1.1 Overview of Data Analytics

   - Definition and scope of data analytics

   - Importance and applications in various industries

   - Evolution and current trends in data analytics

1.2 Basics of Data Analytics Tools

   - Introduction to Excel for data analysis

   - Overview of SQL for database querying

   - Introduction to Power BI for data visualization

1.3 Role of Data Analyst

   - Responsibilities and key skills required

   - Career paths and opportunities in data analytics

   - Real-world examples of successful data analytics projects

3.1 Understanding EDA

   - Purpose and objectives of exploratory data analysis

   - Techniques for summarizing and visualizing data in Excel

   - Importance of EDA in hypothesis generation and feature selection

3.2 EDA Techniques with Excel and Power BI

   - Descriptive statistics: mean, median, mode, variance, standard deviation in Excel

   - Data visualization: creating charts, pivot tables in Excel and Power BI

   - Correlation analysis: exploring relationships between variables in Power BI

3.3 Case Study: EDA on Real-World Dataset

   - Performing EDA using Excel and Power BI

   - Drawing insights and identifying patterns in the data

4.1 Introduction to SQL

   - Structured Query Language fundamentals

   - Understanding relational databases and tables

   - Basic SQL commands: SELECT, WHERE, GROUP BY, ORDER BY

4.2 Advanced SQL Queries

   - Joins: INNER JOIN, LEFT JOIN, RIGHT JOIN

   - Subqueries and nested queries

   - Aggregation functions: COUNT, SUM, AVG, MAX, MIN

4.3 Hands-on Project: Data Analysis with SQL

   - Writing SQL queries to extract, transform, and analyze data

   - Performing data analysis tasks such as segmentation, filtering, and aggregation

5.1 Advanced Excel Functions for Data Analysis

   - VLOOKUP, HLOOKUP, INDEX-MATCH

   - Pivot tables and Pivot charts for interactive analysis

   - Conditional formatting and data validation

5.2 Statistical Analysis with Excel

   - Regression analysis: linear and multiple regression

   - Forecasting techniques: moving averages, exponential smoothing

   - What-if analysis: scenario manager, goal seek, data tables

5.3 Hands-on Project: Advanced Data Analysis in Excel

   - Applying advanced Excel functions and tools for data analysis

   - Building interactive dashboards and reports

6.1 Introduction to Power BI

   - Overview of Power BI Desktop and Power BI Service

   - Connecting to data sources: Excel, SQL Server, CSV files

   - Building data models and relationships

6.2 Creating Visualizations in Power BI

   - Designing interactive reports and dashboards

   - Using different visualization types: bar charts, line charts, maps, etc.

   - Adding interactivity with slicers, filters, and drill-through

6.3 Hands-on Project: Data Visualization in Power BI

   - Importing and transforming data in Power BI Desktop

   - Designing visualizations and creating a dynamic dashboard

7.1 Power BI Data Modeling

   - Understanding data modeling concepts: star schema, snowflake schema

   - Creating relationships between tables

   - Implementing calculated columns and measures

7.2 Introduction to DAX (Data Analysis Expressions)

   - DAX syntax and functions

   - Calculated columns vs. measures

   - Aggregation functions: SUMX, AVERAGEX, COUNTX

7.3 Hands-on Project: Data Modeling and DAX in Power BI

   - Building a data model with multiple tables and relationships

   - Writing DAX expressions to perform calculations and create KPIs

8.1 Integration with Excel and SQL

   - Connecting Power BI to Excel and SQL databases

   - Importing data from Excel and SQL Server

   - Combining data from multiple sources in Power BI

8.2 Advanced Data Visualization Techniques

   - Custom visuals and plugins in Power BI

   - Geographic and time series visualizations

   - Creating reports for mobile devices

8.3 Real-Time Analytics with Power BI

   - Streaming data with Power BI Service

   - Building real-time dashboards and alerts

   - Case study: monitoring sensor data in real-time

9.1 Capstone Project Overview

   - Applying skills and knowledge acquired throughout the course

   - Working on a real-world data analytics project using Excel, SQL, and Power BI

   - Presentation and demonstration of the project to peers and instructors

9.2 Project Selection and Planning

   - Identifying a suitable dataset and problem statement

   - Defining project objectives and milestones

   - Planning project implementation and resource allocation

9.3 Project Execution and Evaluation

   - Data collection, preprocessing, and analysis

   - Building data models, writing SQL queries, and creating visualizations

   - Evaluating project outcomes and addressing challenges

9.4 Project Presentation and Documentation

   - Creating a comprehensive project report

   - Presenting findings, insights, and recommendations

   - Receiving feedback and reflecting on the project experience

10.1 Industry Case Studies

   - Real-world applications of data analytics with Excel, SQL, and Power BI

   - Success stories and lessons learned from industry leaders

10.2 Career Development in Data Analytics

   - Resume building and interview preparation

   - Networking opportunities and professional certifications

   - Continuing education and lifelong learning in data analytics

10.3 Emerging Trends in Data Analytics

   - Future directions and evolving technologies

   - Opportunities and challenges in the data-driven economy

   - Strategies for staying updated and adapting to change