claude-scientific-skills/scientific-thinking/exploratory-data-analysis/assets/report_template.md

Exploratory Data Analysis Report

Dataset: [Dataset Name] Analysis Date: [Date] Analyst: [Name]

---

Executive Summary

[2-3 paragraph summary of key findings, major insights, and recommendations]

Key Findings:

• [Finding 1]
• [Finding 2]
• [Finding 3]

Recommendations:

• [Recommendation 1]
• [Recommendation 2]

---

1. Dataset Overview

1.1 Data Source

• Source: [Source name and location]
• Collection Period: [Date range]
• Last Updated: [Date]
• Format: [CSV, Excel, JSON, etc.]

1.2 Data Structure

• Observations (Rows): [Number]
• Variables (Columns): [Number]
• Memory Usage: [Size in MB]

1.3 Variable Types

• Numeric Variables ([Count]): [List column names]
• Categorical Variables ([Count]): [List column names]
• Datetime Variables ([Count]): [List column names]
• Boolean Variables ([Count]): [List column names]

---

2. Data Quality Assessment

2.1 Completeness

Overall Data Completeness: [Percentage]%

Missing Data Summary:

Column	Missing Count	Missing %	Assessment
[Column 1]	[Count]	[%]	[High/Medium/Low]
[Column 2]	[Count]	[%]	[High/Medium/Low]

Missing Data Pattern: [Description of patterns, if any]

Visualization:

2.2 Duplicates

• Duplicate Rows: [Count] ([Percentage]%)
• Action Required: [Yes/No - describe if needed]

2.3 Data Quality Issues

[List any identified issues]

• Issue 1: [Description]
• Issue 2: [Description]
• Issue 3: [Description]

---

3. Univariate Analysis

3.1 Numeric Variables

[For each key numeric variable:]

[Variable Name]

Summary Statistics:

• Mean: [Value]
• Median: [Value]
• Std Dev: [Value]
• Min: [Value]
• Max: [Value]
• Range: [Value]
• IQR: [Value]

Distribution Characteristics:

• Skewness: [Value] - [Interpretation]
• Kurtosis: [Value] - [Interpretation]
• Normality: [Normal/Not Normal based on tests]

Outliers:

• IQR Method: [Count] outliers ([Percentage]%)
• Z-Score Method: [Count] outliers ([Percentage]%)

Visualization:

Insights:

• [Key insight 1]
• [Key insight 2]

---

3.2 Categorical Variables

[For each key categorical variable:]

[Variable Name]

Summary:

• Unique Values: [Count]
• Most Common: [Value] ([Percentage]%)
• Least Common: [Value] ([Percentage]%)
• Balance: [Balanced/Imbalanced]

Top Categories:

Category	Count	Percentage
[Cat 1]	[Count]	[%]
[Cat 2]	[Count]	[%]
[Cat 3]	[Count]	[%]

Visualization:

Insights:

• [Key insight 1]
• [Key insight 2]

---

3.3 Temporal Variables

[If datetime columns exist:]

[Variable Name]

Time Range: [Start Date] to [End Date] Duration: [Time span] Temporal Coverage: [Complete/Gaps identified]

Temporal Patterns:

• Trend: [Increasing/Decreasing/Stable]
• Seasonality: [Yes/No - describe if present]
• Gaps: [List any gaps in timeline]

Visualization:

Insights:

• [Key insight 1]
• [Key insight 2]

---

4. Bivariate Analysis

4.1 Correlation Analysis

Overall Correlation Structure:

• Strong Positive Correlations: [Count]
• Strong Negative Correlations: [Count]
• Weak/No Correlations: [Count]

Correlation Matrix:

Notable Correlations:

Variable 1	Variable 2	Pearson r	Spearman ρ	Strength	Interpretation
[Var 1]	[Var 2]	[Value]	[Value]	[Strong/Moderate/Weak]	[Interpretation]
[Var 1]	[Var 3]	[Value]	[Value]	[Strong/Moderate/Weak]	[Interpretation]

Insights:

• [Key insight about correlations]
• [Potential multicollinearity issues]
• [Feature engineering opportunities]

---

4.2 Key Relationships

[For important variable pairs:]

[Variable 1] vs [Variable 2]

Relationship Type: [Linear/Non-linear/None] Correlation: [Value] Statistical Test: [Test name, p-value]

Visualization:

Insights:

• [Description of relationship]
• [Implications]

---

5. Multivariate Analysis

5.1 Scatter Matrix

Observations:

• [Pattern 1]
• [Pattern 2]
• [Pattern 3]

5.2 Clustering Patterns

[If clustering analysis performed:]

Method: [Method used] Number of Clusters: [Count]

Cluster Characteristics:

• Cluster 1: [Description]
• Cluster 2: [Description]

Visualization: [Link to visualization]

---

6. Outlier Analysis

6.1 Outlier Summary

Overall Outlier Rate: [Percentage]%

Variables with High Outlier Rates:

Variable	Outlier Count	Outlier %	Method	Action
[Var 1]	[Count]	[%]	[IQR/Z-score]	[Keep/Investigate/Remove]
[Var 2]	[Count]	[%]	[IQR/Z-score]	[Keep/Investigate/Remove]

Visualization:

6.2 Outlier Investigation

[For significant outliers:]

[Variable Name]

Outlier Characteristics:

• [Description of outliers]
• [Potential causes]
• [Validity assessment]

Recommendation: [Keep/Remove/Transform/Investigate further]

---

7. Key Insights and Findings

7.1 Data Quality Insights

1. [Insight 1]: [Description and implication]
2. [Insight 2]: [Description and implication]
3. [Insight 3]: [Description and implication]

7.2 Statistical Insights

1. [Insight 1]: [Description and implication]
2. [Insight 2]: [Description and implication]
3. [Insight 3]: [Description and implication]

7.3 Business/Research Insights

1. [Insight 1]: [Description and implication]
2. [Insight 2]: [Description and implication]
3. [Insight 3]: [Description and implication]

7.4 Unexpected Findings

1. [Finding 1]: [Description and significance]
2. [Finding 2]: [Description and significance]

---

8. Recommendations

8.1 Data Quality Actions

• [Action 1]: [Description and priority]
• [Action 2]: [Description and priority]
• [Action 3]: [Description and priority]

8.2 Analysis Next Steps

1. [Step 1]: [Description and rationale]
2. [Step 2]: [Description and rationale]
3. [Step 3]: [Description and rationale]

8.3 Feature Engineering Opportunities

• [Opportunity 1]: [Description]
• [Opportunity 2]: [Description]
• [Opportunity 3]: [Description]

8.4 Modeling Considerations

• [Consideration 1]: [Description]
• [Consideration 2]: [Description]
• [Consideration 3]: [Description]

---

9. Limitations and Caveats

9.1 Data Limitations

• [Limitation 1]
• [Limitation 2]
• [Limitation 3]

9.2 Analysis Limitations

• [Limitation 1]
• [Limitation 2]
• [Limitation 3]

9.3 Assumptions Made

• [Assumption 1]
• [Assumption 2]
• [Assumption 3]

---

10. Appendices

Appendix A: Technical Details

Software Environment:

• Python: [Version]
• Key Libraries: pandas ([Version]), numpy ([Version]), scipy ([Version]), matplotlib ([Version])

Analysis Scripts: [Link to repository or location]

Appendix B: Variable Dictionary

Variable Name	Type	Description	Unit	Valid Range	Missing %
[Var 1]	[Type]	[Description]	[Unit]	[Range]	[%]
[Var 2]	[Type]	[Description]	[Unit]	[Range]	[%]

Appendix C: Statistical Test Results

[Detailed statistical test outputs]

Normality Tests:

Variable	Test	Statistic	p-value	Result
[Var 1]	Shapiro-Wilk	[Value]	[Value]	[Normal/Non-normal]

Correlation Tests:

Var 1	Var 2	Coefficient	p-value	Significance
[Var 1]	[Var 2]	[Value]	[Value]	[Yes/No]

Appendix D: Full Visualization Gallery

[Links to all generated visualizations]

1. Visualization 1 description
2. Visualization 2 description
3. Visualization 3 description

---

Contact Information

Analyst: [Name] Email: [Email] Date: [Date] Version: [Version number]

---

Document History:

Version	Date	Changes	Author
1.0	[Date]	Initial analysis	[Name]