Issue 14 | Automated Data Analysis and Insight Extraction with CSV

🎯 Learning Objectives

Upon completing this issue, you will master:

1. How to enable Claude Code to read CSV/Excel data sources for automated analysis
2. Building funnel analysis, user retention matrices, and anomaly detection
3. Data visualization output (charts + reports)
4. Completing an end-to-end process from raw data to executive reports in the terminal

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📖 Core Concepts Explained

14.1 Advantages of Terminal Data Analysis

Traditional Method:
  Download CSV → Open Excel → Manual Pivot →
  Screenshot → Paste into PPT → Write conclusions
  Time spent: 2-4 hours

Claude Code Method:
  "Analyze this CSV, generate a visual report and key insights"
  Time spent: 3-5 minutes

Claude's capabilities in data analysis:
  ✅ Automatically identify column meanings and data types
  ✅ Select appropriate analysis methods
  ✅ Generate Python / JS data processing scripts
  ✅ Output analysis reports in Markdown format
  ✅ Generate interactive HTML charts

14.2 Analysis Flow

Input CSV
  │
  ▼
Claude reads and understands data structure
  │
  ▼
Identify analysis dimensions (time / category / numerical)
  │
  ▼
Select analysis methods: 
  • Trend analysis
  • Distribution statistics  
  • Funnel conversion
  • YoY/MoM comparison
  │
  ▼
Generate analysis script (Python pandas)
  │
  ▼
Execute script, output:
  • Data summary table
  • Visual charts (HTML / matplotlib)
  • Key insights report

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💻 Simulating Claude TUI Interaction

Scenario: User Behavior Funnel Analysis

> This is our product's user behavior data user_events.csv.
  Please perform a registration conversion funnel analysis.

╭─ Claude ─────────────────────────────────────────────╮
│                                                      │
│  🔍 Tool: Read → user_events.csv                    │
│  📊 Data Overview:                                   │
│  - Rows: 45,832 rows                                 │
│  - Columns: user_id, event_type, timestamp, device   │
│  - Time range: 2024-01-01 ~ 2024-03-31               │
│  - Event types: visit, signup_click, form_fill,      │
│    email_verify, profile_complete                    │
│                                                      │
│  ⚙️ Tool: Write → analyze_funnel.py                  │
│  ⚙️ Tool: Bash                                       │
│  $ python analyze_funnel.py                           │
│                                                      │
│  📋 Registration Conversion Funnel:                  │
│  ╭────────────────────────────────────────────╮      │
│  │ Step           │  Users  │ Conversion │ Drop-off │      │
│  │───────────────│────────│───────────│─────────│      │
│  │ Visit Homepage │ 12,450 │ 100%      │  -      │      │
│  │ Click Register │  4,230 │ 34.0%     │ 66%     │      │
│  │ Fill Form      │  2,815 │ 66.5%     │ 33%     │      │
│  │ Email Verify   │  1,906 │ 67.7%     │ 32%     │      │
│  │ Complete Profile │  1,524 │ 80.0%     │ 20%     │      │
│  ╰────────────────────────────────────────────╯      │
│                                                      │
│  🔑 Key Insights:                                    │
│  1. The churn rate from Homepage → Register is the highest (66%),   │
│     suggest optimizing CTA.                          │
│  2. iOS users' registration completion rate (15.2%) is significantly │
│     higher than Android (9.8%), possibly related to form experience.│
│  3. Weekend registration volume is 42% higher than weekdays.       │
│                                                      │
│  ⚙️ Tool: Write → funnel_report.html                 │
│  ✅ Interactive funnel chart generated, open in browser to view.   │
│                                                      │
╰──────────────────────────────────────────────────────╯

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💻 Code Demo

# ✅ Basic Data Analysis
claude "Read data/sales.csv and tell me:
  1. Monthly sales trends
  2. Top 10 products by sales revenue
  3. Which region is growing fastest"

# ✅ Generate Visual Report
claude "Analyze data/metrics.csv,
  generate an HTML report page with charts"

# ✅ Anomaly Detection
claude "Analyze data/server_logs.csv,
  find anomalous request volume peaks and possible causes"

# ✅ Comparative Analysis
claude "Compare q1_data.csv and q2_data.csv,
  find the year-over-year changes in key metrics"

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🔧 Tools Involved

Tool	Analysis Stage	Purpose
Read	Data Loading	Reads CSV/JSON data files
Write	Script Generation	Creates Python analysis scripts
Bash	Analysis Execution	Runs pandas/matplotlib
Write	Report Output	Generates HTML/Markdown reports

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📝 Key Takeaways from This Issue

1. Claude Code can complete the entire process from data to report end-to-end
2. Describing the analysis objective is more important than describing the analysis method
3. Generated analysis scripts can be reused multiple times
4. HTML reports can be shared directly with the team, no PPT conversion needed
5. Suitable for PMs' daily quick data insight scenarios

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🔗 References

• Analyze data with Claude Code