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name: data-analyst description: Python 생태계(Jupyter, Pandas, Scikit-learn)를 활용하여 데이터에서 심층적인 인사이트를 도출하는 전문 분석 워크플로우입니다.

Data Analyst Workflow

Python 생태계(Jupyter, Pandas, Scikit-learn)를 활용하여 데이터에서 심층적인 인사이트를 도출하는 전문 분석 워크플로우입니다. OSEMN 방법론과 SKILL.md의 표준을 따릅니다.

0단계: 기본 원칙 (Core Principles)

1. Explanation First (선 설명 후 코드): 코드를 작성하기 전에 무엇을, 왜 분석하는지 Markdown으로 서술합니다.
2. Interpretation Mandatory (결과 해석 필수): 모든 코드 셀(Code Cell)의 출력 하단에는 반드시 Markdown 셀을 추가하여 통계적 수치나 그래프가 의미하는 바를 한글로 상세히 해석합니다.
   • Bad: 그래프만 덩그러니 있음.
   • Good: "히스토그램의 꼬리가 우측으로 긴 것으로 보아(Skewed > 1), 로그 변환이 필요함을 시사합니다."

1단계: 분석 환경 및 목표 정의 (Environment & Goal)

1. Context Loading:
   • this document를 로드하여 **'Core Principles'**를 확인합니다.
   • Methodology Screening (방법론 스크리닝):
     • SKILL.md의 **'Methodology Master List'**를 스캔합니다.
     • 현재 데이터셋의 특성(Type, Shape, Quality)과 분석 목표(Goal)를 대조하여 후보군(Candidates)을 3개 이상 필터링합니다.
     • [Check]: 각 후보군 선정 이유를 한 줄로 서술하였는가? (예: "결측치가 많으므로 Random Forest 채택")
2. Define Objective:
   • resources/plan-template.md를 사용하여 docs/plans/ANALYSIS_[주제].md를 작성합니다.
   • 단순 통계/정확도 외에 **비즈니스 임팩트(KPI)**를 성공 지표로 정의합니다.
3. Notebook Setup:
   • docs/notebooks/[Topic]_Analysis.ipynb를 생성하거나 엽니다.
   • AI Readability: 모든 통계량(describe, p-value)과 그래프는 노트북 출력 셀에 남겨두어야 합니다 (No Hiding).

3단계: 방법론 스크리닝 (Methodology Screening)

"The Right Tool for the Job"

데이터 특성에 맞춰 SKILL.md의 Methodology Master List에서 최적의 기법을 선정하고 Plan에 명시합니다.

• Metric Selection: 비즈니스 목표와 데이터 불균형 여부에 따라 평가지표를 선택합니다. (예: Imbalanced → F1/Recall/PRCURSOR, Regression → MAPE/RMSE)
• Preprocessing:
  • Outliers: 이상치가 많으면 RobustScaler 선택.
  • High Cardinality: 카테고리가 너무 많으면 Target Encoding 고려.
  • Imbalance: SMOTE 또는 Class Weights 적용 여부 결정.
• Modeling:
  • Baseline: Logistic/Linear Regression, Decision Tree.
  • Advanced: CatBoost(Categorical), XGBoost/LightGBM(Large Data), Isolation Forest(Anomaly) 등 선택.
• Validation стратегия:
  • Time Series: 반드시 Time Series Split 사용.
  • Imbalanced: Stratified K-Fold 필수.

2단계: 데이터 적재 및 품질 검증 (Obtain & Scrub)

Garbage In, Garbage Out을 방지하기 위한 데이터 신뢰성 확보 단계입니다.

1. Smart Loading:
   • Excel: 수식(Formula) 보존이 필요하면 openpyxl을 사용하고, 계산된 값(Calculated Values)을 하드코딩(Hardcoding)하지 않도록 주의합니다.
   • CSV: 인코딩(utf-8, cp949) 및 구분자(Delimiter) 자동 감지 기능을 활용합니다.
2. Data Profiling:
   • 기초 통계량(describe()), 결측치(isnull().sum()), 데이터 타입(dtypes)을 확인합니다.
   • [Markdown]: 확인된 결측치 현황과 데이터 타입을 텍스트로 요약합니다.
3. Deep Sanity Check:
   • SKILL.md의 'Logical Failures' 항목을 참조하여 정밀 점검합니다.
   • 불가능한 값 감지: 음수 나이(Age < 0), 미래의 날짜(Future Dates) 등 상식 밖의 데이터를 식별합니다.
4. Strategic Cleaning:
   • 결측치 및 이상치 처리 시, 앞서 스크리닝한 'Imputation Methodology' (Simple, KNN, Iterative)를 적용합니다.
   • 처리 기준(삭제 vs 대치)과 근거를 명시합니다.

3단계: 가설 주도적 탐색 (Hypothesis Driven EDA)

단순한 그래프 나열을 지양하고, 질문(Question) -> 시각화(Viz) -> 발견(Finding)의 흐름을 유지합니다.

1. Univariate Analysis: 개별 변수의 분포, 왜도(Skewness), 첨도(Kurtosis)를 확인합니다.
   • [Markdown]: 각 변수의 분포 특성과 이상치 유무를 서술합니다.
2. Bivariate Analysis: 상관관계 행렬(Correlation Matrix), 산점도(Scatter)를 통해 변수 간 관계를 시각화합니다.
   • [Markdown]: 변수 간의 선형/비선형 관계를 해석합니다.
3. Statistical Validation:
   • 시각적 발견을 통계적 검정(Normality Test, T-test, ANOVA)으로 검증하여 '우연'이 아님을 증명합니다.
   • [Markdown]: p-value를 근거로 귀무가설 기각 여부를 명시합니다.
4. Insight Logging:
   • 분석 중간에 발견된 중요한 사실이 비즈니스 KPI에 미칠 영향을 즉시 기록합니다.

4단계: 모델링 및 해석 (Model & Interpret) [Optional]

1. Baseline Model:
   • 가장 간단한 모델(Dummy, Linear)로 성능 하한선(Baseline)을 설정하고 이를 넘어서는지 확인합니다.
2. Feature Engineering:
   • SKILL.md 리스트의 기법(Encoding, Scaling, PCA)을 적용합니다.

3. 4.3 Advanced Modeling & Tuning

• Model Selection: 선정된 고성능 모델(XGBoost, CatBoost, RF 등)을 학습합니다.
• Hyperparameter Tuning: 단순히 Grid Search를 넘어 Optuna 등을 활용해 효율적으로 최적화합니다.
• Rigorous Validation: Cross Validation 점수와 Hold-out Test 점수의 차이를 확인하여 오버피팅을 감지합니다. (Gap > 5% 시 경고)
  • Clustering: K-Means 등을 사용할 경우 Elbow Method나 Silhouette Score로 최적의 군집 수(K)를 결정합니다.

4. Rigorous Validation:
   • Stratified K-Fold를 사용하여 과적합을 방지하고 일반화 성능을 평가합니다.
5. Interpretation & Error Analysis:
   • Feature Importance: SHAP, Permutation Importance 등을 활용해 모델의 판단 근거를 설명합니다.
   • Error Analysis: 'Top 10 Worst Errors' (모델이 가장 크게 틀린 샘플 10개)를 수동으로 검사하여 원인을 파악합니다.
6. Reporting:
   • 기술적 수치(Accuracy, MSE)를 비즈니스 언어(예상 수익, 리스크 감소)로 번역하여 보고합니다.

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Standards & Rules

Data Analyst Standards (OSEMN)

Purpose

To transform raw data into actionable business insights using a rigorous, hypothesis-driven approach.

Core Principles (Core Philosophy)

"No Hiding" AI Readability: All statistical outputs (describe, corr, p-value) and graphs must remain in the notebook output. This ensures tools like NotebookLM can contextually understand the analysis.

Quality Standards (Tier 1 Best Practices)

1. Data Integrity (Obtain & Scrub)

"Garbage In, Garbage Out"

• Data Source Verification: Check file extension, size, and metadata.
• Data Quality Check:
  • Missing Values: Identify mechanism (MCAR, MAR, MNAR) before imputing.
  • Logical Failures: Check for impossible values (e.g., age < 0, future dates), Data Leakage, and Overfitting indicators.
  • Data Types: Ensure numeric cols are not strings, etc.
• File Handling Standards:
  • Excel (.xlsx): Preserve existing formatting/formulas. Use openpyxl for editing, pandas for reading. Zero Hardcoding of calculated values.
  • CSV (.csv): Detect delimiter automatically (csv.Sniffer). Handle encoding errors (utf-8 vs cp949) explicitly.

2. Hypothesis Driven EDA (Explore)

"Ask, Don't just Plot"

• Univariate Analysis: Distribution of each key variable (Histogram/Boxplot). Check for Skewness/Kurtosis.
• Bivariate Analysis: Correlation matrix, Scatter plots for relationships.
• Statistical Validation:
  • Normality Test: Shapiro-Wilk or K-S test.
  • Significance: T-test/ANOVA for group differences.
• Insight Logging: Record the implication of every finding immediately.

3. Rigorous Modeling (Model)

"Trust but Verify"

• Baseline First: Compare complex models against a Dummy/Logistic Baseline.
• Feature Engineering: Scale numericals, Encode categoricals, Create interaction terms.
• Cross-Validation: Use Stratified K-Fold to prevent overfitting.
• Metric Selection: Optimize for business KPI (not just Accuracy).
• Methodology Screening: Consult the Methodology Master List (below) to select appropriate algorithms.

4. Interpretation (Interpret)

"Why did it predict that?"

• Feature Importance: SHAP values or Permutation Importance.
• Error Analysis: Manually inspect the "Top 10 Worst Errors".
• Actionable Conclusion: Translate stats into business recommendations.

Checklist (Quality Gate)

Before finalizing:

• Reproducibility: Can the notebook run from top to bottom without error?
• Storytelling: Does the notebook flow like a narrative?
• Visuals: Are all graphs labeled (Title, Axis, Legend)?

Appendix: Methodology Master List (Reference)

Scan these tables to select the most appropriate methodology for your data and goal.

1. Preprocessing & Data Cleaning

Methodology	Usage / Purpose	Data Constraints
Simple Imputation	Missing Value Imputation (Simple Replacement)	Mean/Median (Numeric), Mode (Categorical)
KNN Imputation	Missing Value Imputation (Similarity-based)	Mainly Numeric, useful when correlations exist
Iterative Imputation	Missing Value Imputation (Model-based)	High variable correlation, assumes MAR
One-Hot Encoding	Categorical to Numeric	Nominal data, Low Cardinality
Label Encoding	Categorical to Numeric	Ordinal data
Target Encoding	Categorical to Numeric	High Cardinality features, Risk of Overfitting
Standard Scaler	Scaling (Standardization)	Sensitive to outliers, assumes Gaussian distribution
MinMax Scaler	Scaling (Normalization)	Bounded data, distribution agnostic
Robust Scaler	Scaling (Robust to Outliers)	Data with many outliers (Uses Median/IQR)
SMOTE	Oversampling (Imbalanced Data)	Synthesize minority class samples (Training set ONLY)
PCA	Dimensionality Reduction, Multicollinearity Removal	Continuous variables, assumes linear relationships

2. Machine Learning Models

Methodology	Type	Usage / Purpose	Constraints / Notes
Linear Regression	Regression	Baseline for regression	Linear relationship assumption
Logistic Regression	Classification	Baseline for classification	Linear separation assumption, large sparse data OK
SVM / SVR	Class/Reg	High accuracy in high dimensional spaces	Computationally expensive (O(n^3)), Scale-sensitive
K-Nearest Neighbors	Class/Reg	Instance-based learning, Simple	Scale-sensitive, Small data
Random Forest	Ensemble	Robust Classification/Regression	Handles Mixed types, Robust to outliers/missing values
XGBoost / LightGBM	Ensemble	High Performance	Large datasets, handles missing values internally
CatBoost	Ensemble	Best for Categorical Features	Handles categories automatically, Slower training
Isolation Forest	Anomaly Detection	Outlier/Anomaly Detection	High dimensional data, efficiency
K-Means	Clustering	Partitioning into K clusters	Spherical Clusters, Sensitive to outliers, Scale-sensitive
DBSCAN	Clustering	Density-based clustering, Detects Outliers	Arbitrary shapes, Scale-sensitive, finding epsilon is hard
Hierarchical	Clustering	Dendrogram visualization	Computationally expensive for large data

3. Deep Learning Models

Methodology	Usage / Purpose	Data Constraints
CNN	Image/Pattern Recognition	Grid-like data (Images, etc.)
RNN / LSTM	Sequence/Time-Series Prediction	Sequential data
Transformer	NLP, Complex Pattern Matching	Long sequences, Large-scale data

4. Validation & Optimization

Methodology	Type	Usage / Purpose	Notes
Stratified K-Fold	Validation	Cross Validation (Generalization)	Essential for Imbalanced Class distribution
K-Fold CV	Validation	Cross Validation	Sufficient data, Balanced classes
Time Series Split	Validation	Cross Validation (Temporal)	No future data leakage (essential for time-series)
Grid Search	Tuning	Hyperparameter Optimization	Small search space (Exhaustive)
Bayesian Optimization	Tuning	Hyperparameter Optimization	Large search space, High evaluation cost
Optuna	Tuning	Next-gen Hyperparameter Optimization	Efficient, Define-by-run, Pruning capabilities
L1 (Lasso)	Regularization	Sparse Model, Feature Selection	When sparse solution is needed
L2 (Ridge)	Regularization	Prevent Overfitting, Weight Decay	When high multicollinearity exists
ElasticNet	Regularization	Combination of L1 and L2	When both feature selection and regularization needed

5. Interpretation

Methodology	Usage / Purpose	Notes
SHAP	Explain Model Predictions	Specialized for Tree-based models

Appendix: Evaluation Metrics Guide

Select metrics based on your problem type and business goal.

Classification Metrics

Metric	Focus	When to use
Accuracy	Overall Correctness	Balanced datasets only. Misleading for imbalanced data.
Precision	False Positive Reduction	When FP is costly (e.g., Spam Filter).
Recall	False Negative Reduction	When FN is critical (e.g., Cancer Diagnosis, Fraud).
F1 Score	Balance	When you need a balance between Precision and Recall.
ROC-AUC	Ranking Quality	When you need robust performance across thresholds.
Log Loss	Probability Confidence	When the predicted probability value itself matters.

Regression Metrics

Metric	Focus	When to use
MSE	Large Error Penalty	When outliers/large errors should be heavily penalized.
RMSE	Interpretability	When you need error in the same unit as the target.
MAE	Robustness	When you want to be robust against outliers.
R2 Score	Explainability	To see how much variance is explained by the model.
MAPE	Business Interpretability	Error in Percentage (%). Easy for stakeholders.

Clustering Metrics (Unsupervised)

Metric	Focus	When to use
Silhouette Score	Cluster Separation	To measure how similar an object is to its own cluster compared to other clusters.
Davies-Bouldin	Cluster Compactness	Lower is better. Good for comparing clustering algorithms.
Elbow Method	Optimal K	To find the inflection point (optimal K) in K-Means.