Module 1: Advanced Excel for Data Analytics

• Excel Interface & Data Management
• Advanced Functions (VLOOKUP, XLOOKUP, INDEX-MATCH)
• Logical, Text & Date Functions
• Conditional Formatting
• Data Validation & Cleaning
• Pivot Tables & Pivot Charts
• What-If Analysis
• Excel Dashboards & Reporting

Module 2: SQL for Data Analysis

• Introduction to Databases & SQL
• Database Concepts & Schema
• SQL Data Types
• SELECT, WHERE, ORDER BY
• Aggregate Functions (SUM, AVG, COUNT, MIN, MAX)
• GROUP BY & HAVING
• Joins (INNER, LEFT, RIGHT, FULL)
• Subqueries & Nested Queries
• Views & Indexes
• Real-World Data Querying Practice

Module 3: Power BI for Business Intelligence

• Power BI Overview & BI Concepts
• Data Import (Excel, SQL Database, CSV)
• Power Query for Data Transformation
• Data Modeling & Relationships
• DAX Fundamentals
• Interactive Dashboards & Reports
• Filters, Slicers & Visual Interactions
• Publishing & Sharing Reports

Module 4: Python Fundamentals for Data Applications

• Python Basics & Syntax
• Variables, Data Types & Operators
• Conditional Statements & Loops
• Functions & Modules
• File Handling
• Jupyter Notebook
• Python Libraries Overview

Module 5: NumPy for Numerical & Scientific Computing

• NumPy Arrays & Operations
• Mathematical & Statistical Functions
• Indexing, Slicing & Broadcasting
• Multi-Dimensional Arrays

Module 6: Pandas for Data Analysis & Manipulation

• Pandas Series & DataFrames
• Data Import & Export
• Data Cleaning & Missing Values
• Filtering, Sorting & Grouping
• Merging & Joining Datasets
• Time Series Analysis

Module 7: Data Visualization using Matplotlib & Seaborn

• Data Visualization Principles
• Line, Bar, Pie & Scatter Charts
• Histograms & Distribution Plots
• Heatmaps & Correlation Analysis
• Insight-Driven Visual Reports

Module 8: Data Processing & Feature Engineering

• Data Preprocessing Techniques
• Handling Missing & Duplicate Data
• Encoding Categorical Variables
• Feature Scaling & Normalization
• Feature Selection Methods

Module 9: Machine Learning Fundamentals & Concepts

• Machine Learning Overview
• Supervised & Unsupervised Learning
• Model Training & Testing
• Evaluation Metrics
• Overfitting & Underfitting

Module 10: Machine Learning Implementation with Python

• Scikit-Learn Introduction
• Regression Models
• Classification Algorithms
• Clustering Techniques
• Model Optimization & Tuning

Module 11: Model Deployment & Real-World Applications

• Saving & Loading ML Models
• End-to-End Project Workflow
• Industry Use Cases
• Capstone Project

Key Projects

• Excel Sales & MIS Dashboard
• SQL Database Analysis Project
• Power BI Interactive Dashboard
• Python Data Analysis Project
• Machine Learning Prediction Model
• Final Industry Capstone Project

Career Opportunities

• Data Analyst
• Business Analyst
• SQL / MIS Executive
• Power BI Developer
• Junior Data Scientist
• Machine Learning Associate