Below is a 10-week structured program designed to turn students into job-ready data analysts using Excel, SQL, Power BI & Python.

🟦 Module 1: Introduction to Data Analysis (Week 1)

Live Class 1: What is Data Analysis?

• Understanding data

• Types of data (categorical, numeric, time series)

• Data analysis process (Collect → Clean → Analyze → Visualize → Report)

• Roles in the data world: data analyst, data scientist, BI analyst

Class Activity:
Analyze scenarios and identify business KPIs.

🟦 Module 2: Microsoft Excel for Data Analysis (Week 2–3)

Live Class 2: Excel Fundamentals for Analysis

• Worksheets, tables, formatting

• Sorting & filtering

• Basic functions (SUM, IF, COUNT, AVERAGE)

Live Class 3: Advanced Excel Functions

• VLOOKUP, XLOOKUP

• IF statements

• Text functions

• Date functions

Live Class 4: Data Cleaning & Pivot Tables

• Removing duplicates

• Handling missing values

• Pivot tables for aggregation

• Conditional formatting

Assignment:
Clean a student performance dataset + generate insights using pivot tables.

🟦 Module 3: SQL for Data Analysis (Week 4–5)

Live Class 5: Introduction to SQL

• What is SQL and where it is used

• Installing MySQL / Google Cloud SQL

• Creating databases & tables

Live Class 6: Querying Data

• SELECT, WHERE, ORDER BY

• LIMIT

• Filtering and searching data

Live Class 7: Joins & Aggregations

• INNER JOIN, LEFT JOIN

• GROUP BY, HAVING

• COUNT, SUM, AVG, MAX, MIN

Assignment:
Write SQL queries to analyze school payments or student attendance records.

🟦 Module 4: Power BI for Data Visualization (Week 6–7)

Live Class 8: Introduction to Power BI

• Importing datasets

• Data transformation using Power Query

• Relationship modeling

• Star schema basics

Live Class 9: Creating Interactive Dashboards

• Cards, tables, slicers

• Bar, column, pie, map visuals

• Creating drill-through pages

• Building KPI dashboards

Hands-On:
Create a dashboard for school fees collections or exam performance.

🟦 Module 5: Python for Data Analysis (Week 8–9)

Live Class 10: Python Basics for Data Analysts

• Installing Anaconda/Jupyter Notebook

• Python syntax

• Lists, dictionaries, loops

• Reading files (CSV, JSON)

Live Class 11: Data Analysis with Pandas

• DataFrames, indexing

• Cleaning data

• Handling missing data

• Applying functions

Live Class 12: Data Visualization with Matplotlib & Seaborn

• Line charts

• Histograms

• Scatter plots

• Correlation analysis

Assignment:
Analyze a dataset using Python and create visual findings.

🟦 Module 6: Final Project + Portfolio Build (Week 10)

Live Class 13: Full Data Analysis Project

Students complete a real-world project end-to-end:

Examples:

• School fees analysis dashboard

• Student performance forecast

• Sales & revenue dashboard

• Customer segmentation

Live Class 14: Portfolio & CV Guidance

• How to present projects

• Creating a data analyst portfolio

• LinkedIn optimization

• Job interview questions & mock prep

📌 What Students Will Gain

• Excel cleaning & analysis skills

• SQL querying & reporting

• Power BI visualization mastery

• Python data analysis foundation

• Ability to work on real business datasets

• A complete portfolio to apply for jobs