Notebook 3.3 - Visualization & Integration

Management Science - CEO Presentations That Wow!

Introduction

Welcome to your final CEO challenge at Bean Counter!

The Board Meeting Challenge

Tomorrow morning, you’re presenting Bean Counter’s annual performance to the board of directors and potential investors. You have:

Massive datasets analyzed with NumPy
Complex reports created with Pandas
Incredible insights about the business

The Problem: Numbers alone don’t convince boards. They need to SEE the story! A table with 1000 rows won’t inspire investment, but a compelling chart will.

Your Solution: Data Visualization - turning your analyses into powerful visual stories that drive decisions and secure funding.

In this tutorial, you’ll create the visualizations that will secure Bean Counter’s future and cement your legacy as CEO.

How to Use This Tutorial

Import matplotlib along with pandas and numpy. Remember, we work with uv, so just use uv add matplotlib in the terminal. Now you can create compelling charts for your board presentation!

Start practicing generate AI

The creation of plots is a perfect opportunity to start practicing generate AI. Usually you need a lot of code for nice visualizations and most of it is repetitive.

Section 1 - Bar Charts for Performance Comparison

As CEO, you often need to compare performance across stores, products, or time periods. Bar charts are perfect for this.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Top 5 store performance
stores = ['Airport', 'Downtown', 'Mall', 'Plaza', 'University']
revenue = [620, 450, 510, 485, 380]

# Create bar chart
plt.figure(figsize=(10, 6))
plt.bar(stores, revenue, color='#F6B265')
plt.title('Bean Counter Top 5 Store Revenue (Thousands $)', fontsize=16, fontweight='bold')
plt.xlabel('Store Location', fontsize=12)
plt.ylabel('Revenue ($1000s)', fontsize=12)
plt.grid(axis='y', alpha=0.3)
plt.xticks(rotation=45)

# Add value labels on bars
for i, v in enumerate(revenue):
    plt.text(i, v + 10, f'${v}k', ha='center', fontweight='bold')

plt.tight_layout()
plt.show()

Bar Charts Are Your Best Friend

As CEO, bar charts will be your most-used visualization:

Compare stores, products, or departments
Show rankings clearly
Easy for board members to understand
Perfect for “top 10” or “bottom 5” analyses

Exercise 1.1 - Product Performance Bar Chart

Create a bar chart showing Bean Counter’s product sales for the board meeting.

import pandas as pd
import matplotlib.pyplot as plt

# Product sales data
products_df = pd.DataFrame({
    'product': ['Latte', 'Espresso', 'Cappuccino', 'Americano', 'Mocha', 'Macchiato'],
    'units_sold': [3200, 4500, 2800, 2100, 1900, 1500]
})

# YOUR CODE BELOW
# 1. Sort products by units_sold (highest first)
products_sorted =

# 2. Create a bar chart

Section 2 - Line Plots for Trends Over Time

CEOs need to show growth and trends. Line plots are perfect for time series data.

import pandas as pd
import matplotlib.pyplot as plt

# Monthly revenue trend
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
revenue = [4.2, 4.5, 4.8, 5.1, 5.4, 5.8]

plt.figure(figsize=(10, 6))
plt.plot(months, revenue, marker='o', linewidth=2, markersize=8, color='#537E8F')
plt.title('Bean Counter Revenue Growth - H1 2024', fontsize=16, fontweight='bold')
plt.xlabel('Month', fontsize=12)
plt.ylabel('Revenue ($ Millions)', fontsize=12)
plt.grid(True, alpha=0.3)

# Add value labels
for i, v in enumerate(revenue):
    plt.text(i, v + 0.1, f'${v}M', ha='center')

plt.tight_layout()
plt.show()

Exercise 2.1 - Customer Growth Trend

Show the board Bean Counter’s customer growth over the past 8 months.

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

# Customer data (in thousands)
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug']
customers = [145, 152, 158, 165, 174, 182, 195, 208]

# YOUR CODE BELOW
# Create a line plot showing customer growth

Section 3 - Histograms for Distribution Analysis

Show the board how metrics are distributed across your empire.

import numpy as np
import matplotlib.pyplot as plt

# Simulate customer satisfaction scores
np.random.seed(42)
satisfaction_scores = np.random.normal(4.3, 0.4, 1000)
satisfaction_scores = np.clip(satisfaction_scores, 1, 5)

plt.figure(figsize=(10, 6))
plt.hist(satisfaction_scores, bins=20, alpha=0.7, edgecolor='black')
plt.title('Customer Satisfaction Distribution (n=1000)', fontsize=16, fontweight='bold')
plt.xlabel('Satisfaction Score', fontsize=12)
plt.ylabel('Number of Responses', fontsize=12)
plt.axvline(x=satisfaction_scores.mean(), color='red', linestyle='--', linewidth=2,
           label=f'Mean: {satisfaction_scores.mean():.2f}')
plt.legend()
plt.grid(axis='y', alpha=0.3)
plt.tight_layout()
plt.show()

Exercise 3.1 - Store Efficiency Distribution

Show the board how store efficiency is distributed across all locations.

import numpy as np
import matplotlib.pyplot as plt

# Simulate efficiency scores for 50 stores
np.random.seed(100)
efficiency_scores = np.random.normal(75, 12, 50)  # Mean 75, std 12

# YOUR CODE BELOW
# Create histogram of efficiency distribution

Section 4 - Integrated Analysis with Pandas

Combine Pandas data manipulation with visualization for powerful insights.

import pandas as pd
import matplotlib.pyplot as plt

# Quarterly performance data
quarters_df = pd.DataFrame({
    'quarter': ['Q1', 'Q2', 'Q3', 'Q4'],
    'revenue': [4.2, 4.8, 5.3, 5.9],
    'profit': [1.3, 1.5, 1.8, 2.1],
    'customers': [145, 165, 188, 210]
})

# Create subplots for comprehensive view
fig, axes = plt.subplots(1, 3, figsize=(15, 5))

# Revenue trend
axes[0].plot(quarters_df['quarter'], quarters_df['revenue'],
            marker='o', linewidth=2, markersize=10, color='#2E7D32')
axes[0].set_title('Revenue Growth', fontsize=14, fontweight='bold')
axes[0].set_ylabel('Revenue ($M)', fontsize=11)
axes[0].grid(True, alpha=0.3)

# Profit bars
axes[1].bar(quarters_df['quarter'], quarters_df['profit'], color='#1976D2')
axes[1].set_title('Quarterly Profit', fontsize=14, fontweight='bold')
axes[1].set_ylabel('Profit ($M)', fontsize=11)
axes[1].grid(axis='y', alpha=0.3)

# Customer growth
axes[2].plot(quarters_df['quarter'], quarters_df['customers'],
            marker='s', linewidth=2, markersize=10, color='#D32F2F')
axes[2].set_title('Customer Base', fontsize=14, fontweight='bold')
axes[2].set_ylabel('Customers (K)', fontsize=11)
axes[2].grid(True, alpha=0.3)

plt.suptitle('Bean Counter 2024 Performance Dashboard', fontsize=16, fontweight='bold')
plt.tight_layout()
plt.show()

Exercise 4.1 - Complete CEO Dashboard

Create an integrated dashboard combining NumPy simulation with Pandas analysis and visualization. Create revenue projections for the company:

Start at $6M in January
Grow approximately 2% each month
Add random variation (±25%) to simulate market fluctuations

To create realistic monthly growth:

Start with base revenue of 6.0M
Each month, apply 2% growth: revenue = previous_revenue * 1.02
Add random variation: multiply by np.random.uniform(0.75, 1.25)

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Simulate next year's projections
np.random.seed(42)
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
          'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']

# YOUR CODE BELOW
# 1. Create revenue projections for the company
# Start with 6.0M, grow each month by 2% with random variation
revenue_projection = []
current_revenue = 6.0  # Starting revenue in millions

# Loop through 12 months


# 2. Create DataFrame with months and revenue


# 3. Calculate cumulative revenue


# 4. Create visualization with two subplots

Conclusion

Congratulations! You’ve completed your CEO journey at Bean Counter!

You’ve mastered:

Bar Charts - Comparing performance across stores and products
Line Plots - Showing growth trends over time
Histograms - Understanding distributions of key metrics
Integrated Dashboards - Combining multiple visualizations for comprehensive insights
NumPy + Pandas + Visualization - The complete data science toolkit

Your Bean Counter CEO visualization toolkit enables you to:

Present compelling stories to the board of directors
Identify trends and patterns at a glance
Compare performance across multiple dimensions
Make data-driven decisions with confidence
Communicate complex insights simply and effectively

Remember:

Bar charts are best for comparisons
Line plots show trends over time
Histograms reveal distributions
Always label your axes and add titles
Use colors consistently across related charts
Annotations can highlight key insights

Your Bean Counter Legacy:

From Barista Trainee to CEO, you’ve transformed Bean Counter into a data-driven enterprise.

Standardized operations with functions
Managed complex data with dictionaries
Optimized decisions with sorting algorithms
Processed big data with NumPy’s speed
Analyzed business metrics with Pandas
Communicated insights with powerful visualizations

What’s Next: Armed with these data science foundations, you’re ready for the advanced algorithms in upcoming lectures! You’ll tackle Monte Carlo simulations, forecasting, scheduling optimization, and more. The skills you’ve built here - from basic Python to advanced visualization - will be the foundation for solving complex management science problems!

Solutions

You will likely find solutions to most exercises online. However, I strongly encourage you to work on these exercises independently without searching explicitly for the exact answers to the exercises. Understanding someone else’s solution is very different from developing your own. Use the lecture notes and try to solve the exercises on your own. This approach will significantly enhance your learning and problem-solving skills.

Remember, the goal is not just to complete the exercises, but to understand the concepts and improve your programming abilities. If you encounter difficulties, review the lecture materials, experiment with different approaches, and don’t hesitate to ask for clarification during class discussions.