Learning Objectives

• Build business dashboards
• Create KPIs and metrics
• Tell data-driven stories

Dashboard with Plotly Dash

python
Run Inline
import dash
from dash import dcc, html
import plotly.express as px
import pandas as pd
app = dash.Dash(__name__)
# Load data
df = pd.read_csv('data/processed/sales.csv')
# Create figures
sales_trend = px.line(df.groupby('date')['revenue'].sum().reset_index(),
                       x='date', y='revenue', title='Daily Revenue')
region_chart = px.bar(df.groupby('region')['revenue'].sum().reset_index(),
                       x='region', y='revenue', title='Revenue by Region')
app.layout = html.Div([
    html.H1('Business Dashboard'),
    dcc.Graph(figure=sales_trend),
    dcc.Graph(figure=region_chart)
])
if __name__ == '__main__':
    app.run(debug=True)

Q1: What KPIs would you track for an e-commerce dashboard?

Revenue, conversion rate, average order value, customer acquisition cost, customer lifetime value, churn rate, cart abandonment rate, repeat purchase rate. Q2: What makes a dashboard effective?

Clear purpose, audience-appropriate, most important metrics prominent, filters/interactivity, consistent design, real-time or appropriate refresh, actionable insights. Q3: How to choose chart type?

Trend: line. Comparison: bar. Composition: stacked bar/pie. Distribution: histogram/box. Relationship: scatter. Geospatial: map. Rank: bar sorted. Q4: What is a KPI tree?

Hierarchical breakdown of business metrics. Top: overall goal (revenue). Level 2: drivers (customers x avg order). Level 3: granular metrics (traffic, conversion, price). Q5: Dashboard tools?

Power BI, Tableau, Metabase (open source), Superset, Google Data Studio, Plotly Dash (Python), Streamlit. Q6: Create KPI card in HTML:

html
<div class="kpi-card">
  <h3>Monthly Revenue</h3>
  <p class="value">$1,234,567</p>
  <p class="change positive">+12.5% vs last month</p>
</div>