Mini Project: Executive Sales & Profitability Dashboard0%

Mini Project: Executive Sales & Profitability Dashboard

Beginner12 min read•Updated: Jul 11, 2026
Study Materials

Mini Project: Executive Sales & Profitability Dashboard

Reading Time: 8 Mins | Level: Intermediate

1. Executive Dashboard Project Overview

In this capstone lesson, we bring together Matplotlib and Seaborn to construct a 2x2 Multi-Panel Executive Sales Dashboard displaying:

  1. 1
    Top-Left: Monthly Revenue Trend with Target Benchmark
  2. 2
    Top-Right: Revenue Breakdown by Product Category
  3. 3
    Bottom-Left: Profitability vs Discount Scatter Plot
  4. 4
    Bottom-Right: Regional Performance Comparison (Horizontal Bar Chart)

2. Complete Python Implementation

Python
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
 
# Set global theme
sns.set_theme(style='whitegrid')
fig, axes = plt.subplots(2, 2, figsize=(14, 10), dpi=150)
fig.suptitle('MSK Institute - Executive Sales & Profitability Dashboard (FY 2026)', fontsize=16, fontweight='bold', y=0.98)
 
# Panel 1: Monthly Trend
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
rev = [45, 52, 60, 68, 85, 95]
axes[0, 0].plot(months, rev, marker='o', color='#2563eb', linewidth=2.5, label='Actual')
axes[0, 0].axhline(70, color='#ef4444', linestyle='--', label='Monthly Target (70L)')
axes[0, 0].set_title('Monthly Revenue Trajectory (Lakhs INR)')
axes[0, 0].legend()
 
# Panel 2: Product Category Share
categories = ['Data Analytics', 'Full Stack Web', 'Python Backend', 'Cloud DevOps']
shares = [40, 25, 20, 15]
axes[0, 1].bar(categories, shares, color=['#3b82f6', '#10b981', '#f59e0b', '#8b5cf6'])
axes[0, 1].set_title('Revenue Share by Tech Track (%)')
axes[0, 1].set_xticklabels(categories, rotation=15, ha='right')
 
# Panel 3: Discount vs Profitability
discounts = np.random.uniform(5, 40, 100)
profits = 100 - (discounts * 2.2) + np.random.normal(0, 10, 100)
axes[1, 0].scatter(discounts, profits, color='#059669', alpha=0.7)
axes[1, 0].set_title('Discount % vs Operating Margin Impact')
axes[1, 0].set_xlabel('Discount Offered (%)')
axes[1, 0].set_ylabel('Profit Margin (%)')
 
# Panel 4: Regional Performance
regions = ['Delhi NCR', 'Western UP', 'Mumbai Metro', 'Bengaluru Hub']
performance = [320, 280, 210, 190]
axes[1, 1].barh(regions, performance, color='#0284c7')
axes[1, 1].invert_yaxis()
axes[1, 1].set_title('Gross Enrollments by Regional Hub')
 
plt.tight_layout()
plt.show()
Interactive Quiz

Multiple Choice Questions

Question 1
In a 2x2 subplot layout created with fig, axes = plt.subplots(2, 2), how is the bottom-left subplot referenced?
Question 2
Which command sets an overarching master title across the entire multi-panel dashboard figure?
Question 3
What is the primary purpose of constructing a multi-panel dashboard figure over showing isolated charts?
Question 4
How do you rotate x-axis tick labels by 45 degrees so long category strings do not overlap?
Question 5
What role does an executive dashboard play in a data analyst's professional portfolio?

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Practice Quiz

Test your understanding of this lesson with 5 questions. Each question has one correct answer.

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