Module 1 Lab: Hands-on Business Analytics

EXERCISE 1

The Restaurant Location Dilemma

Beginner Friendly

You're consulting for a pizza chain that wants to open a new location. The management team has given you their "gut feeling" criteria. Your job is to translate these into measurable data points.

🍕 Scenario: Pizza Palace Expansion

The CEO says: "We need a location with good vibes, lots of young people, and where people like to eat out."

Task 1: Translate "Good Vibes"

What measurable data could represent "good vibes"? Think about crime rates, reviews, foot traffic patterns...

Task 2: Define "Young People"

Convert this vague term into specific demographic data points. Age ranges? Income levels? Education?

Task 3: Measure "Like to Eat Out"

Find proxy metrics that indicate dining behavior. Credit card data? Restaurant density? Delivery app usage?

Think about how successful companies like Starbucks choose locations. They don't rely on feelings—they use demographic data, traffic patterns, competition analysis, and income levels. Every "feeling" can be translated into numbers!

EXERCISE 2

Analyzing Real Coffee Shop Data

Some Coding Required

Let's analyze actual coffee shop sales data to find patterns humans might miss. You'll write simple Python code to uncover insights.

Store ID	Location Type	Daily Sales ($)	Foot Traffic	Parking Spots	Mobile Orders (%)
Store_001	Downtown	$3,450	High	0	45%
Store_002	Suburban	$5,200	Medium	25	78%
Store_003	Downtown	$2,800	High	5	52%
Store_004	Suburban	$4,900	Low	30	82%
Store_005	Mall	$3,100	Very High	500	15%

analysis.py

# Your First Data Analysis!
# Let's find what really drives coffee shop revenue

import pandas as pd

# This is our coffee shop data
data = {
    'store': ['Store_001', 'Store_002', 'Store_003', 'Store_004', 'Store_005'],
    'location': ['Downtown', 'Suburban', 'Downtown', 'Suburban', 'Mall'],
    'daily_sales': [3450, 5200, 2800, 4900, 3100],
    'foot_traffic': ['High', 'Medium', 'High', 'Low', 'Very High'],
    'parking': [0, 25, 5, 30, 500],
    'mobile_orders': [45, 78, 52, 82, 15]
}

df = pd.DataFrame(data)

# TODO: Find the average sales by location type
# Hint: Use df.groupby('location')['daily_sales'].mean()

# TODO: Find correlation between parking and sales
# Hint: Is there a pattern?

print("🎯 Key Insight:")
print("Stores with more parking have higher sales!")
print(f"Suburban stores average: ${df[df['location']=='Suburban']['daily_sales'].mean():.0f}")
print(f"Downtown stores average: ${df[df['location']=='Downtown']['daily_sales'].mean():.0f}")

Task 1: Find the Surprise Pattern

Look at the data table. Which factor seems MOST correlated with high sales? It's not what management expected!

Task 2: Calculate the Mobile Order Impact

Modify the code to calculate average sales for stores with >70% mobile orders vs <70%

EXERCISE 3

Building Your Business Case

Business Math

Convince the CFO to invest in data analytics by calculating the real ROI.

💰 The Investment Decision

Current State (Manual)

📊 Monthly reports: 10
⏱️ Hours per report: 6
💰 Analyst cost: $50/hour
❌ Wrong decisions/month: 2
💸 Cost per wrong decision: $25,000

With Analytics Tools

📊 Monthly reports: 50
⏱️ Hours per report: 0.5
💰 Same analyst cost: $50/hour
✅ Wrong decisions/month: 0.5
💰 Tool cost: $500/month

roi_calculator.py

# ROI Calculation for Data Analytics Investment

# Current costs (monthly)
manual_report_hours = 10 * 6  # reports * hours each
manual_labor_cost = manual_report_hours * 50  # $50/hour
bad_decisions_cost = 2 * 25000  # 2 bad decisions * $25k each
current_total = manual_labor_cost + bad_decisions_cost

# With analytics (monthly)
auto_report_hours = 50 * 0.5  # more reports, less time!
auto_labor_cost = auto_report_hours * 50
tool_cost = 500
new_bad_decisions = 0.5 * 25000  # fewer mistakes!
new_total = auto_labor_cost + tool_cost + new_bad_decisions

# Calculate savings
monthly_savings = current_total - new_total
annual_savings = monthly_savings * 12

print(f"💰 Current monthly cost: ${current_total:,.0f}")
print(f"📊 With analytics cost: ${new_total:,.0f}")
print(f"✨ Monthly savings: ${monthly_savings:,.0f}")
print(f"🎯 ANNUAL SAVINGS: ${annual_savings:,.0f}")
print(f"\n🚀 ROI: {(annual_savings / (tool_cost * 12)) * 100:.0f}%")

CHALLENGE

The Complete Analysis Challenge

Advanced

Put it all together! You have data from 20 stores. Find the hidden pattern that predicts success.

🏆 The Ultimate Test

The CEO wants to know: "What single factor best predicts store success?"

Available Data:

📍 Distance to competitor
🚗 Parking availability
👥 Population density
💰 Average income
📱 Mobile order percentage
☀️ Weather (sunny days/year)
🎓 Nearby universities
🚇 Public transit access

🔬 Lab 1: From Gut Feeling to Data Insights

The Restaurant Location Dilemma

🍕 Scenario: Pizza Palace Expansion

Task 1: Translate "Good Vibes"

Task 2: Define "Young People"

Task 3: Measure "Like to Eat Out"

Analyzing Real Coffee Shop Data

Task 1: Find the Surprise Pattern

Task 2: Calculate the Mobile Order Impact

Building Your Business Case

💰 The Investment Decision

Current State (Manual)

With Analytics Tools

The Complete Analysis Challenge

🏆 The Ultimate Test

Ready to Submit Your Lab?

🔬 Lab 1: From Gut Feeling to Data Insights

The Restaurant Location Dilemma

🍕 Scenario: Pizza Palace Expansion

Task 1: Translate "Good Vibes"

Task 2: Define "Young People"

Task 3: Measure "Like to Eat Out"

Analyzing Real Coffee Shop Data

Task 1: Find the Surprise Pattern

Task 2: Calculate the Mobile Order Impact

Building Your Business Case

💰 The Investment Decision

Current State (Manual)

With Analytics Tools

The Complete Analysis Challenge

🏆 The Ultimate Test

Ready to Submit Your Lab?

🎉 Lab Complete!