Data Analytics Course with Python, SQL, Excel & Power BI

Master Data Analytics with Python, SQL, Excel & Power BI

Data Analytics Course with Python, SQL, Excel & Power BI is a comprehensive industry-oriented training program designed to help students, fresh graduates, working professionals, business analysts, and aspiring data professionals build strong analytical skills and prepare for successful careers in Data Analytics, Business Intelligence, Reporting, and Data-Driven Decision Making.

This Data Analytics Course with Python, SQL, Excel & Power BI covers everything from basic data concepts to advanced analytics, business intelligence, dashboard development, statistics, AI-powered analytics, and career preparation. The curriculum focuses heavily on practical learning through real-world projects, case studies, and hands-on exercises.

The course is ideal for:

• Students
• Fresh Graduates
• Working Professionals
• Business Analysts
• Excel Users
• Reporting Professionals
• Entrepreneurs
• Managers
• Career Switchers
• Aspiring Data Analysts

Why Learn Data Analytics?

Data Analytics is one of the fastest-growing career domains globally. Organizations rely on data to:

• Improve Business Decisions
• Increase Revenue
• Understand Customers
• Optimize Operations
• Reduce Costs
• Predict Future Trends

Data Analysts are responsible for transforming raw data into meaningful business insights.

Skills You Will Learn

After completing this Data Analytics Course with Python, SQL, Excel & Power BI, you will be able to:

• Analyze Business Data
• Work with SQL Databases
• Perform Data Cleaning
• Create Interactive Dashboards
• Build Power BI Reports
• Use Python for Analytics
• Perform Statistical Analysis
• Generate Business Insights
• Create Data Visualizations
• Apply AI Tools in Analytics
• Prepare for Data Analyst Interviews

Complete Data Analytics Course Curriculum

Section 1: Introduction to Data Analytics

Lesson 1: Introduction to Data Analytics

Lesson 2: Data Analytics vs Data Science

Lesson 3: Types of Data Analytics

Lesson 4: Data Analytics Lifecycle

Lesson 5: Business Applications of Data Analytics

Lesson 6: Data Analyst Roles & Responsibilities

Lesson 7: Data Analytics Tools Overview

Lesson 8: Data Analytics Career Opportunities

Section 2: Excel Fundamentals for Data Analytics

Lesson 9: Introduction to Excel

Lesson 10: Excel Interface

Lesson 11: Data Entry & Formatting

Lesson 12: Excel Formulas & Functions

Lesson 13: Logical Functions

Lesson 14: Lookup Functions

Lesson 15: Data Validation

Lesson 16: Conditional Formatting

Lesson 17: Sorting & Filtering

Lesson 18: Pivot Tables

Lesson 19: Pivot Charts

Lesson 20: Excel Dashboards

Section 3: Advanced Excel for Data Analytics

Lesson 21: Advanced Formulas

Lesson 22: Power Query in Excel

Lesson 23: Data Cleaning in Excel

Lesson 24: Advanced Pivot Tables

Lesson 25: Data Visualization in Excel

Lesson 26: Excel Automation

Lesson 27: Excel Reporting

Lesson 28: Excel Analytics Project

Section 4: SQL for Data Analytics

Lesson 29: Introduction to SQL

Lesson 30: Installing MySQL

Lesson 31: Database Fundamentals

Lesson 32: SQL Data Types

Lesson 33: Creating Databases

Lesson 34: CRUD Operations

Lesson 35: SQL Filtering

Lesson 36: Sorting Data

Lesson 37: Aggregate Functions

Lesson 38: GROUP BY & HAVING

Lesson 39: Joins

Lesson 40: Subqueries

Lesson 41: Views

Lesson 42: Stored Procedures

Lesson 43: Window Functions

Lesson 44: SQL Analytics Project

Section 5: Advanced SQL for Data Analytics

Lesson 45: Advanced Joins

Lesson 46: Common Table Expressions (CTEs)

Lesson 47: Ranking Functions

Lesson 48: Data Warehousing Basics

Lesson 49: SQL Optimization

Lesson 50: Query Performance Tuning

Lesson 51: Business Reporting with SQL

Lesson 52: Advanced SQL Analytics Project

Section 6: Python Fundamentals

Lesson 53: Introduction to Python

Lesson 54: Installing Python

Lesson 55: Python Syntax

Lesson 56: Variables & Data Types

Lesson 57: Operators

Lesson 58: Conditional Statements

Lesson 59: Loops

Lesson 60: Functions

Lesson 61: Lists

Lesson 62: Tuples

Lesson 63: Dictionaries

Lesson 64: Sets

Lesson 65: File Handling

Lesson 66: Exception Handling

Lesson 67: Object-Oriented Programming

Lesson 68: Python Project

Section 7: Advanced Python

Lesson 69: Modules & Packages

Lesson 70: Lambda Functions

Lesson 71: List Comprehensions

Lesson 72: Generators

Lesson 73: Decorators

Lesson 74: APIs in Python

Lesson 75: JSON Handling

Lesson 76: Web Scraping Basics

Lesson 77: Database Connectivity

Lesson 78: Automation with Python

Lesson 79: Data Processing with Python

Lesson 80: Advanced Python Project

Section 8: Python Libraries for Data Analytics

Lesson 81: NumPy Fundamentals

Lesson 82: Pandas Basics

Lesson 83: DataFrames

Lesson 84: Data Cleaning with Pandas

Lesson 85: Data Transformation

Lesson 86: Exploratory Data Analysis

Lesson 87: Matplotlib Visualization

Lesson 88: Seaborn Visualization

Lesson 89: Statistical Analysis

Lesson 90: Python Data Analytics Project

Section 9: Statistics for Data Analytics

Lesson 91: Introduction to Statistics

Lesson 92: Mean Median Mode

Lesson 93: Probability Concepts

Lesson 94: Standard Deviation

Lesson 95: Variance

Lesson 96: Correlation

Lesson 97: Regression Basics

Lesson 98: Business Statistics

Lesson 99: Statistical Analysis Project

Section 10: Power BI Fundamentals

Lesson 100: Introduction to Power BI

Lesson 101: Installing Power BI Desktop

Lesson 102: Connecting Data Sources

Lesson 103: Data Transformation

Lesson 104: Data Modeling

Lesson 105: Relationships

Lesson 106: Power Query

Lesson 107: Basic Visualizations

Lesson 108: Interactive Reports

Lesson 109: Power BI Dashboard Project

Section 11: Advanced Power BI

Lesson 110: DAX Fundamentals

Lesson 111: Calculated Columns

Lesson 112: Measures

Lesson 113: Time Intelligence

Lesson 114: Advanced DAX

Lesson 115: KPI Dashboards

Lesson 116: Business Intelligence Reporting

Lesson 117: Publishing Reports

Lesson 118: Power BI Service

Lesson 119: Advanced Dashboard Project

Section 12: Real-World Data Analytics Projects

Lesson 120: Retail Sales Analytics Project

Lesson 121: Customer Segmentation Project

Lesson 122: HR Analytics Project

Lesson 123: Marketing Analytics Project

Lesson 124: Finance Analytics Project

Lesson 125: Healthcare Analytics Project

Lesson 126: E-Commerce Analytics Project

Lesson 127: Capstone Data Analytics Project

Section 13: AI Tools for Data Analysts

Lesson 128: Introduction to AI in Analytics

Lesson 129: ChatGPT for Data Analysis

Lesson 130: Microsoft Copilot

Lesson 131: AI Powered Reporting

Lesson 132: Prompt Engineering for Analysts

Lesson 133: Automating Analytics Tasks

Lesson 134: AI Analytics Project

Section 14: Career Preparation & Placement Support

Lesson 135: Resume Building for Data Analysts

Lesson 136: LinkedIn Optimization

Lesson 137: Git & GitHub for Data Analysts

Lesson 138: Portfolio Development

Lesson 139: Mock Interviews

Lesson 140: Data Analytics Interview Questions

Lesson 141: Case Study Interviews

Lesson 142: Internship Preparation

Tools Covered

• Microsoft Excel
• Advanced Excel
• SQL
• MySQL
• Python
• NumPy
• Pandas
• Matplotlib
• Seaborn
• Power BI
• Power Query
• DAX
• Git
• GitHub
• ChatGPT
• Microsoft Copilot

Real-World Projects Included

• Excel Analytics Project
• SQL Analytics Project
• Advanced SQL Analytics Project
• Python Project
• Advanced Python Project
• Python Data Analytics Project
• Statistical Analysis Project
• Power BI Dashboard Project
• Advanced Dashboard Project
• Retail Sales Analytics Project
• Customer Segmentation Project
• HR Analytics Project
• Marketing Analytics Project
• Finance Analytics Project
• Healthcare Analytics Project
• E-Commerce Analytics Project
• AI Analytics Project
• Capstone Data Analytics Project

Career Opportunities

After completing this course, learners can apply for:

• Data Analyst
• Business Analyst
• Power BI Developer
• Reporting Analyst
• MIS Executive
• Business Intelligence Analyst
• Data Visualization Specialist
• Operations Analyst
• Marketing Analyst
• Financial Analyst
• Analytics Consultant
• Junior Data Scientist

Learning Outcomes

After completing this course, you will be able to:

• Analyze and interpret business data.
• Create professional dashboards and reports.
• Write SQL queries for data analysis.
• Use Python for data analytics and automation.
• Perform statistical analysis.
• Build Power BI dashboards.
• Apply AI tools in analytics workflows.
• Complete real-world analytics projects.
• Build professional portfolios.
• Prepare for interviews and placements.