flowchart LR
A[Source Data] --> B[Extract]
B --> C[Transform]
C --> D[Load]
D --> E[(Data Warehouse)]
E --> F[Analytics]
E --> G[Dashboards]
Modern slides with code, diagrams, and math
February 28, 2026
This template includes everything you need for technical presentations:
import pandas as pd
import matplotlib.pyplot as plt
# Load and process data
df = pd.read_csv("data.csv")
summary = df.groupby("category").agg({
"value": ["mean", "std", "count"]
})
# Visualize results
summary.plot(kind="bar", figsize=(10, 6))
plt.title("Category Summary")
plt.tight_layout()
plt.savefig("output.png")flowchart LR
A[Source Data] --> B[Extract]
B --> C[Transform]
C --> D[Load]
D --> E[(Data Warehouse)]
E --> F[Analytics]
E --> G[Dashboards]
sequenceDiagram
participant U as User
participant A as API
participant D as Database
U->>A: Request data
A->>D: Query
D-->>A: Results
A-->>U: JSON response
Inline equations work naturally: \(E = mc^2\)
Display equations for complex formulas:
\[ \mathcal{L} = -\frac{1}{4}F_{\mu\nu}F^{\mu\nu} + \bar{\psi}(i\gamma^\mu D_\mu - m)\psi \]
\[ P(A|B) = \frac{P(B|A) \cdot P(A)}{P(B)} \]
| Metric | Value |
|---|---|
| Users | 12,450 |
| Sessions | 45,230 |
| Conversion | 3.2% |
| Revenue | $84K |
Note
Callouts help important information stand out from the rest of your content.
Keyboard Shortcuts
Press F for fullscreen, O for overview, S for speaker notes.
Warning
Always test your presentation in the browser you’ll use for presenting.
Content can appear incrementally:
This keeps your audience focused on one idea at a time.
| Feature | Basic | Pro | Enterprise |
|---|---|---|---|
| Projects | 3 | 25 | Unlimited |
| Storage | 1 GB | 50 GB | 500 GB |
| API Access | - | Limited | Full |
| Support | Community | Dedicated | |
| SSO | - | - | Yes |
Sample visualization from Unsplash
The best way to predict the future is to invent it.
— Alan Kay
Use blockquotes for citations, testimonials, or emphasis.
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