Data Visualization — Overview

What is Data Visualization?

Data visualization is the graphical representation of information and data using charts, graphs, maps, and dashboards. The goal is to make complex data patterns immediately understandable.

Why it matters for Data Analysts:

•Raw data tables are impossible to interpret at scale

•Stakeholders need instant insight, not raw numbers

•Charts reveal trends, outliers, and correlations that SQL cannot

Core Chart Types and When to Use Each

Chart Type	Best For	Avoid When

|-----------|----------|-----------|

Bar / Column	Comparing categories	Too many categories (>15)
Line chart	Trends over time	Non-continuous data
Pie / Donut	Part-of-whole (max 5 slices)	Too many segments
Scatter plot	Correlation between 2 variables	Categorical data
Heatmap	Density / frequency patterns	Simple comparisons
Funnel	Conversion stages	Non-sequential data
Box plot	Distribution + outliers	Non-technical audience
Histogram	Frequency distribution	Categorical data
Treemap	Hierarchical proportions	Deep hierarchies
Waterfall	Incremental changes	Totals only

The Grammar of Effective Visualizations

1. Choose the right encoding

•Position (x/y axis): Most accurate perception

•Length: Bar charts use this well

•Color hue: Use for categories (max 8 distinct colors)

•Color saturation: Use for continuous values (heatmaps)

•Size: Use carefully (area vs diameter confusion)

•Shape: Use for categorical differentiation

2. Reduce cognitive load

•Remove grid lines that don't add value

•Direct label instead of using legends when possible

•Use consistent color meaning (red = bad, green = good)

•Align numbers right, text left

•Start y-axis at zero for bar charts (never truncate)

3. Data-to-ink ratio (Edward Tufte)

Remove everything that doesn't encode information:

•No 3D charts (distorts perception)

•No decorative backgrounds

•No excessive borders

•No redundant labels

Python Libraries for Visualization

python

# Matplotlib — low-level, full control
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(10, 6))
ax.bar(categories, values, color='steelblue')
ax.set_title('Monthly Revenue', fontsize=14, fontweight='bold')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
plt.tight_layout()
plt.savefig('revenue.png', dpi=150)

# Seaborn — statistical visualization
import seaborn as sns
sns.heatmap(df.corr(), annot=True, cmap='coolwarm', fmt='.2f')

# Plotly — interactive charts
import plotly.express as px
fig = px.scatter(df, x='ad_spend', y='revenue', color='channel',
                 size='conversions', hover_data=['campaign'])
fig.show()

# Pandas built-in plotting
df.groupby('month')['revenue'].sum().plot(kind='bar', figsize=(12,5))

Key Principles for Dashboard Design

1.Answer ONE question per chart

2.Most important metric: top-left (eye scans Z or F pattern)

3.Use filters/slicers for interactivity

4.Refresh cadence: Know if data is real-time, daily, weekly

5.Color palette: 2–3 brand colors, 1 accent for alerts

6.Mobile-friendly: Key metrics readable on 375px screen