Data Science
Conduct thorough exploratory data analysis
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
Design effective data dashboards
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
Structure data analysis reports professionally
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
Tester Prompts
Choose the right ML model for your problem
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.]