How not to do data science

Help me design a dashboard for:

**Audience:** [who will use this]
**Purpose:** [monitoring/analysis/reporting]
**Key Questions:** [what decisions will it inform]
**Data Sources:** [where data comes from]
**Update Frequency:** [real-time/daily/weekly]

Provide:

**1. KPI Selection**
- Primary metrics (3-5 max)
- Supporting metrics
- Metric definitions and calculations

**2. Layout Recommendation**
- Information hierarchy
- Section organization
- Visual flow

**3. Chart Types**
For each metric:
- Recommended visualization
- Why this chart type
- Interaction patterns

**4. Filtering & Drill-down**
- Global filters needed
- Drill-down paths
- Comparison options

**5. Alerts & Thresholds**
- What to highlight
- Threshold values
- Alert conditions

**6. Anti-patterns to Avoid**
- Common dashboard mistakes
- Specific to this use case

You are a senior data analyst. Create an analysis report for:

Dataset: [DESCRIBE YOUR DATA]
Business Question: [WHAT ARE YOU TRYING TO ANSWER]
Audience: [WHO WILL READ THIS]

Structure:
1. Executive Summary: Key findings in 3 bullets
2. Methodology: How you approached the analysis
3. Key Metrics: Define each metric, current values, trends
4. Insights: What the data tells us
5. Recommendations: Data-driven action items
6. Next Steps: Further analysis needed

Guide me through an EDA for this dataset:

**Dataset Description:** [what the data represents]
**Columns:** [list main columns and types]
**Business Question:** [what you're trying to learn]
**Tools:** [Python/R/SQL]

Provide code and explanations for:

**1. Data Overview**
- Shape, types, memory usage
- First/last rows inspection

**2. Data Quality**
- Missing values analysis
- Duplicate detection
- Outlier identification

**3. Univariate Analysis**
- Distributions of key variables
- Summary statistics
- Visualizations (histograms, box plots)

**4. Bivariate Analysis**
- Correlations
- Cross-tabulations
- Scatter plots for relationships

**5. Key Insights**
- Top 5 findings with implications
- Hypotheses to test
- Data quality issues to address

**6. Next Steps**
- Recommended analyses
- Features to engineer
- Questions for stakeholders

Help me choose the right ML model for my problem:

**Problem Type:** [classification/regression/clustering/etc.]
**Target Variable:** [what you're predicting]
**Features:** [types of input data]
**Dataset Size:** [rows and columns]
**Constraints:** [interpretability, latency, compute]
**Current Baseline:** [if any]

Provide:

**1. Problem Framing**
- Confirm problem type
- Success metrics to use
- Evaluation strategy

**2. Model Candidates**
For each recommended model:
- Why it fits this problem
- Pros and cons
- Hyperparameters to tune
- Computational requirements

**3. Recommended Approach**
- Start with: [simple baseline]
- Then try: [more complex options]
- Consider: [advanced techniques if needed]

**4. Implementation Checklist**
- Data preprocessing steps
- Feature engineering ideas
- Cross-validation strategy
- Overfitting prevention

**5. Code Skeleton**
- Basic implementation in [Python/sklearn/etc.]