Data Analyst AI Prompts

Create an exploratory visualization dashboard for this dataset using matplotlib and seaborn. Include: - Histogram for each numeric column (up to 6, pick the most interesting) - Bar chart showing the top 10 values of the highest-cardinality categorical column - Correlation heatmap for all numeric columns - Line chart showing the trend over time if a date column exists Layout: a 2×2 or 2×3 grid of subplots. Style: clean white background, readable font sizes, meaningful titles and axis labels. Do not use default matplotlib styles — apply a clean, minimal look.

Create a ranked bar chart for the most important categorical breakdown in this dataset: 1. Identify the best categorical column to use as the dimension (highest analytical value) 2. Identify the primary numeric metric to measure 3. Calculate the metric per category and sort from highest to lowest 4. Create a horizontal bar chart (easier to read category labels) 5. Color the top 3 bars in a highlight color, the rest in a neutral color 6. Add data labels at the end of each bar showing the exact value 7. Add a vertical dashed line at the average value 8. Title the chart: '[Metric] by [Category] — [period]' Use a clean, minimal style with no unnecessary gridlines or chart elements.

Create a well-designed correlation heatmap for the numeric columns in this dataset: 1. Compute the Pearson correlation matrix for all numeric columns 2. Plot as a heatmap using a diverging colormap: dark blue for strong positive correlation, dark red for strong negative, white for zero 3. Show only the lower triangle (remove redundant upper triangle) 4. Add the correlation coefficient value inside each cell, rounded to 2 decimal places 5. Bold or highlight cells where |r| > 0.7 6. Sort columns and rows so that highly correlated variables are clustered together (use hierarchical clustering on the correlation matrix) 7. Set figure size so all labels are readable without overlapping Add a subtitle explaining what the strongest correlation means in business terms.

Create a complete chart pack for a {{report_type}} report on {{subject}} covering {{time_range}}. The pack should include exactly these chart types in order: 1. An overview trend chart: {{primary_metric}} over time with 30-day moving average 2. A breakdown chart: {{primary_metric}} by {{dimension_column}} as a ranked bar chart 3. A comparison chart: current {{time_range}} vs prior {{time_range}} for top 5 {{dimension_column}} values 4. A composition chart: share of {{primary_metric}} by {{segment_column}} as a donut chart 5. A scatter or correlation chart: {{primary_metric}} vs {{secondary_metric}} with regression line Style requirements: - Consistent color palette across all 5 charts: primary color {{brand_color}}, accent {{accent_color}} - All charts use the same font family and font sizes - Each chart has a title, subtitle (one-sentence insight), and data source label - Export as a 5-page PDF or a 5-slide PowerPoint layout

Create a visualization comparing the distribution of a key metric across multiple groups or segments: 1. Identify the primary numeric metric and the best grouping column in the dataset 2. Create a violin plot showing the full distribution shape per group 3. Overlay individual data points using a strip plot (jittered) for transparency 4. Add a box plot overlay to show median and quartiles clearly 5. Annotate each group with its median value and sample size (n=) 6. Sort groups from highest to lowest median 7. Use a color palette that distinguishes groups clearly without being distracting Add a title that describes what comparison is being shown.

Create an executive-ready KPI dashboard for this dataset. 1. Identify the 4–6 most important business metrics from the column names and data context 2. For each metric, create a time-series line chart with: - A trend line (7-day rolling average) - An annotation marking the most significant change point - Clear title, axis labels, and current value callout 3. Add a summary row at the top showing: current value, % change vs prior period, and a directional arrow (▲▼) 4. Use a consistent color palette — brand-neutral (blues and grays) 5. Layout should be presentation-ready (16:9 aspect ratio)

Step 1: Identify the single most important insight in this dataset that deserves a visual treatment. Step 2: Choose the most appropriate chart type for that insight and justify the choice. Step 3: Create the primary chart with full production styling: clean theme, annotated key points, descriptive title, subtitle with the insight stated explicitly. Step 4: Create one supporting chart that provides necessary context or comparison for the primary chart. Step 5: Write a 3-sentence data caption for the primary chart that a non-technical reader can understand — what is shown, what is the key finding, and what action does it suggest.

Create a calendar heatmap to reveal daily and weekly patterns in this time series: 1. Aggregate the primary metric by day 2. Create a GitHub-style calendar heatmap where: - Rows are days of the week (Mon–Sun) - Columns are weeks - Cell color intensity represents the metric value (lighter = lower, darker = higher) 3. Use a sequential color palette (e.g. light yellow to dark green) 4. Add month labels along the top 5. Add a color bar legend showing the value scale 6. Annotate the 3 highest and 3 lowest days with their exact values Below the chart, answer: Is there a clear day-of-week pattern? Is there a clear seasonal pattern across months?

Step 1: Analyze the dataset and identify the top 3 most important insights — each should be a specific, quantified finding that could drive a business decision. Step 2: For each insight, select the single best chart type to communicate it visually. Justify your choice in one sentence. Step 3: Build chart 1 with full production styling: meaningful title, subtitle stating the insight explicitly, annotated key data points, clean minimal theme. Step 4: Build charts 2 and 3 in the same visual style as chart 1, ensuring a consistent look across all three. Step 5: Arrange all three charts into a single figure with a shared title. Write a 50-word executive caption that tells the complete story across all three charts in sequence. Step 6: Export the figure at 300 DPI. Suggest the most appropriate slide or document format for sharing with a non-technical audience.

Create a visualization of missing data patterns in this dataset: 1. Generate a heatmap where rows are observations and columns are variables — missing values shown in a distinct color (e.g. red), present values in white or light gray 2. Sort columns left to right by missing value percentage (most missing on the left) 3. Add a bar chart below the heatmap showing the missing percentage per column 4. Add annotations for any columns with more than 20% missing values 5. Check for patterns: are missing values random, or do they cluster in certain rows or time periods? Write a 2-sentence interpretation: what is the overall completeness of the dataset, and is the missing data pattern random or systematic?

Create a compact multi-metric summary dashboard using sparklines: 1. Identify the top 6–8 business metrics in this dataset 2. For each metric, create one row in a summary table containing: - Metric name - Current value (latest period) - Change vs prior period: absolute and percentage, with colored arrow (▲ green / ▼ red) - A sparkline — a tiny inline line chart showing the last 12 periods of trend - A status indicator: ✅ On track / ⚠️ Watch / 🔴 Alert (based on whether the trend is improving or deteriorating) 3. Sort metrics by business importance, not alphabetically 4. Use a clean table layout — no heavy borders, subtle row alternation 5. The whole dashboard should fit on a single A4 page or slide This format is designed for a weekly business review where space is limited.

Create a composition chart showing how a total is broken down across categories: 1. Identify the best categorical column for the breakdown (5–8 categories ideal) 2. Calculate each category's share of the total 3. Create a donut chart (not a pie — donut is cleaner and leaves room for a center label) 4. Place the total value and a label ('Total [metric]') in the center of the donut 5. Show percentage labels on each segment, but only if the segment is larger than 3% (suppress tiny labels) 6. Group all segments smaller than 2% into an 'Other' category 7. Use a qualitative color palette — no gradients, each category a distinct color 8. Add a clean legend outside the chart Also create a companion table showing: category, count, percentage, ranked from largest to smallest.

Create an annotated scatter plot showing the relationship between two key variables: 1. Identify the two most interesting numeric variables to compare (ideally a cause and an effect) 2. Create a scatter plot with: - Each point representing one row - A linear regression line with confidence band (95%) - The R² value displayed in the top corner 3. Color points by a categorical variable if one exists (e.g. region, product type) 4. Label the top 5 and bottom 5 outlier points with their identifier 5. Add axis labels that describe what each variable measures, including units 6. If the relationship is non-linear, also fit and plot a polynomial regression line Write a one-sentence interpretation below the chart: what does the relationship mean in business terms?

Create a comparison chart for {{metric}} broken down by {{segment_column}}. Chart specifications: - Chart type: {{chart_type}} — bar chart for categorical comparison, line chart for time-series comparison, box plot for distribution comparison - Time range: {{time_range}} - Highlight the top 3 and bottom 3 segments with distinct colors - Add a dashed reference line at the overall average value - Annotate the highest and lowest data points with their exact values - Title: '{{metric}} by {{segment_column}} — {{time_range}}' - Export at 300 DPI suitable for a slide deck

Create a clean time series chart for the primary metric in this dataset: 1. Plot the raw values as a thin line in a neutral color 2. Overlay a 7-day rolling average as a thicker, more prominent line 3. Add a horizontal reference line at the overall average 4. Annotate the global maximum and minimum points with their values and dates 5. Shade the area under the rolling average line with low opacity 6. Use a minimal style: no gridlines on the x-axis, light gridlines on y-axis, clean legend Title the chart with the metric name and the date range shown.

Create a waterfall chart to show how a metric changed between two periods: 1. Identify the starting value (e.g. last month's total) and ending value (this month's total) 2. Decompose the change into its contributing factors — what drove the increase or decrease? (look for dimension columns like region, product, channel) 3. Build a waterfall chart where: - First bar: starting value (blue) - Middle bars: positive contributors (green, going up) and negative contributors (red, going down) - Final bar: ending value (blue) 4. Add value labels on top of each bar 5. Add a dashed horizontal reference line at the starting value 6. Sort contributing factors from largest positive to largest negative Title the chart: '[Metric]: [Start Period] to [End Period] — What Changed and Why'

Analyze this dataset and generate a complete, production-ready Python cleaning script. The script must: 1. Load the data 2. Fix all data type issues (with explicit dtype mapping) 3. Handle all missing values with the appropriate strategy per column 4. Remove or flag duplicate rows 5. Apply all string standardizations needed 6. Fix date columns to ISO 8601 format 7. Clip or remove outliers where appropriate 8. Assert data quality at the end: row count within expected range, no nulls in key columns, all dates in valid range Code style requirements: - Use pandas - Add a comment above every transformation explaining why it is needed - Include a final print() summary: rows before, rows after, columns changed - Make the script idempotent — safe to run multiple times

Review all column names in this dataset and produce a renaming plan: 1. Identify columns that violate snake_case convention (spaces, camelCase, PascalCase, hyphens, special characters) 2. Identify columns with unclear or ambiguous abbreviations (e.g. 'qty', 'amt', 'dt', 'flg') 3. Identify columns where the name doesn't match the apparent content based on sample values 4. Propose a clear, descriptive snake_case name for each column that needs renaming Return a table: original_name | issue | proposed_name | reason Also return a Python code snippet using df.rename() to apply all changes at once.

Step 1: Assess completeness — calculate the percentage of non-null values across all columns and rows. Score: (non-null cells / total cells) × 100. Step 2: Assess consistency — count type mismatches, formatting inconsistencies, and constraint violations. Score: 100 minus one point per violation type found. Step 3: Assess uniqueness — count exact duplicate rows and near-duplicate rows. Score: (unique rows / total rows) × 100. Step 4: Assess validity — count values that fail domain rules (impossible numbers, invalid dates, unexpected categoricals). Score: (valid rows / total rows) × 100. Step 5: Compute an overall Data Quality Score as a weighted average: completeness 30%, consistency 25%, uniqueness 20%, validity 25%. Step 6: Return a one-page Data Quality Report: score per dimension, overall score, top 5 issues to fix, and estimated effort to resolve each.

Audit and fix the data types in this dataset: - Identify all columns where the stored type does not match the semantic type (e.g. dates stored as strings, IDs stored as floats, booleans stored as integers) - For each mismatch: show current type, correct type, and a code snippet to convert it - Check numeric columns for values that contain currency symbols, commas, or percent signs that prevent proper numeric parsing - Identify any column that should be treated as an ordered categorical rather than a plain string Return corrected pandas dtype assignments for the full dataset.

Standardize all date and time columns in this dataset: 1. Identify every column that contains dates or times, including those stored as strings 2. Detect all date format variations in use (e.g. 'MM/DD/YYYY', 'DD-Mon-YYYY', 'YYYY-MM-DD', Unix timestamps) 3. Convert all date columns to a single standard format: ISO 8601 (YYYY-MM-DD for dates, YYYY-MM-DDTHH:MM:SS for datetimes) 4. Handle timezone information: identify columns with mixed timezones and convert all to UTC 5. Extract useful components as new columns where relevant: year, month, day_of_week, hour, is_weekend 6. Flag any ambiguous dates where format is unclear (e.g. 01/02/03 could be Jan 2, 2003 or Feb 1, 2003) Return the conversion code and a before/after sample for each date column.

Find and handle all duplicate records in this dataset: 1. Exact duplicates — rows identical across all columns. Count them and show 3 examples. 2. Key duplicates — rows with the same primary key or identifier column but different values elsewhere. Which columns differ? 3. Fuzzy duplicates — near-identical records in string columns (name, email, address). Use fuzzy matching with a similarity threshold of 0.85. For each type, recommend a deduplication strategy (keep first, keep last, merge, manual review). Apply the strategy and report how many rows were removed.

Step 1: Audit the dataset — list all data quality issues: missing values, duplicates, type mismatches, impossible values, encoding problems, inconsistent formatting. Step 2: Propose a prioritized cleaning plan. Order tasks from highest to lowest impact. Justify each decision. Step 3: Execute every cleaning step. Show before and after statistics for each transformation. Step 4: Validate the cleaned dataset — confirm row count retained, zero remaining missing values (or document intentional exceptions), type consistency. Step 5: Generate a cleaning report: one row per transformation, with column name, issue type, action taken, and rows affected.

Scan this dataset for values that are logically impossible or highly implausible: 1. Numeric impossibilities: negative ages, negative prices, quantities above a plausible maximum, percentages above 100 or below 0 2. Date impossibilities: future dates in historical columns, dates before the business existed, end dates before start dates 3. Cross-column contradictions: e.g. refund amount greater than original order amount, child account older than parent account 4. Statistical implausibility: values more than 6 standard deviations from the mean (not just outliers — true anomalies) For each impossible value found: column, row index, value, why it is impossible, and recommended fix.

Analyze all missing values in this dataset and recommend a handling strategy for each column. For numeric columns, choose between: drop, mean imputation, median imputation, forward fill, or model-based imputation. For categorical columns, choose between: drop, mode imputation, 'Unknown' category, or indicator variable. For any column with more than 50% missing values, recommend dropping it. Also scan for hidden nulls: empty strings, 'N/A', 'null', 'None', 'nan', '-', whitespace-only values. Return a table: column | missing % | recommended strategy | rationale.

Audit the numeric precision in this dataset: 1. For each numeric column, check the number of decimal places used — is it consistent? 2. Identify columns where values are suspiciously round (e.g. all multiples of 100 or 1000) — this may indicate estimation or truncation 3. Check for floating point precision issues (e.g. 0.1 + 0.2 ≠ 0.3 artifacts stored as data) 4. Identify columns that should be integers but are stored as floats (e.g. counts, quantities, IDs) 5. Flag any columns where precision varies significantly across rows (some with 0 decimals, some with 8) Return recommendations for the correct data type and precision for each numeric column, plus code to apply the corrections.

Identify and treat outliers in all numeric columns: 1. Detect outliers using IQR (flag values beyond Q1 - 1.5×IQR and Q3 + 1.5×IQR) 2. For each outlier cluster, determine: data entry error, legitimate extreme, or distribution tail? 3. Apply appropriate treatment per column: - Cap at percentile boundary (Winsorization) if legitimate extremes - Replace with null then impute if likely data error - Keep as-is if confirmed legitimate 4. Show before/after statistics for each treated column 5. Document every decision made

Check referential integrity across the tables or joined datasets provided: 1. Identify all foreign key – primary key relationships between tables 2. For each relationship, count: - Orphaned records: foreign key values with no matching primary key - Unmatched primary keys: records in the parent table with no children 3. Calculate the match rate for each join: what percentage of records join successfully? 4. Show examples of orphaned records and identify the most common missing foreign key values 5. Assess the impact: if orphaned records are dropped, what percentage of rows would be lost from each table? Return an integrity report table and recommend whether to: drop orphans, impute, or flag for manual review.

Validate this dataset against data engineering best practices: Naming: Are column names in snake_case? Any spaces, special characters, or ambiguous abbreviations? Types: Do types match the semantic meaning? (e.g., IDs stored as float, dates stored as string, booleans stored as int) Ranges: Are there numeric values outside a plausible range? (negative ages, prices, quantities; future dates in a historical column) Cardinality: Do categorical columns contain unexpected values or obvious typos? Referential integrity: If multiple tables are provided, do foreign keys match primary keys? Return a validation report with columns: field | issue type | severity (error / warning / info) | description | suggested fix.

Standardize all text and string columns in this dataset: 1. Trim leading and trailing whitespace from all string fields 2. Detect and normalize inconsistent casing (e.g. 'new york', 'New York', 'NEW YORK' → 'New York') 3. Find and consolidate equivalent values using different spellings or abbreviations (e.g. 'US', 'USA', 'United States', 'U.S.A.') 4. Detect and fix common OCR or data entry errors (e.g. '0' vs 'O', '1' vs 'l') 5. Standardize phone numbers, postcodes, and email addresses to consistent formats where present Return the full set of replacements applied and the number of rows affected by each.

Audit this dataset for whitespace, encoding, and character set issues: 1. Find all string columns that contain leading or trailing whitespace 2. Detect non-printable characters, null bytes, or control characters embedded in string fields 3. Identify any columns with mixed encodings (UTF-8 vs Latin-1 artifacts like é instead of é) 4. Find columns with inconsistent use of quotes, apostrophes, or escaped characters 5. Detect columns where numeric values are stored with thousands separators (1,234 vs 1234) or currency symbols ($1,234) 6. Check for Windows-style line endings (\r\n) in text fields Return a list of affected columns, the issue type, an example, and the cleaning code to fix it.

Analyze pairwise relationships between the key variables in this dataset: 1. Identify the most important target or outcome variable 2. For each other numeric column, create a scatter plot vs the target variable and compute the correlation coefficient 3. For each categorical column, show the mean target value per category (group-by analysis) 4. Flag any non-linear relationships that a correlation coefficient would miss 5. Identify the single variable that has the strongest relationship with the target, linear or otherwise 6. Note any interaction effects — pairs of variables that together predict the target better than either alone Return a ranked list of variables by predictive relationship strength.

Profile all categorical and text columns in this dataset: - For each column: unique value count, top 10 most frequent values with percentages - Flag high-cardinality columns (more than 50 unique values) - Identify columns that look like free text vs controlled vocabulary - Check for inconsistent formatting within the same column (e.g. 'USA' vs 'United States' vs 'us') - Identify any categorical column that could be useful as a grouping or segmentation dimension Return a profile table and highlight the 3 most analytically useful categorical columns.

Map the relationships between columns in this dataset: 1. Identify likely primary key columns (unique identifiers) 2. Identify likely foreign key columns (references to other entities) 3. Group columns into logical categories: identifiers, dimensions, measures, dates, flags 4. For each measure column, identify which dimension columns are most likely used to slice or filter it 5. Draw a simple text-based entity map showing how columns relate to each other This should help me understand the data model before I start querying.

Find all significant correlations in this dataset: - Compute the full correlation matrix for all numeric columns - List the top 10 strongest positive and negative correlations with their r values - Flag any pairs with |r| > 0.85 as multicollinearity risks - For each flagged pair, recommend which column to keep based on relationship to the target or business relevance - Visualize the correlation matrix as a heatmap with annotations - Note any correlations that are surprising or counterintuitive

Check how fresh and timely this dataset is: 1. What is the most recent record date in the dataset? 2. How many hours or days old is the most recent data compared to today? 3. Is there evidence of data latency — events that happened recently but haven't appeared yet? 4. Are records added in batches (e.g. large jumps at specific times) or continuously? 5. Compare record volume in the most recent period vs the equivalent prior period — does it look complete or truncated? 6. Flag any columns that suggest pipeline delays (e.g. processing_date significantly later than event_date) Return a freshness verdict: Real-time / Near real-time / Daily batch / Delayed / Stale.

Give me a complete overview of this dataset. Include: - Shape (rows, columns) - Column names and data types - Missing values per column (%) - Basic statistics for numeric columns (mean, std, min, max, quartiles) - Sample of first 5 rows Highlight any immediate data quality issues you notice.

Assess the dimensionality and information density of this dataset: 1. How many features are there relative to the number of rows? Is this dataset wide, tall, or balanced? 2. Apply PCA and report how many components explain 80%, 90%, and 95% of the variance — this shows the true effective dimensionality 3. Identify groups of highly correlated features that are effectively measuring the same thing 4. Flag any features that appear to be near-linear combinations of others (redundant features) 5. Identify features with near-zero variance — they carry almost no information 6. Recommend a minimum feature set that retains 90% of the information in the dataset Return a dimensionality report with: original features, effective dimensions, redundant groups, and recommended feature set.

Analyze the distribution of every numeric column in this dataset: - Compute mean, median, std, skewness, and kurtosis - Identify columns with skewness above 1 or below -1 - Flag outliers using the IQR method (1.5× IQR rule) - Suggest an appropriate transformation for skewed columns (log, sqrt, Box-Cox) - Plot a histogram for each numeric column Return a summary table: column | skewness | outlier count | recommended transformation.

I just received this dataset and have never seen it before. Help me understand it from scratch: 1. What does this dataset appear to be about? What business domain or process does it describe? 2. What is the grain of the data — what does one row represent? 3. What are the most important columns, and what do they measure? 4. What time period does it cover? 5. What are the top 3 questions this data could answer? 6. What are the top 3 questions it clearly cannot answer? Write your response in plain English, as if explaining to someone seeing data for the first time.

Step 1: Profile the dataset — shape, column types, missing values, duplicates, memory usage. Step 2: Analyze distributions and detect outliers in all numeric columns. Step 3: Analyze cardinality and value frequencies in all categorical columns. Flag any with high cardinality (>50 unique values). Step 4: Compute and visualize the correlation matrix. Flag pairs with |r| > 0.85. Step 5: Identify the 5 most interesting patterns, anomalies, or relationships in the data. Step 6: Write a 1-page EDA summary report: dataset description, key findings, data quality issues, and recommended next steps.

Create a clean summary table for all numeric columns in this dataset. For each numeric column include: - Count of non-null values - Mean and median - Standard deviation - Min and max - 25th, 50th, and 75th percentiles - Number of zeros - Number of negative values - Number of unique values Format as a transposed table where each column name is a row. Highlight any column where the mean and median differ by more than 20% — this indicates skewness. Highlight any column with more than 10% zero values — these may need special treatment.

Give me a comprehensive map of extreme values across this entire dataset: 1. For every numeric column, show the top 5 highest and bottom 5 lowest values with their row indices 2. Flag any value that exceeds 5 standard deviations from the mean — these are extreme outliers 3. Check whether extreme values cluster in the same rows (a single row that is extreme across many columns is suspicious) 4. Classify each extreme value: plausible business value, likely data error, or needs investigation 5. Calculate what percentage of rows contain at least one extreme value Return a summary table and highlight the 3 rows most deserving of manual inspection.

Run a quick health check on this dataset and return a traffic-light summary: 🟢 Good / 🟡 Needs attention / 🔴 Critical issue Check: 1. Completeness — missing values above 5% per column? 2. Consistency — mixed data types, formatting issues, encoding errors? 3. Timeliness — what is the date range? Are there gaps? 4. Accuracy — values that seem impossible or implausible? 5. Uniqueness — duplicate rows present? For each check, state the status and a one-sentence finding.

Inspect the temporal structure of this dataset: - Identify all date, datetime, or timestamp columns - For each: min date, max date, total time span, and inferred frequency (daily, weekly, monthly) - Check for gaps in the time series — are there missing periods? - Check for duplicate timestamps - Identify any time-zone inconsistencies - Plot the number of records per time period to visualize data volume over time Summarise whether this dataset is suitable for time series analysis and flag any issues that must be resolved first.

Analyze this dataset and return exactly 5 key findings, ordered from most to least important. For each finding: - A bold one-sentence headline stating the finding - Two to three supporting sentences with specific numbers from the data - One sentence on the business implication Rules: - No filler or vague statements. Every sentence must contain a specific number or comparison. - Findings must be distinct — no overlapping insights. - Use plain language a non-analyst could understand. End with one sentence: 'The finding that most urgently requires action is [finding N] because [reason].'

Identify customers or users at risk of churning based on behavioral signals in this dataset: 1. Define churn signals from the available columns (e.g. declining purchase frequency, reduced engagement, support ticket spikes, payment failures) 2. Score each customer on a churn risk scale of 1–10 based on the strength of signals present 3. Identify the top 20 highest-risk customers with their risk score and primary churn signal 4. Segment at-risk customers by reason: price sensitivity, product dissatisfaction, competitive alternative, inactivity 5. Recommend one targeted retention action per segment Return a churn risk table and a 2-sentence executive summary of the overall churn risk level.

Step 1: Identify the single most important insight in this dataset. State it in one sentence, as if telling a non-technical colleague. Step 2: Find exactly 3 data points that serve as compelling evidence for this insight. For each: state the number, what it means, and why it matters. Step 3: Find one counterintuitive or surprising finding that adds nuance and prevents oversimplification. Step 4: Identify the top 2 questions this data cannot answer — what additional data would you need to be fully confident in your recommendation? Step 5: Write a complete data narrative: opening hook, central insight with evidence, nuance, data gap acknowledgement, and a clear call to action.

Analyze this dataset and write a concise executive summary in exactly 3 paragraphs: Paragraph 1 — Situation: What does this data describe? What is the main trend over the period shown? Paragraph 2 — Complication: What is the most significant risk, anomaly, or missed opportunity hidden in the data? Cite at least two specific numbers. Paragraph 3 — Recommendation: What is the single most important action to take, who should own it, and by when? Tone: direct, data-driven, no jargon. Max 200 words total. Write as if presenting to a C-suite audience.

Generate a KPI status report for {{reporting_period}} using the data provided. For each key metric: - Current value and target (source: {{target_source}}) - Absolute and percentage change vs {{comparison_period}} - Status label: ✅ On Track / ⚠️ At Risk / 🔴 Off Track - One-sentence explanation of the primary driver behind the change - If Off Track: one specific recommended corrective action Format: a clean table with one KPI per row. At the bottom, add a 2-sentence overall summary: is the business trending in the right direction, and what is the most urgent issue to address?

Use this dataset to size the biggest business opportunity available: 1. Identify the metric that has the largest gap between current performance and best-in-class performance (either internal top performer or industry benchmark if known) 2. Calculate the revenue or metric impact of closing 25%, 50%, and 100% of that gap 3. Identify which segment, region, or cohort offers the fastest path to closing the gap 4. Estimate the effort level: is this gap likely due to a process issue (fixable quickly) or a structural issue (requires longer investment)? 5. Write a one-paragraph opportunity statement suitable for an internal business case

Analyze pricing patterns and their relationship to business outcomes in this dataset: 1. Show the distribution of prices across products, tiers, or regions 2. Identify any price clustering (common price points that appear frequently) 3. Calculate the correlation between price and volume/quantity — is there a clear demand elasticity signal? 4. Find the price point with the highest total revenue contribution (price × quantity) 5. Identify any products or segments where price and margin seem misaligned 6. Recommend 2–3 pricing adjustments based on the data, with estimated revenue impact of each

Analyze the performance of every segment in this dataset. For each segment, compute: - Size (row count and % of total) - Mean and median of the primary metric - Growth rate vs the prior period - Share of total metric contribution Then: - Rank segments from highest to lowest performing - Flag any segment with an unusual growth rate (more than 2 standard deviations from the average segment growth) - Identify the segment with the highest untapped potential - Write 3 concrete strategic recommendations based on the segment findings

Build a demand forecast that incorporates external factors: 1. Base model: fit a Prophet or ARIMA model on historical {{target_metric}} alone. Record baseline MAPE. 2. Add external regressors: {{external_factors}} (e.g. price, promotions, marketing spend, economic index). Fit a new model including these. 3. Compare accuracy: does adding external factors improve MAPE by more than 5%? If yes, use the richer model. 4. Identify which external factor has the highest predictive power (use feature importance or correlation with residuals) 5. Generate a {{forecast_horizon}}-day forecast with three scenarios: optimistic, base, pessimistic — varying the {{key_lever}} assumption. Return: accuracy comparison table, feature importance chart, and the 3-scenario forecast plot.

Step 1: Decompose the time series using STL decomposition. Identify and plot trend, seasonality, and residual components. Note the dominant seasonality period. Step 2: Test for stationarity using the ADF test. If non-stationary, apply first differencing or log transformation and retest. Step 3: Train three competing models on the first 80% of the data: (a) ARIMA with auto-selected p,d,q parameters, (b) Facebook Prophet with default settings, (c) Exponential Smoothing (Holt-Winters). Step 4: Evaluate all three models on the held-out 20% test window. Report MAPE, RMSE, and MAE for each. Declare a winner. Step 5: Use the winning model to generate a {{forecast_horizon}}-day forecast. Include 80% and 95% confidence intervals. Plot forecast vs actuals. Step 6: Write a one-paragraph forecast commentary: expected trend, key risks, seasonality effects to watch for, and the confidence level in this forecast.

Calculate and analyze growth rates for the primary metric in this dataset: 1. Compute week-over-week (WoW), month-over-month (MoM), and year-over-year (YoY) growth rates for each time period 2. Plot all three growth rate series on a single chart with a zero reference line 3. Identify the periods of fastest and slowest growth 4. Calculate whether growth is accelerating or decelerating — fit a trend to the MoM growth rate itself 5. Compare growth rates across segments if a segment column exists 6. Project where the metric will be in 90 days if the current growth trajectory continues unchanged Return a growth rate table and a plain-English summary: is the business growing faster or slower than before, and is the trend improving or deteriorating?

Build a time series forecast using Facebook Prophet on this dataset. 1. Prepare the data: rename the date column to 'ds' and the target column to 'y' 2. Configure Prophet with: - Yearly seasonality: auto-detect - Weekly seasonality: enabled if data frequency is daily - Country holidays: {{country_code}} if applicable 3. Split: use the last 20% of data as a test set 4. Fit the model on the training set and evaluate on the test set: report MAPE, MAE, and RMSE 5. Generate a forecast for the next {{forecast_horizon}} days with 80% and 95% uncertainty intervals 6. Plot: actual vs forecast, trend component, and seasonality components separately

Generate a 3-scenario forecast for {{metric}} over the next {{forecast_horizon}}: Scenario definitions: - Pessimistic: assume {{pessimistic_assumption}} (e.g. growth slows to half the current rate, churn increases by 20%) - Base case: continue current trend with normal seasonality - Optimistic: assume {{optimistic_assumption}} (e.g. growth accelerates by 50%, a new product launch captures additional market) For each scenario: - Plot the forecast line with a distinct color - Show the projected value at 30, 60, and 90 days - State the key assumption driving each scenario Highlight the range between pessimistic and optimistic as a shaded uncertainty band. Add a plain-English paragraph explaining what would need to be true for the optimistic scenario to materialize.

Decompose this time series to understand its underlying components: 1. Apply STL (Seasonal-Trend decomposition using LOESS) to separate the series into trend, seasonality, and residual components 2. Plot all three components with the original series 3. Quantify the strength of the seasonal component: what percentage of variance does it explain? 4. Identify the dominant seasonality period (daily, weekly, monthly, annual) 5. Check the residual component — does it look like white noise or does it contain unexplained structure? 6. Describe in plain English: what is the underlying growth trend, what is the seasonal pattern, and are there any unusual residuals that need investigation?

Project the future trend of the primary metric in this dataset. 1. Fit both a linear and a polynomial (degree 2) trend line. Compare R² values — which fits better? 2. Calculate the compound growth rate (CAGR) over the full observed period 3. Project the metric 30, 60, and 90 days into the future using the better-fitting model 4. Check for seasonal patterns and, if found, describe how they affect the projection 5. State the 3 key assumptions behind this forecast and one external factor that could invalidate it

Write a SQL cohort retention analysis using the table {{table_name}} in {{database_type}}. Definitions: - Cohort: the month of a user's first {{cohort_event}} recorded in {{date_column}} - Retention: whether the user performed {{retention_event}} in each subsequent month The query should: 1. Define cohorts using a CTE that finds each user's first event month 2. Join back to activity data to find which months each user was active 3. Calculate cohort size and retention count per month offset (0, 1, 2, ... N) 4. Return a cohort × month offset matrix with retention percentages Include comments on each CTE. Database: {{database_type}}.

Write a SQL query to calculate customer lifetime value (LTV) from the transactions table {{table_name}} in {{database_type}}. The query should compute per customer: - First purchase date and most recent purchase date - Total number of orders - Total revenue - Average order value - Purchase frequency (orders per month since first purchase) - Predicted LTV using the formula: avg_order_value × purchase_frequency × customer_lifespan_months Also segment customers into LTV tiers: Top 10%, Mid 40%, Bottom 50%. Return one row per customer with all metrics and the LTV tier label. Database: {{database_type}}.

Write a SQL query to analyze the date coverage of the table {{table_name}} using the date column {{date_column}} in {{database_type}}. The query should: 1. Return min date, max date, and total days spanned 2. Count distinct dates present vs expected dates in the range 3. Identify any missing dates (gaps in the sequence) 4. Show the top 5 largest gaps with start date, end date, and gap length in days 5. Count records per month to show data volume over time Add a comment explaining how to interpret each section.

Write a SQL funnel analysis for a multi-step user journey in this dataset. 1. Identify the funnel steps from the event data (look for event_name or action columns) 2. For each step, count: users who reached it, users who converted to the next step, and the conversion rate 3. Calculate time between steps for users who completed each transition (median and p90) 4. Identify where the biggest drop-off occurs 5. Segment the funnel by at least one dimension (e.g., device type, acquisition channel, country) if the column exists Return the full funnel table and a plain-English summary of the biggest opportunity for improvement.

Write a SQL query for the table {{table_name}} using window functions to compute: 1. Running total of {{metric}} ordered by {{date_column}} 2. 7-day and 30-day moving average of {{metric}} 3. Rank of each {{entity_column}} by {{metric}} within each {{partition_column}} 4. Each row's {{metric}} as a percentage of its {{partition_column}} total 5. Period-over-period change: vs prior row and vs same period last year Database: {{database_type}}. Add a comment explaining each window function used.

Write a SQL Type 2 Slowly Changing Dimension (SCD) implementation for the dimension table {{dim_table}} in {{database_type}}. The table structure uses: - Natural key: {{natural_key}} - Tracked attributes that trigger a new version: {{tracked_columns}} - SCD columns to manage: surrogate_key, valid_from, valid_to, is_current Write: 1. The CREATE TABLE statement with all required columns 2. The MERGE / UPSERT logic to handle: new records, changed records (expire old, insert new), unchanged records 3. A query to retrieve the current version of each record 4. A query to retrieve the historical version valid at a specific point in time: {{as_of_date}} Add comments explaining the SCD logic at each step.

Write a SQL query to profile the table {{table_name}} in {{database_type}}. The query should return: - Total row count - Distinct value count per column - NULL count and NULL percentage per column - Min, max, and average for numeric columns - Top 5 most frequent values for categorical columns Structure the output so each column appears as a separate row with all metrics in one result set. Add comments explaining each section.

Scan all business metrics in this dataset for unusual spikes or drops: 1. For each metric, compute the week-over-week and month-over-month percentage change 2. Flag any change greater than 2 standard deviations from the historical average change rate 3. For flagged metrics, check whether the spike is isolated to one dimension (e.g. one region, one product) or affects the whole metric 4. Determine whether the spike is a one-off event or the start of a new trend 5. Rank flagged metrics by business impact (highest volume or revenue first) Return a spike report table and a plain-English summary of the top 3 most concerning changes.

Detect anomalies that only appear in the combination of multiple variables: 1. Apply Isolation Forest to the full numeric feature matrix 2. Apply Local Outlier Factor (LOF) with n_neighbors=20 3. Find rows flagged as anomalous by both methods — these are high-confidence anomalies 4. For each high-confidence anomaly: show the full row, which features deviate most, and how they relate to each other 5. Compare anomalous rows against the median row to quantify how extreme each feature value is Return a ranked anomaly report with confidence score and a plain-English description of what makes each anomaly unusual.

Step 1: Identify the anomaly — which metric, which timestamp, and how large is the deviation from expected? Step 2: Slice the anomalous metric by every available dimension (region, product, channel, user segment, etc.). Where is the anomaly most concentrated? Step 3: Check all other metrics in the same time window. Are there correlated anomalies that suggest a common cause? Step 4: Compare the anomaly period against the same period from the prior week, prior month, and prior year. Is this pattern seasonal or truly novel? Step 5: Synthesize your findings into a root cause report: top 3 hypotheses ranked by likelihood, supporting evidence for each, and recommended next diagnostic step.

Detect outliers across all numeric columns using three methods: 1. Z-score — flag values beyond ±3 standard deviations 2. IQR — flag values below Q1 − 1.5×IQR or above Q3 + 1.5×IQR 3. Isolation Forest — use if the dataset has more than 1,000 rows For each outlier detected: - Column, row index, value, and which method(s) flagged it - Your assessment: likely data error, or genuine extreme value? Return a ranked list sorted by severity (most extreme first).

Detect anomalies in this time series data: 1. Build a rolling mean ± 2 standard deviation envelope (window = 7 periods) 2. Flag all points outside the envelope as anomalies 3. Check for abrupt level shifts using a changepoint detection method 4. Identify seasonality anomalies — values that are unusual specifically for their time of day, weekday, or month For each anomaly found: - Timestamp, observed value, expected range - Severity score from 1 (mild) to 10 (extreme) - Hypothesis: what might have caused this anomaly?