Claude Code Agent: Data Scientist — Analysis & Visualization

What it is

The Data Scientist agent is a Claude Code agent template focused on data science workflows. It activates automatically when you work on exploratory analysis, statistical modeling, data visualization, feature engineering, or Jupyter notebook tasks. The agent understands pandas, matplotlib, seaborn, scikit-learn, and common data science patterns.

This agent is for data scientists and analysts who use Claude Code and want domain-specific assistance. Instead of explaining your data science context every time, the agent comes pre-configured with knowledge of best practices for analysis workflows.

How it saves time or tokens

The agent provides data science context out of the box, eliminating the prompt engineering needed to make a general assistant produce clean analysis code. It knows to suggest appropriate chart types, handle missing data correctly, and structure notebooks with clear documentation. One command installs the agent.

How to use

1. Install the Data Scientist agent template:

npx claude-code-templates@latest --agent data-ai/data-scientist --yes

1. The agent activates automatically when data science tasks are detected.

1. Ask it to handle analysis tasks:

'Explore this dataset and show distributions for all numeric columns'
'Build a correlation heatmap and identify the top features'
'Create a classification model and show the confusion matrix'

Example

# The agent generates structured analysis code like this:
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

df = pd.read_csv('customers.csv')

# Summary statistics
print(df.describe())
print(f'Missing values:\n{df.isnull().sum()}')

# Distribution plots
fig, axes = plt.subplots(2, 3, figsize=(15, 10))
for i, col in enumerate(df.select_dtypes('number').columns[:6]):
    ax = axes[i // 3][i % 3]
    df[col].hist(ax=ax, bins=30)
    ax.set_title(col)
plt.tight_layout()
plt.savefig('distributions.png', dpi=150)

# Correlation matrix
corr = df.select_dtypes('number').corr()
sns.heatmap(corr, annot=True, cmap='coolwarm', center=0)
plt.savefig('correlation.png', dpi=150)

Related on TokRepo

• AI tools for research -- Research and analysis tools
• AI coding tools -- Developer productivity agents

Common pitfalls

• The agent generates code but does not execute it. Review the generated analysis code before running it on sensitive datasets.
• Large datasets may require chunked processing. The agent suggests appropriate techniques when it detects memory-intensive operations.
• The agent template requires Claude Code to be installed. It does not work with other AI coding tools.