Data Science Workflows with AI Lab

Data Science Workflows with AI Lab

Dec 14, 2025 - 3 Min read

Jupyter + AI = Data Science Superpower

Data science is exploration. Questions lead to data, data leads to insights, insights lead to more questions. The faster you can explore, the more you can discover.

AI Lab accelerates every step.

The Data Science Loop

[Question]
    ↓
[Get Data] ← AI helps write queries
    ↓
[Explore] ← AI explains patterns
    ↓
[Analyze] ← AI suggests approaches
    ↓
[Visualize] ← AI creates plots
    ↓
[Interpret] ← AI helps explain findings
    ↓
[New Question]

AI doesn’t replace the scientist—it removes friction at every step.

Data Acquisition

Traditional: Write SQL, fight with schema, iterate on query.

With AI Lab: “Get me monthly sales by region for the last year”

AI writes the SQL, executes it, returns a DataFrame.

Exploration

Traditional: df.describe(), df.info(), manual inspection.

With AI Lab: “What are the interesting patterns in this data?”

AI runs exploratory analysis and highlights what’s notable.

Statistical Analysis

Traditional: Remember which test to use, look up syntax.

With AI Lab: “Is the difference between group A and group B statistically significant?”

AI selects appropriate test, runs it, interprets results.

Visualization

Traditional: plt.figure(), ax.plot(), hours of formatting.

With AI Lab: “Create a chart showing the trend over time with confidence intervals”

AI generates publication-ready visualizations.

Code Generation

Need specific code? Just ask:

“Write a function to clean this data:

Remove nulls in column X
Standardize column Y
One-hot encode column Z”

AI writes the function. You review and use.

The %calliope Magic

AI Lab includes the %calliope magic command:

%calliope ask-sql What customers churned last month?

%calliope chat Explain this correlation matrix

%calliope list-datasources

AI assistance directly in notebook cells.

Multi-Model Support

Different models for different tasks:

Complex analysis: Claude for nuanced reasoning Quick code: GPT-4 for fast generation Long context: Gemini for large datasets Privacy: Local models for sensitive data

Switch models per task.

Data Connectivity

AI Lab connects to your data:

PostgreSQL, MySQL, SQLite
Snowflake, BigQuery, Redshift
CSV, Excel, Parquet files
S3, GCS, Azure Blob

Your data, queryable through natural language.

Collaboration

Data science is collaborative:

Share notebooks: Work with your team Share datasets: Curate data for others Share to Chat Studio: Let non-technical users query your data

One data scientist’s work benefits many.

Reproducibility

AI Lab supports reproducible science:

Environment consistency: Same packages, same versions Notebook versioning: Track changes over time Data lineage: Know where data came from

Science you can trust.