MODULE 1: DATA ANALYSIS FOUNDATION
Session 1: Data Analysis Introduction
🕒 Time: 1 Hour
🎯 Objective: Understand what Data Analysis is, its importance, and how it is used in decision-making.
🔹 Example:
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A retail store analyses customer purchases to identify the best-selling products.
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Netflix analyses user watch history to recommend movies.
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"Based on the data trends, we should stock more summer clothing in May."
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"The sales report shows a 20% increase in demand for organic products."
Session 2: Data Preparation for Analysis
🕒 Time: 1 Hour
🎯 Objective: Learn how to clean, transform, and prepare data for analysis.
🔹 Example:
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Removing duplicate customer records in an e-commerce database.
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Handling missing values in survey responses.
📌 Real-Life Terminology Usage:
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"We need to remove incomplete entries before analyzing survey results."
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"Let's convert all currency values to USD for consistency."
Session 3: Common Data Problems
🕒 Time: 1 Hour
🎯 Objective: Identify common data issues and how to fix them.
🔹 Example:
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An HR team finds that employee age data contains negative values.
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A bank finds transaction timestamps missing for some records.
📌 Real-Life Terminology Usage:
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"Let's standardize date formats before processing."
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"We found inconsistencies in the revenue reports; let's clean the data first."
Session 4: Various Tools for Data Analysis
🕒 Time: 1 Hour
🎯 Objective: Introduction to tools like Excel, Power BI, Tableau, Python, and R.
🔹 Example:
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A logistics company uses Power BI to visualize shipment delays.
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A financial analyst uses Excel PivotTables to summarize sales data.
📌 Real-Life Terminology Usage:
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"We use Power BI dashboards for real-time sales tracking."
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"Excel’s VLOOKUP helps merge data from different sheets."
Session 5: Evolution of Analytics Domain
🕒 Time: 1 Hour
🎯 Objective: Understand how data analytics has evolved from simple reports to AI-powered insights.
🔹 Example:
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Early analytics: Excel-based financial reports.
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Modern analytics: AI-driven stock market predictions.
📌 Real-Life Terminology Usage:
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"Earlier, we manually checked trends, but now AI predicts them."
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"Data-driven decisions have revolutionized customer experience."
MODULE 2: CLASSIFICATION OF ANALYTICS
Session 6: Four Types of Analytics
🕒 Time: 1 Hour
1️⃣ Descriptive Analytics (What happened?)
🔹 Example: Monthly sales reports show total revenue and growth.
📌 "Last month's report showed a 15% increase in sales."
2️⃣ Diagnostic Analytics (Why did it happen?)
🔹 Example: A drop in sales was due to stock shortages.
📌 "Sales dropped in July because of supply chain delays."
3️⃣ Predictive Analytics (What will happen?)
🔹 Example: Predicting next month’s customer demand.
📌 "We expect a 10% rise in demand before Diwali."
4️⃣ Prescriptive Analytics (What should be done?)
🔹 Example: AI suggests reducing prices to boost sales.
📌 "Lowering prices by 5% could increase revenue by 20%."
Day 2: CRIP-DM Model, Data Analysis Techniques & Visualization
MODULE 3: CRIP-DM Model
Session 7: CRIP-DM Model Overview
🕒 Time: 1 Hour
🎯 Objective: Understand how businesses apply analytics systematically.
🔹 Example:
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A telecom company uses CRIP-DM for customer churn prediction.
📌 Real-Life Terminology Usage:
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"Before launching the new product, let's analyze customer feedback using the CRIP-DM framework."
MODULE 4: UNIVARIATE DATA ANALYSIS
Session 8: Summary Statistics & Central Tendency
🕒 Time: 1 Hour
🎯 Objective: Learn to compute Mean, Median, and Mode.
🔹 Example:
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Average monthly expenses of employees.
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Finding the most frequent product purchased in a store.
📌 Real-Life Terminology Usage:
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"The average score in the exam was 75%."
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"The median salary in our company is ₹50,000 per month."
MODULE 5: DATA ANALYSIS WITH VISUAL CHARTS
Session 9: Visualization with Charts
🕒 Time: 1.5 Hours
📊 Real-Life Examples of Charts:
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Line Chart: Website traffic over months.
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Column/Bar Chart: Product sales comparison.
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Waterfall Chart: Revenue contribution from different channels.
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Tree Map Chart: Product categories contribution to total sales.
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Box Plot: Distribution of employee salaries.
📌 Real-Life Terminology Usage:
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"A bar chart clearly shows the top-selling products."
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"A box plot helps us see salary disparities in different departments."
MODULE 6: BI-VARIATE DATA ANALYSIS
Session 10: Regression & Correlation Analysis
🕒 Time: 1 Hour
🔹 Example:
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Analyzing how temperature affects ice cream sales.
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Relationship between advertising budget and revenue.
📌 Real-Life Terminology Usage:
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"A high correlation between marketing spend and revenue shows a positive impact."
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"Regression analysis predicts next quarter’s sales based on past trends."
MODULE 7: Advanced Power BI with Probability & Prediction
Session 11: Power BI Forecasting & Custom Predictions
🕒 Time: 1.5 Hours
🔹 Example:
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Power BI predicting next month’s revenue based on past trends.
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Using Python scripts in Power BI for advanced forecasting.
📌 Real-Life Terminology Usage:
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"Power BI's AI-powered forecasting helps predict seasonal sales trends."
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"Using Python in Power BI, we customized demand forecasting for different regions."
Final Hands-On Exercise & Q&A
🕒 Time: 2 Hours
🎯 Objective: Participants apply concepts to real-world datasets and present insights.
🔹 Real-Life Scenario:
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Analyzing customer feedback data for a product launch strategy.
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Predicting employee attrition using historical HR data.
📌 Final Takeaways:
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"Data analysis is the foundation of smart business decisions."
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"Understanding past trends helps predict and improve future outcomes."
Conclusion & Certification
🕒 Time: 30 Mins
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Recap of key learnings.
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Distribution of certificates.
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Feedback & next steps.
🚀 Outcome:
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Participants gain hands-on experience in data analytics.
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They understand real-life applications of analytics in business & daily life.
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They learn to communicate insights effectively using the right terminology.
Would you like any modifications or additional hands-on case studies? 😊
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