OBJECTIVES
Module Learning Objectives
By the end of this module, students will be able to:
- Distinguish between intuition-based and data-driven decision making
- Translate vague business requirements into measurable metrics
- Identify the four types of business analytics and their applications
- Calculate ROI for analytics investments
- Recognize patterns in data that humans typically miss
Start by asking students about their worst business decision. Most will realize it was based on "gut feeling" rather than data. This creates immediate buy-in for the module.
PART 1
10 min Opening: The Coffee Shop Crisis
Hook Story
The $2M Mistake
CoffeeChain opened 15 new stores based on "prime locations" - high foot traffic areas near offices. Six months later, half are failing. Their competitor across the street? Thriving. Why?
Quick Poll (2 min)
- Ask: "Who here has made a decision based on gut feeling?"
- Follow up: "How did that work out?"
- Reveal: "73% of business decisions are still intuition-based"
The Big Reveal
Show the data: Competitor's "worse" location has parking. 73% of coffee revenue now comes from mobile orders for pickup. Foot traffic doesn't matter if customers can't park!
This isn't about replacing human judgment - it's about enhancing it with data. Intuition + Data = Powerful decisions.
PART 2
60 min Core Content: The Four Pillars
Section 1: Business vs. Data Thinking (15 min)
| Traditional Thinking |
Data-Driven Thinking |
Real Example |
| "Good location" |
Specific metrics: traffic, parking, demographics |
Starbucks uses 25+ variables |
| "Young customers" |
Age 18-34, income $30-60k, college educated |
Netflix segments by behavior, not age |
| "Popular product" |
Sales velocity, repeat purchase rate, margin |
Amazon's "frequently bought together" |
| "Good employee" |
Productivity metrics, retention risk score |
Google's Project Oxygen findings |
🎯 Interactive Exercise (5 min)
Give students a vague business requirement: "We need happy customers"
Have them translate it into 5 measurable metrics. Share and discuss.
Section 2: The Four Types of Analytics (20 min)
1. Descriptive Analytics - What Happened?
Example: "Sales dropped 15% last quarter"
Tools: Dashboards, reports, basic statistics
Business Value: Understand current state
2. Diagnostic Analytics - Why Did It Happen?
Example: "Sales dropped because competitor launched promotion"
Tools: Drill-down, correlation analysis, root cause
Business Value: Learn from the past
3. Predictive Analytics - What Will Happen?
Example: "Sales will increase 20% during back-to-school season"
Tools: Forecasting, machine learning, trends
Business Value: Prepare for the future
4. Prescriptive Analytics - What Should We Do?
Example: "Move 2 staff from Store A to Store B on Tuesdays"
Tools: Optimization, simulation, decision models
Business Value: Optimize operations
Use the same business scenario (coffee shop) to demonstrate all four types. This shows progression from basic to advanced analytics using familiar context.
Section 3: Your First Code (15 min)
Introduce Python as "Excel on steroids" - don't scare non-technical students!
import pandas as pd
data = pd.read_csv('sales_data.csv')
best_store = data.groupby('store')['revenue'].sum().max()
Address Common Fears
- "I'm not technical" → You don't need to be! It's like learning Excel formulas
- "It looks complicated" → We'll start with 5 simple commands
- "Why not just use Excel?" → Show example of analyzing 1 million rows
Section 4: ROI Calculation (10 min)
Make it real with numbers that matter to business:
| Metric |
Manual Process |
With Analytics |
Savings |
| Time per report |
8 hours |
30 minutes |
7.5 hours |
| Reports per month |
10 |
50 |
5x more insights |
| Bad decisions/month |
3 @ $50k |
0.5 @ $50k |
$125k/month |
| Annual Impact |
-$1.8M loss |
-$300k loss |
$1.5M saved |
Real companies see 5-10x ROI on analytics investments within the first year. This isn't theoretical - it's proven.
PART 3
15 min Guided Practice
Mini Case Study: Restaurant Location Analysis
🏃 Quick Exercise
Scenario: A pizza chain wants to open a new location. Management says: "Find us a spot with good vibes and young people who eat out a lot."
Student Task (5 min):
- Break into pairs
- Translate each vague term into 3 measurable metrics
- Identify what data sources you'd need
- Share with class
Live Coding Demo (10 min)
data = pd.read_csv('restaurant_locations.csv')
data.corr()['success_rate'].sort_values()
Let students suggest what to analyze next. When they see their question answered in one line of code, they're hooked!
RESOURCES
Additional Teaching Resources
Common Student Questions & Answers
Q: "Do I need to know math/statistics?"
A: No more than you need for Excel. The computer does the math - you interpret results.
Q: "Will AI replace analysts?"
A: AI is a tool that makes analysts more powerful, not obsolete. Someone needs to ask the right questions and interpret results in business context.
Q: "How is this different from regular reporting?"
A: Reporting tells you what happened. Analytics tells you why, predicts what's next, and recommends actions.
Troubleshooting Tips
- If students seem overwhelmed: Return to Excel comparison, show side-by-side
- If too technical: Focus on business outcomes, not code
- If too basic: Add competitive analysis angle - "your competitors are already doing this"
- For skeptics: Show real company case studies (Netflix, Amazon, Uber)
Extension Activities
- Bring in a real dataset from your organization
- Have students analyze their own online shopping behavior
- Compare two competing businesses using public data
- Create a mini dashboard for coffee shop data