysis

• Customer Retention Analysis

• SQL + Power BI + Excel Integration Projects