1┌─────────────────────────────────────────────────────────┐
2│              Data Storytelling = 3 Pillars              │
3├─────────────────────────────────────────────────────────┤
4│                                                          │
5│     ┌──────────────┐                                    │
6│     │    DATA      │                                    │
7│     │   Analysis   │                                    │
8│     │   Accuracy   │                                    │
9│     │   Context    │                                    │
10│     └──────┬───────┘                                    │
11│            │                                            │
12│            v                                            │
13│     ┌──────────────┐      ┌──────────────┐             │
14│     │   VISUALS    │──────│  NARRATIVE   │             │
15│     │   Charts     │      │   Story Arc  │             │
16│     │   Design     │      │   Insights   │             │
17│     │   Clarity    │      │   Recommend  │             │
18│     └──────────────┘      └──────────────┘             │
19│                                                          │
20│            ═══════════════════════════                  │
21│                    STORY IMPACT                         │
22│                 Decision + Action                       │
23│                                                          │
24└─────────────────────────────────────────────────────────┘

1┌─────────────────────────────────────────────────────────┐
2│                  Audience Types                          │
3├─────────────────────────────────────────────────────────┤
4│                                                          │
5│  👔 EXECUTIVES                                          │
6│  • Want: Bottom line impact, strategic insights         │
7│  • Time: 2-5 minutes                                    │
8│  • Format: High-level summary, key metrics              │
9│  • Question: "So what?" and "What should we do?"        │
10│                                                          │
11│  📊 ANALYSTS / DATA TEAM                                │
12│  • Want: Methodology, details, validation               │
13│  • Time: 15-30 minutes                                  │
14│  • Format: Technical depth, assumptions                 │
15│  • Question: "How did you get this?" "Is it correct?"   │
16│                                                          │
17│  🎯 OPERATIONS / MANAGERS                               │
18│  • Want: Actionable steps, team impact                  │
19│  • Time: 5-10 minutes                                   │
20│  • Format: Specific recommendations, next steps         │
21│  • Question: "What do we need to change?"               │
22│                                                          │
23│  🌐 GENERAL STAKEHOLDERS                                │
24│  • Want: Context, simple explanations                   │
25│  • Time: 5-10 minutes                                   │
26│  • Format: Visuals, analogies, minimal jargon           │
27│  • Question: "Why does this matter to me?"              │
28│                                                          │
29└─────────────────────────────────────────────────────────┘

1# Example: Same data, different presentations
2
3# Revenue increased 15% YoY
4
5# For Executives
6"""
7Executive Summary:
8Revenue grew 15% ($2.3M) year-over-year, exceeding target by 3%.
9Key driver: New product line contributed 40% of growth.
10Recommendation: Increase marketing budget by $500K for Q2.
11"""
12
13# For Operations Team
14"""
15Q1 Performance:
16- Total revenue: $17.6M (+15% YoY)
17- Average order value: $127 (+8%)
18- Order volume: 138,500 (+6.5%)
19
20Action items:
211. Scale fulfillment capacity by 10%
222. Prioritize inventory for Product Line X
233. Extend support hours for peak periods
24"""
25
26# For Data Team
27"""
28Analysis Details:
29- Period: Q1 2024 vs Q1 2023
30- Data source: Sales_Master, cleaned for returns
31- Methodology: Standard YoY comparison, seasonally adjusted
32- Confidence: 95% CI [14.2%, 15.8%]
33- Caveats: Excludes one-time enterprise deal ($400K)
34"""

1┌─────────────────────────────────────────────────────────┐
2│                    SCQA Framework                        │
3├─────────────────────────────────────────────────────────┤
4│                                                          │
5│  S - SITUATION (Context)                                │
6│      "Currently, our customer churn rate is 8%..."      │
7│                                                          │
8│  C - COMPLICATION (Problem)                             │
9│      "...but this has increased 40% in the last         │
10│      quarter, threatening annual revenue by $2M."       │
11│                                                          │
12│  Q - QUESTION (What we need to solve)                   │
13│      "What's driving this increase and how can          │
14│      we reduce churn back to historical levels?"        │
15│                                                          │
16│  A - ANSWER (Your insight + recommendation)             │
17│      "Analysis shows 60% of churners cite pricing       │
18│      as primary reason. Introducing a loyalty tier      │
19│      could reduce churn by 25%, saving $500K."          │
20│                                                          │
21└─────────────────────────────────────────────────────────┘

1┌─────────────────┐
2                    │  Key Message    │   ← Start here!
3                    │   (So what?)    │
4                    └────────┬────────┘
5                             │
6        ┌────────────────────┼────────────────────┐
7        v                    v                    v
8   ┌─────────┐         ┌─────────┐         ┌─────────┐
9   │Support 1│         │Support 2│         │Support 3│
10   │ Point   │         │ Point   │         │ Point   │
11   └────┬────┘         └────┬────┘         └────┬────┘
12        │                   │                   │
13   ┌────┴────┐         ┌────┴────┐         ┌────┴────┐
14   │  Data   │         │  Data   │         │  Data   │
15   │Evidence │         │Evidence │         │Evidence │
16   └─────────┘         └─────────┘         └─────────┘
17 
18Example:
19Key Message: "We should expand to Region X"
20├── Support 1: Market size is $50M with 15% growth
21│   └── Data: Market research, competitor analysis
22├── Support 2: Customer demand exists (survey results)
23│   └── Data: Survey: 72% interested, 45% willing to pay premium
24└── Support 3: ROI positive within 18 months
25    └── Data: Financial projections, sensitivity analysis

1# Act 1: Setup (Where are we?)
2"""
3Context: Our e-commerce platform serves 500K active customers.
4         Average order value is $85 with 2.3 orders per customer/year.
5         We've been growing 20% annually for 3 years.
6"""
7
8# Act 2: Conflict (What's the problem?)
9"""
10Challenge: Q4 data shows concerning trends:
11- Cart abandonment increased from 68% to 76%
12- Mobile conversion dropped 15%
13- Customer complaints about checkout up 200%
14
15Root cause: Analysis reveals checkout flow has 7 steps
16vs. industry standard of 3-4 steps.
17"""
18
19# Act 3: Resolution (What should we do?)
20"""
21Solution: Redesign checkout to 4 steps
22Expected impact:
23- Reduce abandonment by 10 percentage points
24- Recover $2.5M in annual revenue
25- Timeline: 6 weeks development, 2 weeks testing
26
27Next steps: Approve budget, assign team, begin A/B test design
28"""

1┌──────────────────────────────────────────────────────────┐
2│                  Chart Selection Matrix                   │
3├──────────────────────────────────────────────────────────┤
4│                                                           │
5│  COMPARISON                                               │
6│  ├── Between categories → Bar Chart                      │
7│  ├── Over time → Line Chart                              │
8│  └── Multiple groups → Grouped/Stacked Bar              │
9│                                                           │
10│  COMPOSITION                                              │
11│  ├── Parts of whole (static) → Pie, Donut               │
12│  ├── Parts over time → Stacked Area                     │
13│  └── Hierarchical → Treemap                             │
14│                                                           │
15│  DISTRIBUTION                                             │
16│  ├── Single variable → Histogram, Box Plot              │
17│  ├── Two variables → Scatter Plot                       │
18│  └── Many variables → Heatmap                           │
19│                                                           │
20│  RELATIONSHIP                                             │
21│  ├── Two variables → Scatter Plot                       │
22│  ├── Correlation matrix → Heatmap                       │
23│  └── Network → Graph/Network Diagram                    │
24│                                                           │
25│  TREND                                                    │
26│  ├── Single series → Line Chart                         │
27│  ├── Multiple series → Multi-line                       │
28│  └── With target → Line + Reference Line                │
29│                                                           │
30└──────────────────────────────────────────────────────────┘

1import matplotlib.pyplot as plt
2import pandas as pd
3import numpy as np
4
5# Sample data
6categories = ['Product A', 'Product B', 'Product C', 'Product D', 'Product E']
7values = [23, 45, 12, 38, 29]
8
9# BAD: 3D Pie Chart (cluttered, hard to compare)
10fig, axes = plt.subplots(1, 2, figsize=(14, 5))
11
12# Don't use pie for comparisons
13axes[0].pie(values, labels=categories, autopct='%1.1f%%')
14axes[0].set_title('❌ BAD: Pie Chart for Comparison')
15
16# GOOD: Horizontal Bar Chart (easy to compare)
17sorted_idx = np.argsort(values)[::-1]
18sorted_cats = [categories[i] for i in sorted_idx]
19sorted_vals = [values[i] for i in sorted_idx]
20
21bars = axes[1].barh(sorted_cats, sorted_vals, color='steelblue')
22axes[1].set_xlabel('Sales ($K)')
23axes[1].set_title('✅ GOOD: Sorted Bar Chart')
24axes[1].bar_label(bars, fmt='$%dK')
25axes[1].invert_yaxis()
26
27plt.tight_layout()
28plt.show()

1# Highlight key data points
2fig, axes = plt.subplots(1, 2, figsize=(14, 5))
3
4months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
5sales = [120, 135, 128, 142, 138, 165]
6target = 150
7
8# BAD: All same color, no focus
9axes[0].plot(months, sales, 'o-', linewidth=2)
10axes[0].axhline(target, linestyle='--', label='Target')
11axes[0].set_title('❌ BAD: No Visual Hierarchy')
12axes[0].legend()
13
14# GOOD: Highlight the insight
15colors = ['gray' if v < target else 'green' for v in sales]
16axes[1].bar(months, sales, color=colors)
17axes[1].axhline(target, color='red', linestyle='--', label='Target: $150K')
18
19# Annotate the success
20axes[1].annotate('Hit target! ↑12%', 
21                 xy=(5, 165), 
22                 xytext=(4, 175),
23                 fontsize=11,
24                 fontweight='bold',
25                 color='green',
26                 arrowprops=dict(arrowstyle='->', color='green'))
27
28axes[1].set_ylabel('Sales ($K)')
29axes[1].set_title('✅ GOOD: Highlight Key Insight')
30axes[1].legend()
31
32plt.tight_layout()
33plt.show()

1INSIGHT = OBSERVATION + IMPLICATION + ACTION
2 
3Example:
4❌ Weak: "Sales increased 15% in Q4."
5 
6✅ Strong: "Sales increased 15% in Q4, driven primarily by 
7           the holiday promotion (contributing 60% of growth).
8           To sustain this momentum, we should extend the 
9           promotion into Q1 with a 'New Year' theme."

1Level 1: KEY FINDING (Lead with the insight)
2└── Level 2: Supporting Evidence
3    └── Level 3: Details/Data
4 
5Example Structure:
6 
7===========================================
8CUSTOMER CHURN IS COSTING US $2M ANNUALLY
9===========================================
10 
11Churn rate jumped 40% in Q4
12├── 8.2% vs 5.9% same period last year
13├── 2,400 customers lost (vs 1,700)
14└── Average customer value: $850
15 
16Top 3 reasons for churn (exit survey):
171. Pricing (38%) - Competitors undercutting
182. Service (28%) - Support response time
193. Features (19%) - Missing key functionality
20 
21Recommended action: Loyalty program launch
22├── Expected reduction: 25% of at-risk churners
23├── Investment: $150K
24└── ROI: 10x within 12 months

1# ❌ DON'T: Use jargon
2bad = """
3The YoY delta in the LTV:CAC ratio indicates a deteriorating 
4unit economics profile with ARPU declining 15% while CPA 
5increased 23%, resulting in suboptimal ROAS.
6"""
7
8# ✅ DO: Use plain language
9good = """
10We're spending more to acquire customers who are worth less:
11- Cost to acquire a customer: up 23%
12- Revenue per customer: down 15%
13- Result: We lose money until month 8 (vs month 5 last year)
14
15Bottom line: Each new customer now takes 3 months longer 
16to become profitable.
17"""
18
19# ❌ DON'T: Bury the lead
20bad_order = """
211. Methodology explanation (2 paragraphs)
222. Data collection process
233. Analysis approach
244. Findings summary
255. Recommendation (finally!)
26"""
27
28# ✅ DO: Lead with the conclusion
29good_order = """
301. Key recommendation and expected impact
312. Main finding supporting the recommendation
323. Evidence and data
334. Methodology notes (appendix or available on request)
34"""

1┌─────────────────────────────────────────────────────────┐
2│                  Effective Slide Layout                  │
3├─────────────────────────────────────────────────────────┤
4│                                                          │
5│  ┌─────────────────────────────────────────────────┐    │
6│  │ HEADLINE: States the insight (not just topic)   │    │
7│  │ ❌ "Q4 Sales Performance"                        │    │
8│  │ ✅ "Q4 Sales Beat Target by 15%"                 │    │
9│  └─────────────────────────────────────────────────┘    │
10│                                                          │
11│  ┌──────────────────────┬──────────────────────────┐    │
12│  │                      │                          │    │
13│  │      VISUAL          │     KEY TAKEAWAYS       │    │
14│  │      (Chart)         │     • Point 1           │    │
15│  │                      │     • Point 2           │    │
16│  │                      │     • Point 3           │    │
17│  │                      │                          │    │
18│  └──────────────────────┴──────────────────────────┘    │
19│                                                          │
20│  ┌─────────────────────────────────────────────────┐    │
21│  │ Source: Sales_Master database, Dec 2024         │    │
22│  └─────────────────────────────────────────────────┘    │
23│                                                          │
24└─────────────────────────────────────────────────────────┘

1PRESENTATION TEMPLATE: Analysis Findings
2 
31. EXECUTIVE SUMMARY (1 slide)
4   - Key finding
5   - Business impact ($)
6   - Recommendation
7 
82. CONTEXT (1-2 slides)
9   - Business question we answered
10   - Why it matters now
11 
123. KEY FINDINGS (3-5 slides)
13   - Finding 1 + evidence
14   - Finding 2 + evidence
15   - Finding 3 + evidence
16 
174. DEEP DIVE (optional, 2-3 slides)
18   - Detailed analysis
19   - Methodology notes
20   - Supporting data
21 
225. RECOMMENDATIONS (1-2 slides)
23   - Specific actions
24   - Expected outcomes
25   - Timeline and owners
26 
276. APPENDIX
28   - Detailed tables
29   - Methodology
30   - Additional charts

1import plotly.express as px
2import plotly.graph_objects as go
3from plotly.subplots import make_subplots
4
5# Create narrative dashboard
6fig = make_subplots(
7    rows=2, cols=2,
8    subplot_titles=(
9        '📈 Revenue Trend: Exceeding Targets',
10        '🎯 Customer Segments: Focus on Premium',
11        '📊 Regional Performance',
12        '💡 Key Metrics Summary'
13    ),
14    specs=[[{"type": "scatter"}, {"type": "pie"}],
15           [{"type": "bar"}, {"type": "table"}]]
16)
17
18# Data
19months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
20revenue = [1.2, 1.4, 1.3, 1.5, 1.6, 1.8]
21target = [1.3, 1.3, 1.3, 1.4, 1.4, 1.5]
22
23# Chart 1: Revenue trend with annotation
24fig.add_trace(
25    go.Scatter(x=months, y=revenue, name='Actual', line=dict(width=3, color='green')),
26    row=1, col=1
27)
28fig.add_trace(
29    go.Scatter(x=months, y=target, name='Target', line=dict(dash='dash', color='gray')),
30    row=1, col=1
31)
32
33# Chart 2: Customer segments
34fig.add_trace(
35    go.Pie(labels=['Premium', 'Standard', 'Basic'], 
36           values=[45, 35, 20],
37           marker_colors=['gold', 'steelblue', 'lightgray']),
38    row=1, col=2
39)
40
41# Chart 3: Regional
42regions = ['North', 'South', 'East', 'West']
43performance = [120, 95, 110, 85]
44colors = ['green' if p >= 100 else 'red' for p in performance]
45fig.add_trace(
46    go.Bar(x=regions, y=performance, marker_color=colors, name='vs Target %'),
47    row=2, col=1
48)
49
50# Chart 4: Summary table
51fig.add_trace(
52    go.Table(
53        header=dict(values=['Metric', 'Value', 'Status']),
54        cells=dict(values=[
55            ['Revenue', 'Growth', 'NPS'],
56            ['$8.8M', '+23%', '72'],
57            ['✅ On Track', '✅ Above Target', '⚠️ Monitor']
58        ])
59    ),
60    row=2, col=2
61)
62
63fig.update_layout(
64    title_text="Q2 Business Review: Strong Performance with Growth Opportunities",
65    title_font_size=16,
66    showlegend=False,
67    height=600
68)
69
70# Add annotation highlighting key insight
71fig.add_annotation(
72    x='Jun', y=1.8,
73    text="Record month!<br>+20% vs target",
74    showarrow=True,
75    arrowhead=2,
76    row=1, col=1
77)
78
79fig.show()

1# Structure for scrollytelling (conceptual)
2narrative_sections = [
3    {
4        'headline': 'Customer behavior is changing',
5        'visual': 'time_series_chart',
6        'text': 'Over the past year, mobile purchases have grown 45% while desktop declined 12%.',
7        'data_highlight': 'mobile_growth'
8    },
9    {
10        'headline': 'Mobile users convert differently',
11        'visual': 'funnel_comparison',
12        'text': 'Mobile users browse 3x more products but convert 40% less. Cart abandonment is the key blocker.',
13        'data_highlight': 'cart_abandonment'
14    },
15    {
16        'headline': 'The $3M opportunity',
17        'visual': 'impact_calculator',
18        'text': 'Improving mobile checkout could recover 15% of abandoned carts, worth $3M annually.',
19        'data_highlight': 'revenue_opportunity'
20    },
21    {
22        'headline': 'Recommended action',
23        'visual': 'roadmap',
24        'text': 'A simplified mobile checkout with Apple Pay integration could be live in 8 weeks.',
25        'data_highlight': 'timeline'
26    }
27]

1# Build a complete data story:
2# 1. Define audience and objective
3# 2. Structure the narrative (SCQA)
4# 3. Create supporting visualizations
5# 4. Write compelling insights
6# 5. Design executive summary
7
8# YOUR CODE HERE