| name | data-scientist |
| description | Data science methodology and method-selection routing for quantitative research. Covers EDA, data validation, descriptive analysis, causal inference (IV, DiD, RD, synthetic control), clustering/PCA/UMAP, supervised ML, geospatial analysis, and visualization design. Contains the canonical method-to-library routing tree: statsmodels (OLS/GLM/time series), pyfixest (FE/DiD), linearmodels (RE/GMM/SUR), svy (complex surveys), scikit-learn (clustering/prediction ML), geopandas (spatial). For implementation syntax, load the routed tool-specific skill. |
| metadata | {"audience":"any-agent","domain":"research-methodology"} |
Data Scientist Skill
Rigorous data science methodology and mindset for Python research. Covers EDA, data validation, transformation verification, documentation standards, visualization design, descriptive analysis, statistical modeling, causal inference method selection (IV, DiD, RD, synthetic control), unsupervised analysis (clustering, PCA, UMAP), supervised ML methodology (prediction vs. inference, cross-validation, model interpretation, fairness), and geospatial analysis. Provides methodology decisions and analytical approach guidance. For implementation syntax, load the tool-specific skill (polars, statsmodels, plotnine, pyfixest, scikit-learn, geopandas, etc.). Use for any data analysis, exploration, transformation, or modeling task — especially when choosing methods, checking assumptions, or structuring an analysis.
Establishes a rigorous, methodical approach to data science work. This skill is about how to think and work, not specific tools. Load specialized skills (polars, plotnine, plotly, marimo, etc.) for tool-specific guidance.
Core Principles - NON-NEGOTIABLE
These five principles must guide ALL data science work. They are not optional.
Principle 1: Data Robustness First
ALWAYS check data before operating on it.
Before ANY analysis or transformation:
- Check shape, types, and memory usage
- Examine value distributions and ranges
- Identify and characterize missing values (count, percentage, pattern)
- Understand what uniquely identifies each row (granularity)
- Look for outliers and anomalies
Be VERBOSE about what you're checking and what you find. Never assume data is clean.
print(f"Shape: {df.shape}")
print(f"Columns: {df.columns.to_list()}")
print(f"Types:\n{df.dtypes}")
print(f"Null counts:\n{df.null_count()}")
print(f"Sample:\n{df.sample(5)}")
This principle applies only when you are conducting actual data work. Do NOT conduct net new analyses or data inspections when tasked with compiling past work (e.g., analytic notebook creation), or synthesizing prior analyses into a report (e.g., final report writing).
Principle 2: Documentation First
ALWAYS understand or create data documentation.
Before analysis:
- Seek data dictionaries, schemas, or documentation
- Understand where data comes from (provenance)
- Learn collection methods and their implications
- Identify known quality issues or caveats
- Clarify what each column means in business context
If documentation doesn't exist, CREATE IT as you learn about the data.
Principle 3: Verify Every Operation
NEVER assume a transformation worked correctly.
For EVERY data operation:
- Check row counts before and after
- Examine random samples of affected rows
- Validate that expected changes occurred
- Confirm no unintended side effects
- Document what you checked and what you found
print(f"Before: {len(df)} rows, columns: {df.columns.to_list()}")
sample_before = df.filter(pl.col("id").is_in([1, 42, 100]))
print(f"After: {len(result)} rows, columns: {result.columns.to_list()}")
sample_after = result.filter(pl.col("id").is_in([1, 42, 100]))
print(f"Sample comparison:\nBefore:\n{sample_before}\nAfter:\n{sample_after}")
Principle 4: Thorough Code Documentation (ENFORCED)
Write extensive comments explaining your reasoning. This is MANDATORY, not optional.
In research workflows, follow the Inline Audit Trail (IAT) standard (see agent_reference/INLINE_AUDIT_TRAIL.md). The IAT standard is enforced during QA review — scripts with sparse documentation receive WARNING findings.
Every code block should explain:
- WHAT you're trying to accomplish (the goal) → IAT Type 2: Intent Comment
- WHY you chose this approach (the reasoning) → IAT Type 3: Reasoning Comment
- WHAT assumptions you're making (the dependencies) → IAT Type 4: Assumption Comment
For tests and validations, explain:
- What behavior you're checking
- What would indicate success vs. failure
- Why this check matters
Principle 5: Focus on Research Questions
Balance rigor with usefulness.
Always consider:
- What question are we actually answering?
- What level of rigor does this decision require?
- Are there multiple valid approaches with different tradeoffs?
- Should I check with the user before proceeding?
CHECK IN with users when:
- Multiple valid methodologies exist
- Tradeoffs between precision and practicality arise
- Findings are surprising or counterintuitive
- Scope might need adjustment
Related Skills - When to Load
Core Workflow Skills (Load Together):
polars - Required for DataFrame operations; data-scientist provides methodology, polars provides syntaxmarimo - Required for creating validated notebooks; data-scientist defines validation patterns, marimo provides implementation
For Data Analysis Workflows:
In the research pipeline, data-scientist methodology is applied within the file-first execution pattern:
- Write script files FIRST (to
scripts/stage{N}_{type}/)
- Execute via Bash with automatic output capture wrapper script
- Validation results get automatically embedded in scripts as comments
- Marimo notebook assembles validated scripts for interactive review
Closely read agent_reference/SCRIPT_EXECUTION_REFERENCE.md for the mandatory file-first execution protocol covering complete code file writing, output capture, and file versioning rules.
Load for Specific Needs:
What task are you performing?
├─ Data visualization (any kind)
│ └─ Stage 8.2 — FIRST read visualization reference files below:
│ ├─ ./references/visualization-design.md (chart selection, encoding, emphasis)
│ └─ ./references/visualization-execution.md (color, labeling, accessibility, export)
│ THEN load the tool-specific skill:
│ ├─ Static plots → Load `plotnine` skill for grammar of graphics
│ └─ Interactive plots → Load `plotly` skill for interactive charts
├─ Descriptive analysis (subgroups, distributions, decompositions, trends)
│ └─ Stage 8.1 — FIRST read ./references/descriptive-analysis.md
│ THEN use polars for implementation (some methods may also need
│ `statsmodels` for weighted SEs/formal tests or `pyfixest` for descriptive FE regressions)
├─ Statistical modeling (regression, robustness checks)
│ └─ Stage 8.1 — FIRST read ./references/statistical-modeling.md
│ THEN load library skill:
│ ├─ Standard regression (OLS, logistic, GLM) → Load `statsmodels` skill
│ │ (Note: for OLS with clustered SEs, prefer `pyfixest` — native cluster support)
│ ├─ Fixed effects, IV with FE, or DiD → Load `pyfixest` skill
│ ├─ Random effects, between, first difference, Fama-MacBeth → Load `linearmodels` skill
│ ├─ IV without FE (LIML, GMM) → Load `linearmodels` skill
│ ├─ System estimation (SUR, 3SLS) → Load `linearmodels` skill
│ └─ Spatial regression (spatial lag, spatial error, GWR) → Load `geopandas` skill
│ (spatial regression uses PySAL/spreg via geopandas; also read geospatial refs)
├─ Supervised ML (prediction, classification, risk scoring)
│ ├─ Methodology (when to use ML, how to validate, interpret, report)
│ │ → Read supervised-ml.md
│ ├─ Implementation (algorithms, syntax, evaluation)
│ │ → Load `scikit-learn` skill
│ ├─ Model interpretation (SHAP, feature importance)
│ │ → Read supervised-ml.md "Interpreting ML Models" + scikit-learn interpretation.md
│ └─ Fairness assessment
│ → Read supervised-ml.md "Fairness" + scikit-learn fairness.md
├─ Unsupervised analysis (clustering, dimensionality reduction, pattern discovery)
│ └─ Stage 8.1 — FIRST read ./references/exploratory-unsupervised.md
│ THEN load `scikit-learn` skill for implementation
│ ├─ Clustering → clustering.md, evaluation-unsupervised.md
│ ├─ Dimensionality reduction → decomposition.md, manifold.md
│ └─ Index construction via PCA → also read ./references/descriptive-analysis.md
├─ Causal / quasi-experimental analysis
│ └─ FIRST read ./references/causal-inference.md
│ THEN load appropriate library skill (pyfixest for DiD/IV/FE, linearmodels for panel RE/IV-GMM, statsmodels for RD/matching)
├─ Complex survey data analysis (NHANES, ACS PUMS, CPS, ECLS-K, MEPS, etc.)
│ └─ FIRST read ./references/survey-analysis.md (methodology, pitfalls, weight selection)
│ THEN load `svy` skill for implementation syntax
│ ├─ Survey-weighted descriptive statistics → svy estimation.md
│ ├─ Survey-weighted regression (OLS, logistic, Poisson) → svy regression.md
│ ├─ Survey design setup / replicate weights → svy design-weights.md
│ └─ Advanced models not in svy (ordinal, survival, IV) → rpy2 + R survey package
│ (see svy skill "rpy2 Bridge" section)
├─ Communicating to non-technical audiences
│ └─ Load `science-communication` skill
├─ Geospatial / spatial analysis (any kind)
│ └─ FIRST read methodology reference files:
│ ├─ ./references/geospatial-analysis.md (spatial thinking, methods, interpretation)
│ └─ ./references/geospatial-operations.md (joins, weights, interpolation, operations)
│ THEN load `geopandas` skill for implementation syntax
├─ Not currently covered by DAAF skills:
│ ├─ Bayesian modeling (PyMC, bambi) → escalate to orchestrator
│ ├─ Survival / time-to-event analysis → escalate to orchestrator
│ └─ Deep learning (PyTorch, TensorFlow) → escalate to orchestrator
Visualization loading order matters: The reference files provide design principles (what chart to use, how to direct attention, how to handle color accessibly). The tool skills provide syntax (how to code it). Read the design guidance first so implementation choices are principled, not ad-hoc.
For Domain-Specific Analysis (e.g., CCD Education Data):
- Load relevant
*-data-source-* skill first to understand domain-specific data caveats
- Then apply data-scientist methodology with that context
Prerequisite Knowledge:
This skill assumes familiarity with:
- Python programming basics
- DataFrame concepts (rows, columns, filtering)
- Basic statistical concepts (mean, distribution, correlation)
Important: This skill provides the METHODOLOGY. The specialized skills provide TOOL KNOWLEDGE. Use both together.
Reference File Structure
| File | Purpose | When to Read |
|---|
eda-checklist.md | Detailed EDA procedures and validation checks | Starting analysis on new data |
data-documentation.md | Understanding and creating data documentation | Working with unfamiliar data |
transformation-validation.md | Validating data operations | Before/after any transformation |
code-documentation.md | Writing thorough comments and docs | Writing any analysis code |
research-questions.md | Framing questions, stakeholder communication | Scoping work, presenting findings |
visualization-design.md | Chart selection, visual encoding, emphasis, integrity | Before creating any visualization |
visualization-execution.md | Color palettes, accessibility, labeling, typography, export | When producing figures |
descriptive-analysis.md | Summary statistics, subgroups, distributions, decompositions, weighting, inequality, correlation, missing data | Stage 8 analysis when the research contribution is descriptive |
statistical-modeling.md | Model selection, assumption checking, robust inference, coefficient interpretation, robustness checks | Stage 8.1 analysis involving regression, modeling, or hypothesis testing |
causal-inference.md | Causal identification, DAGs, RCTs, IV, RD, DiD, synthetic control, matching | Stage 8.1 analysis requiring causal claims |
survey-analysis.md | Complex survey methodology: design anatomy, weight selection, variance estimation, domain estimation, plausible values, survey-weighted regression, federal survey reference table, pitfalls checklist | Any task involving data from a complex probability survey (NHANES, ACS PUMS, CPS, ECLS-K, HSLS, MEPS, NAEP, etc.) |
geospatial-analysis.md | Spatial thinking, MAUP, CRS, methods decision guide, autocorrelation, regression | Any task involving geographic/spatial data |
geospatial-operations.md | Spatial joins, weights, LISA interpretation, interpolation, zonal statistics, geometry validity | Planning or executing spatial operations (joins, overlays, weights, interpolation, zonal statistics), or interpreting spatial statistics results (Moran's I, LISA) |
exploratory-unsupervised.md | Cluster analysis, dimension reduction (PCA), Gaussian mixture models, nonlinear embeddings (t-SNE, UMAP), cluster validation, classify-analyze problem | Stage 8 tasks involving unsupervised methods, typology construction, or pattern discovery |
supervised-ml.md | Supervised ML methodology: prediction vs. inference (Shmueli 2010), bias-variance tradeoff, cross-validation for structured data (grouped, temporal, spatial), model selection, classification and ML regression methodology, ensemble methods, interpretation caveats (feature importance is not causation), algorithmic fairness and equity (impossibility theorems), deep learning orientation, reporting standards | Stage 8 tasks involving classification, prediction, risk scoring, ML-based variable selection, or any task where the goal is predicting outcomes rather than estimating causal parameters |
Validation Tracking
For multi-step transformations, track validation state with a simple dict:
validation_log = {}
validation_log["Filter to high schools"] = {
"pre_rows": pre_rows,
"post_rows": result.shape[0],
"status": "PASSED" if result.shape[0] > 0 else "FAILED",
}
for step, info in validation_log.items():
print(f" [{info['status']}] {step}: {info['pre_rows']:,} → {info['post_rows']:,}")
This is inline code, not a separate module. Never create a validation.py or import a validation class.
Quick Decision Trees
"I'm starting a new analysis"
Starting new analysis?
├─ Do I have data documentation?
│ ├─ Yes → Read it thoroughly first
│ │ → ./references/data-documentation.md
│ └─ No → Create it as you explore
│ → ./references/data-documentation.md
├─ Have I profiled the data?
│ └─ No → Run full EDA checklist
│ → ./references/eda-checklist.md
├─ Do I understand the research question?
│ └─ Unclear → Clarify with stakeholder
│ → ./references/research-questions.md
└─ Ready to analyze → Document as you go
→ ./references/code-documentation.md
"I have unfamiliar data"
Unfamiliar data?
├─ Step 1: Basic inspection
│ └─ Shape, types, head/tail/sample
│ → ./references/eda-checklist.md
│ IF data contains geometry column, lat/lon, GEOID, or FIPS codes:
│ → also read ./references/geospatial-analysis.md
├─ Step 2: Understand granularity
│ └─ What does each row represent?
│ └─ What columns uniquely identify a row?
├─ Step 3: Check data quality
│ └─ Missing values, duplicates, outliers
│ → ./references/eda-checklist.md
├─ Step 4: Seek documentation
│ └─ Data dictionary, schema, provenance
│ → ./references/data-documentation.md
└─ Step 5: Document findings
└─ Create documentation if none exists
"I need to transform data"
Transforming data?
├─ Before transformation:
│ ├─ Document current state (shape, sample)
│ ├─ Identify what SHOULD change
│ └─ Identify what should NOT change
│ → ./references/transformation-validation.md
├─ After transformation:
│ ├─ Verify shape changes are expected
│ ├─ Check random sample of results
│ ├─ Validate invariants (sums, counts)
│ └─ Document what you verified
│ → ./references/transformation-validation.md
└─ For joins specifically:
├─ Check for unintended row duplication
├─ Check for unintended data loss
└─ Validate join keys match expectations
"I need to communicate findings"
Communicating findings?
├─ Have I documented limitations?
│ └─ No → List caveats and assumptions
│ → ./references/research-questions.md
├─ Am I making causal claims?
│ └─ Yes → Ensure justified; prefer correlational language
│ → ./references/research-questions.md
├─ Is uncertainty quantified?
│ └─ No → Add confidence intervals or ranges
└─ Have I checked in with stakeholder?
└─ No → Validate findings align with expectations
"I need to create a visualization"
What kind of visualization task?
├─ Choosing what chart to make
│ └─ → ./references/visualization-design.md
├─ Directing attention / emphasis strategy
│ └─ → ./references/visualization-design.md
├─ Selecting colors or ensuring accessibility
│ └─ → ./references/visualization-execution.md
├─ Labeling, titling, or annotating
│ └─ → ./references/visualization-execution.md
├─ Making it publication-ready (export, DPI, fonts)
│ └─ → ./references/visualization-execution.md
├─ Ensuring project consistency (theme, palette)
│ └─ → ./references/visualization-execution.md
├─ Mapping geographic data (choropleth, dot density, proportional symbols)
│ └─ → ./references/geospatial-analysis.md (map design, classification)
│ THEN → ./references/visualization-execution.md (color, accessibility)
│ THEN load `geopandas` skill for implementation
└─ Tool-specific syntax (geoms, traces, themes)
├─ Static plots → Load `plotnine` skill
└─ Interactive plots → Load `plotly` skill
"I need to analyze patterns in this data"
Am I trying to describe or explain?
├─ Describe (characterize what exists)
│ └─ → ./references/descriptive-analysis.md
│ ├─ Subgroup comparisons → Stratification section
│ ├─ Distribution shape → Distributional analysis section
│ ├─ Trends over time → Trend analysis section
│ ├─ Gaps between groups → Decomposition methods section
│ ├─ Composite measure → Index construction section
│ ├─ Inequality → Inequality measurement section
│ └─ Weighted estimates → Weighted analysis section
├─ Model (regression, hypothesis testing)
│ └─ → ./references/statistical-modeling.md
│ ├─ Choose model by outcome type → Model selection framework
│ ├─ Check assumptions → Assumption checking protocol
│ ├─ Choose standard errors → SE type decision table
│ └─ Interpret coefficients → Interpretation guide
└─ Explain causally
└─ → ./references/causal-inference.md
└─ Select method per Method Selection Guide
"I need to establish a causal relationship"
What variation identifies the causal effect?
├─ Random assignment → RCT analysis
│ → ./references/causal-inference.md
├─ I can control for all confounders → Regression / matching
│ → ./references/causal-inference.md
├─ There's a valid instrument → IV / 2SLS
│ → ./references/causal-inference.md
├─ There's a score cutoff → Regression discontinuity
│ → ./references/causal-inference.md
├─ Policy changed for some groups → Difference-in-differences
│ → ./references/causal-inference.md
├─ Few treated units, long pre-period → Synthetic control
│ → ./references/causal-inference.md
└─ Not sure → Start with a DAG
→ ./references/causal-inference.md
"I need to discover structure or groupings in data"
What kind of structure am I looking for?
├─ Groups of similar observations (typology, classification)
│ ├─ Know number of groups → K-means or GMM (scikit-learn)
│ ├─ Don't know number → Try multiple k + validation (scikit-learn)
│ ├─ Arbitrary shapes / noise → DBSCAN or HDBSCAN (scikit-learn)
│ └─ Uncertain → Read exploratory-unsupervised.md "Algorithm Selection" table
├─ Reducing a large variable set
│ ├─ Linear reduction → PCA (scikit-learn)
│ └─ Visualizing structure → t-SNE or UMAP (scikit-learn / umap-learn)
│ └─ CAUTION: visualization only — not for analysis
│ → ./references/exploratory-unsupervised.md
├─ Using unsupervised results in subsequent regression
│ └─ Read exploratory-unsupervised.md "The Classify-Analyze Problem"
└─ Predicting outcomes with ML methods
└─ Load scikit-learn skill → classification.md or regression-ml.md
"I'm working with geographic/spatial data"
Geospatial analysis task?
├─ Understanding spatial concepts, methods, or statistical theory
│ └─ (MAUP, CRS, autocorrelation, spatial regression)
│ → ./references/geospatial-analysis.md
├─ Choosing a spatial analysis method
│ └─ → ./references/geospatial-analysis.md (decision guide)
├─ Performing spatial joins, overlays, or weights construction
│ └─ → ./references/geospatial-operations.md
│ THEN load `geopandas` skill for syntax
├─ Interpreting Moran's I, LISA, or spatial regression results
│ └─ → ./references/geospatial-operations.md (interpretation sections)
├─ Interpolation or areal interpolation
│ └─ → ./references/geospatial-operations.md
├─ Extracting raster values into polygons (zonal statistics)
│ └─ → ./references/geospatial-operations.md
├─ Debugging spatial operation failures or geometry errors
│ └─ → ./references/geospatial-operations.md (geometry validity)
├─ Making maps or choosing classification schemes
│ └─ → ./references/geospatial-analysis.md (map design)
│ THEN → ./references/visualization-execution.md (color, accessibility)
└─ Implementation syntax (geopandas, PySAL, rasterio)
└─ Load `geopandas` skill
"I need to predict an outcome or classify observations"
Am I predicting or explaining?
├─ Explaining (estimating a causal or associational parameter)
│ └─ Use pyfixest or statsmodels — see statistical-modeling.md
├─ Predicting (minimizing prediction error on new data)
│ ├─ Read supervised-ml.md for methodology
│ ├─ Load scikit-learn skill for implementation
│ ├─ Tabular data → Start with logistic regression baseline,
│ │ then try HistGradientBoosting or LightGBM
│ ├─ Text data → See supervised-ml.md "When Deep Learning Methods Are Appropriate"
│ ├─ Need to explain predictions → scikit-learn interpretation.md (SHAP)
│ └─ Need fairness assessment → scikit-learn fairness.md
└─ Not sure → Read supervised-ml.md "Prediction vs. Inference"
Essential Workflows
New Data Workflow
When you receive new data, ALWAYS follow this sequence:
-
Load and inspect (do not transform yet)
df = pl.read_csv("data.csv")
print(f"Shape: {df.shape}")
print(f"Columns: {df.columns}")
print(f"Types:\n{df.dtypes}")
print(f"First 5 rows:\n{df.head()}")
print(f"Last 5 rows:\n{df.tail()}")
print(f"Random sample:\n{df.sample(5)}")
-
Check data quality
print(f"Null counts:\n{df.null_count()}")
print(f"Null percentages:\n{df.null_count() / len(df) * 100}")
print(f"Duplicate rows: {len(df) - len(df.unique())}")
for col in df.columns:
print(f"{col}: {df[col].n_unique()} unique values")
-
Understand distributions
print(df.describe())
for col in df.select(pl.col(pl.String)).columns:
print(f"\n{col}:\n{df[col].value_counts().head(10)}")
-
Identify granularity
candidate_keys = ["id", "user_id", ["user_id", "date"]]
for key in candidate_keys:
cols = [key] if isinstance(key, str) else key
unique_count = df.select(cols).n_unique()
print(f"{cols}: {unique_count} unique vs {len(df)} rows")
-
Document findings before proceeding
Transformation Workflow
For ANY data transformation:
-
Document pre-state
pre_shape = df.shape
pre_columns = df.columns.copy()
pre_sample = df.sample(10, seed=42)
pre_sum = df.select(pl.col("amount").sum()).item()
-
Perform transformation with comments
result = (
df
.filter(pl.col("last_login") > cutoff_date)
.group_by("user_id")
.agg(pl.col("amount").sum().alias("total_spend"))
)
-
Validate post-state
post_shape = result.shape
print(f"Shape: {pre_shape} -> {post_shape}")
sample_ids = pre_sample["user_id"].to_list()[:3]
print(f"Sample before:\n{pre_sample.filter(pl.col('user_id').is_in(sample_ids))}")
print(f"Sample after:\n{result.filter(pl.col('user_id').is_in(sample_ids))}")
-
Document what you verified
Analysis Workflow
From question to answer:
-
Clarify the question
- What decision will this inform?
- What would a "good" answer look like?
- What level of rigor is required?
-
Assess data fitness
- Does the data contain what we need?
- What are the limitations?
- Are there gaps or quality issues?
-
Choose methodology
- What approaches are valid?
- What are the tradeoffs?
- CHECK WITH USER if multiple valid options
-
Execute with verification
- Follow transformation workflow
- Document each step thoroughly
-
Validate findings
- Do results make sense?
- Cross-check with known facts
- Identify limitations and caveats
-
Communicate with appropriate uncertainty
Quick Checklists
Initial Data Inspection Checklist
Pre-Transformation Checklist
Post-Transformation Checklist
Documentation Checklist
Marimo Integration
When working in marimo notebooks:
Cell Organization Pattern
import marimo as mo
import polars as pl
df = pl.read_csv("data.csv")
mo.md("## Data Inspection")
mo.md("## Data Quality")
Using Reactivity for Validation
validation_column = mo.ui.dropdown(
options=df.columns,
label="Select column to validate"
)
mo.md(f"""
### Validation for `{validation_column.value}`
- Null count: {df[validation_column.value].null_count()}
- Unique values: {df[validation_column.value].n_unique()}
- Sample values: {df[validation_column.value].head(5).to_list()}
""")
Documentation Cells
Use markdown cells liberally:
- Before code: explain what you're about to do and why
- After code: summarize findings and implications
- At decision points: document choices made
Topic Index
| Topic | Reference File |
|---|
| Initial data inspection | ./references/eda-checklist.md |
| Missing value analysis | ./references/eda-checklist.md |
| Distribution analysis | ./references/eda-checklist.md |
| Outlier detection | ./references/eda-checklist.md |
| Uniqueness and cardinality | ./references/eda-checklist.md |
| Correlation analysis | ./references/eda-checklist.md |
| Data dictionaries | ./references/data-documentation.md |
| Data provenance | ./references/data-documentation.md |
| Working with undocumented data | ./references/data-documentation.md |
| Questions to ask about data | ./references/data-documentation.md |
| Before/after validation | ./references/transformation-validation.md |
| Join validation | ./references/transformation-validation.md |
| Aggregation validation | ./references/transformation-validation.md |
| Schema validation (Pandera) | ./references/transformation-validation.md |
| Common transformation errors | ./references/transformation-validation.md |
| Comment philosophy | ./references/code-documentation.md |
| Docstring patterns | ./references/code-documentation.md |
| Notebook documentation | ./references/code-documentation.md |
| Test documentation | ./references/code-documentation.md |
| Research question formulation | ./references/research-questions.md |
| Rigor vs. practicality | ./references/research-questions.md |
| Stakeholder check-ins | ./references/research-questions.md |
| Communicating uncertainty | ./references/research-questions.md |
| Causal vs. correlational claims | ./references/research-questions.md |
| Chart selection by relationship | ./references/visualization-design.md |
| Visual encoding hierarchy | ./references/visualization-design.md |
| Exploratory vs. explanatory viz | ./references/visualization-design.md |
| Small multiples | ./references/visualization-design.md |
| Common visualization pitfalls | ./references/visualization-design.md |
| Graphical integrity | ./references/visualization-design.md |
| Color palette selection | ./references/visualization-execution.md |
| Colorblind accessibility | ./references/visualization-execution.md |
| Direct labeling vs. legends | ./references/visualization-execution.md |
| Chart titles and annotation | ./references/visualization-execution.md |
| Typography and layout | ./references/visualization-execution.md |
| Export standards (DPI, format) | ./references/visualization-execution.md |
| Project visual consistency | ./references/visualization-execution.md |
| Figure captions | ./references/visualization-execution.md |
| Summary statistics selection | ./references/descriptive-analysis.md |
| Subgroup analysis and stratification | ./references/descriptive-analysis.md |
| Distributional analysis (KDE, quantiles) | ./references/descriptive-analysis.md |
| Cross-tabulations and rates | ./references/descriptive-analysis.md |
| Trend analysis and time series description | ./references/descriptive-analysis.md |
| Decomposition methods (Oaxaca-Blinder, Kitagawa, Gelbach, shift-share) | ./references/descriptive-analysis.md |
| Index construction and composite measures | ./references/descriptive-analysis.md |
| Weighted analysis (survey, population, IPW) | ./references/descriptive-analysis.md |
| Inequality measurement (Gini, Theil, percentile ratios) | ./references/descriptive-analysis.md |
| Correlation and association methodology | ./references/descriptive-analysis.md |
| Missing data characterization (MCAR/MAR/MNAR) | ./references/descriptive-analysis.md |
| Sample description and representativeness | ./references/descriptive-analysis.md |
| Model selection by outcome type | ./references/statistical-modeling.md |
| Regression as CEF approximation | ./references/statistical-modeling.md |
| OLS assumption checking and diagnostics | ./references/statistical-modeling.md |
| Standard error type selection (robust, clustered, HAC) | ./references/statistical-modeling.md |
| Coefficient interpretation by model type | ./references/statistical-modeling.md |
| Marginal effects (AME vs MEM) | ./references/statistical-modeling.md |
| Robustness checks and sensitivity analysis | ./references/statistical-modeling.md |
| Causal inference method selection | ./references/causal-inference.md |
| DAGs and causal reasoning | ./references/causal-inference.md |
| Potential outcomes framework | ./references/causal-inference.md |
| Randomized controlled trials (RCTs) | ./references/causal-inference.md |
| Instrumental variables (IV / 2SLS) | ./references/causal-inference.md |
| Regression discontinuity (RD) | ./references/causal-inference.md |
| Difference-in-differences (DiD, modern methods) | ./references/causal-inference.md |
| Synthetic control methods | ./references/causal-inference.md |
| Matching and propensity scores | ./references/causal-inference.md |
| Complex survey design (strata, PSUs, clustering) | ./references/survey-analysis.md |
| Survey weight selection and types | ./references/survey-analysis.md |
| Variance estimation (Taylor linearization, BRR, jackknife) | ./references/survey-analysis.md |
| Design effects (DEFF) and effective sample size | ./references/survey-analysis.md |
| Domain / subpopulation estimation (never subset rule) | ./references/survey-analysis.md |
| Plausible values (NAEP, PISA, TIMSS) | ./references/survey-analysis.md |
| Survey-weighted regression methodology | ./references/survey-analysis.md |
| Replicate weights (BRR, jackknife, bootstrap) | ./references/survey-analysis.md |
| Federal survey data sources reference table | ./references/survey-analysis.md |
| Survey analysis pitfalls checklist | ./references/survey-analysis.md |
| Finite population corrections (FPC) | ./references/survey-analysis.md |
| When to weight regressions (Solon-Haider-Wooldridge) | ./references/survey-analysis.md |
| Spatial thinking and Tobler's Law | ./references/geospatial-analysis.md |
| MAUP (Modifiable Areal Unit Problem) | ./references/geospatial-analysis.md |
| Ecological fallacy | ./references/geospatial-analysis.md |
| Coordinate reference systems (CRS) | ./references/geospatial-analysis.md |
| Projections (choosing, setting, transforming) | ./references/geospatial-analysis.md |
| Spatial data types (vector, raster) | ./references/geospatial-analysis.md |
| Spatial method selection | ./references/geospatial-analysis.md |
| Spatial autocorrelation (Moran's I, Geary's C) | ./references/geospatial-analysis.md |
| LISA and local autocorrelation | ./references/geospatial-analysis.md |
| Point pattern analysis | ./references/geospatial-analysis.md |
| Kernel density estimation (KDE) | ./references/geospatial-analysis.md |
| Geometry validity and repair | ./references/geospatial-operations.md |
| Spatial regression methods | ./references/geospatial-analysis.md |
| Raster operations taxonomy | ./references/geospatial-analysis.md |
| Map design and classification schemes | ./references/geospatial-analysis.md |
| Spatial joins (strategies, pitfalls) | ./references/geospatial-operations.md |
| Overlay operations | ./references/geospatial-operations.md |
| Spatial weights construction | ./references/geospatial-operations.md |
| Interpreting Moran's I and LISA | ./references/geospatial-operations.md |
| Interpolation (IDW, kriging) | ./references/geospatial-operations.md |
| Zonal statistics | ./references/geospatial-operations.md |
| Areal interpolation (boundary mismatch) | ./references/geospatial-operations.md |
| Spatial correlograms (residual diagnostics) | ./references/geospatial-operations.md |
| Spatial regression reporting standards | ./references/geospatial-operations.md |
| Islands / disconnected observations in weights | ./references/geospatial-operations.md |
| Endogeneity in spatial lag models | ./references/geospatial-operations.md |
| LM tests (lag vs. error model selection) | ./references/geospatial-analysis.md |
| Coordinate precision and significant digits | ./references/geospatial-analysis.md |
| Choropleth normalization (rates not counts) | ./references/geospatial-analysis.md |
| GWR (Geographically Weighted Regression) | ./references/geospatial-analysis.md |
| Cluster analysis (K-means, hierarchical, DBSCAN, GMM) | ./references/exploratory-unsupervised.md |
| Dimension reduction (PCA as exploration) | ./references/exploratory-unsupervised.md |
| Nonlinear embeddings (t-SNE, UMAP) | ./references/exploratory-unsupervised.md |
| Gaussian mixture models | ./references/exploratory-unsupervised.md |
| Cluster validation (silhouette, stability, ARI) | ./references/exploratory-unsupervised.md |
| Classify-analyze problem | ./references/exploratory-unsupervised.md |
| Typology construction | ./references/exploratory-unsupervised.md |
| Prediction vs. inference distinction | ./references/supervised-ml.md |
| Bias-variance tradeoff | ./references/supervised-ml.md |
| Cross-validation for social science data (grouped, temporal, spatial) | ./references/supervised-ml.md |
| Model selection (supervised ML) | ./references/supervised-ml.md |
| Classification methodology | ./references/supervised-ml.md |
| ML regression (prediction-focused) | ./references/supervised-ml.md |
| Ensemble methods (random forests, boosting) | ./references/supervised-ml.md |
| Feature importance interpretation (SHAP, permutation importance) | ./references/supervised-ml.md |
| Algorithmic fairness and bias | ./references/supervised-ml.md |
| Deep learning orientation | ./references/supervised-ml.md |
| Reporting standards for ML | ./references/supervised-ml.md |
Citation Responsibility
When analytical methods from this skill's reference materials are used in DAAF analyses,
the research-executor includes relevant citations in its structured output. Focus on
primary citations — the papers and tools that directly enable the analytical results,
not every background reference mentioned in the reference files.
Each citation must include a brief rationale explaining why it is included, so the
researcher can make informed decisions about what to keep in the final report.
For the master citation index and inclusion thresholds, consult
agent_reference/CITATION_REFERENCE.md.
