📍 Module 1: Introduction to Data Analytics

• What is Data Analytics?

• Types of Data Analytics: Descriptive, Diagnostic, Predictive, Prescriptive

• Data Analyst vs Data Scientist vs Business Analyst

• Tools of a Data Analyst

• Industry Use Cases: Finance, Healthcare, E-Commerce, Marketing

📍 Module 2: Excel for Data Analysis

• Data Cleaning & Formatting

• Conditional Formatting & Lookup Functions

• Pivot Tables & Pivot Charts

• Excel Formulas for Analysis (SUMIFS, COUNTIFS, INDEX-MATCH)

• Creating Dashboards in Excel

📍 Module 3: Statistics & Data Interpretation

• Descriptive Statistics: Mean, Median, Mode, Range, Variance, SD

• Data Distribution & Normalization

• Probability Basics

• Hypothesis Testing & Confidence Intervals

• Correlation vs. Causation

📍 Module 4: SQL for Data Analysis

• Introduction to Databases & RDBMS

• SQL Basics: SELECT, WHERE, GROUP BY, ORDER BY

• JOINS: INNER, LEFT, RIGHT, FULL

• Subqueries & Nested Queries

• Window Functions (RANK, DENSE_RANK, ROW_NUMBER)

• Case Studies: Sales & Customer Analysis

📍 Module 5: Python for Data Analytics

• Python Basics: Variables, Loops, Functions

• Data Handling with Pandas & NumPy

• Data Cleaning, Merging & Grouping

• Data Visualization using Matplotlib & Seaborn

• Exploratory Data Analysis (EDA)

📍 Module 6: Data Visualization with Power BI

• Introduction to Power BI Interface

• Importing Data from Excel/SQL

• Data Modelling and Transformations

• DAX Formulas Basics

• Creating Interactive Reports & Dashboards

• Publishing & Sharing Reports (Power BI Service)

📍 Module 7: Business Intelligence & Decision Making

• KPI Identification

• Building Insightful Dashboards

• Storytelling with Data

• Making Data-Driven Recommendations

• Business Case Studies & Scenarios

📍 Module 8: Capstone Projects (Choose any 1–2)

• Sales Analytics Dashboard

• Customer Retention Analysis

• Financial Statement Analysis

• HR Attrition Dashboard

• Marketing Campaign Performance Tracker