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Capital One·Data Scientist·Take-home Assignment·Intermediate

Intermediate
Jun 2026

Summary

Capital One Data Scientist take-home where they basically handed you four CSVs and said 'replicate what an analyst would do in Excel, but in pandas.' The scope was bigger than I expected for a single assignment.

Questions Asked (5)

Q1

Load four CSV files into pandas in a way that handles a file path failure gracefully, without crashing the entire script.

Technical Trade-offsAlgorithms & Data Structures
Author's notes

I went with a try/except loop and a fallback path, which felt right, but I spent way too long debating whether to use a retry decorator or just catch the exception inline.

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AI HintsAI Generated

Suggested Approach

Start by outlining a robust loading function that iterates over the file paths, using try-except blocks to catch file-related errors and log them without halting execution. Emphasize that the function should return successfully loaded DataFrames and a list of failures, ensuring the script continues gracefully. Conclude by discussing how this approach balances reliability with maintainability in a production data pipeline.

Pro tip: Mention that you would also validate the loaded DataFrames (e.g., check for empty results or schema mismatches) to catch silent failures, showing you think beyond just file path errors.

1. Define the file paths

List the four CSV file paths in a structured way, such as a list or dictionary, to enable iteration and easy maintenance.

2. Implement error handling

Wrap each file read operation in a try-except block to catch exceptions like FileNotFoundError, PermissionError, or ParserError, and log the error instead of crashing.

3. Load and store results

For each successful read, store the DataFrame in a collection (e.g., dictionary keyed by filename); for failures, record the error details for later reporting.

4. Return or report outcomes

Return the successfully loaded DataFrames and a summary of failures, or print/log a clear message indicating which files loaded and which failed.

5. Optional: Validate loaded data

Briefly mention that you would check the loaded DataFrames for expected structure or content to ensure data quality, going beyond just file path errors.

Key Points to Mention

  • Use of try-except blocks to catch specific exceptions (e.g., FileNotFoundError, PermissionError) rather than a bare except.
  • Logging errors with appropriate severity (e.g., using the logging module) instead of printing, for production readiness.
  • Iterating over file paths to avoid repetitive code and make the solution scalable to more files.
  • Returning a clear summary of successes and failures to inform downstream processes.
  • Considering the trade-off between failing fast (for critical files) and continuing gracefully (for non-critical files).
  • Mentioning pandas-specific error handling, such as catching pd.errors.EmptyDataError or ParserError.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

Q2

Compute net revenue per order by subtracting any refund amount from the order total, treating orders with no refund record as having a zero refund.

Product Analytics & MetricsData Modeling
Author's notes

Straightforward left join on order_id then fillna(0).

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AI HintsAI Generated

Suggested Approach

Start by clarifying the data model: identify the orders table and refunds table, and understand their relationship (e.g., one-to-many). Then write a SQL query that LEFT JOINs orders to refunds, uses COALESCE to handle NULL refunds as zero, and computes net revenue as order_total minus refund_amount. Finally, validate the result with edge cases like multiple refunds per order or partial refunds.

Pro tip: Always confirm whether refunds can be multiple per order and whether they are stored as positive or negative amounts; this prevents incorrect aggregation and ensures you handle real-world data quirks.

1. Clarify the data model

Ask about the structure of the orders and refunds tables, including keys, cardinality, and whether refund amounts are positive or negative.

2. Choose the right join

Use a LEFT JOIN from orders to refunds to keep all orders, and handle cases where an order has no refund or multiple refunds.

3. Handle NULLs and aggregation

Use COALESCE or IFNULL to treat missing refunds as zero, and if multiple refunds exist, aggregate them (e.g., SUM) before subtracting.

4. Compute net revenue

Subtract the total refund amount from the order total to get net revenue per order.

5. Validate and test

Check edge cases such as orders with no refunds, full refunds, partial refunds, and multiple refunds to ensure correctness.

Key Points to Mention

  • Use of LEFT JOIN to preserve all orders
  • COALESCE or IFNULL to convert NULL refunds to zero
  • Aggregation of multiple refunds per order (SUM) before subtraction
  • Assumption about refund amount sign (positive vs negative)
  • Importance of validating with edge cases
  • Potential need to group by order ID if multiple refunds exist

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

Q3

Aggregate net revenue by month and acquisition channel for a specific three-month window, using the order date as the basis for month assignment.

Product Analytics & MetricsData Modeling
Author's notes

The date parsing part tripped me up slightly.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the business context and data schema, then outline a SQL-based aggregation that groups by month and acquisition channel, using order date for month assignment. Emphasize handling of edge cases like time zones, null channels, and revenue definitions, and validate results with sanity checks.

Pro tip: Always confirm whether 'net revenue' means after discounts, returns, and refunds, and whether the three-month window is inclusive or exclusive—these details can drastically change the numbers and show you think like a business partner.

1. Clarify Requirements and Data Schema

Ask about the exact definition of net revenue, the three-month window (inclusive/exclusive), and the acquisition channel field. Confirm the order date column and any time zone considerations.

2. Design the Aggregation Query

Write a SQL query that extracts the month from the order date, groups by month and acquisition channel, and sums net revenue. Use date functions like DATE_TRUNC or EXTRACT.

3. Handle Edge Cases and Data Quality

Address null or unknown acquisition channels, time zone conversions, and revenue adjustments (e.g., refunds). Decide whether to include or exclude incomplete months.

4. Validate and Sanity Check Results

Compare totals against overall revenue, check for missing months or channels, and ensure the sum of parts equals the whole. Consider running a quick pivot to spot anomalies.

5. Present Insights and Recommendations

Summarize key trends, such as which channels drive the most revenue per month, and suggest next steps like deeper cohort analysis or channel optimization.

Key Points to Mention

  • Definition of net revenue (gross revenue minus discounts, returns, and refunds)
  • Use of order date for month assignment and handling of time zones
  • SQL techniques: GROUP BY with DATE_TRUNC or EXTRACT, and filtering for the three-month window
  • Treatment of null or unknown acquisition channels (e.g., 'Unknown' category)
  • Validation checks: sum of monthly revenue equals total, no missing months
  • Business implications: identifying high-performing channels and seasonality

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

Q4

Left-join the aggregated monthly channel revenue to a targets table and compute the gap between actual net revenue and the revenue target, treating channels or months with no target as a zero target.

Data ModelingProduct Analytics & Metrics
Author's notes

This is where I started second-guessing the merge keys.

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AI HintsAI Generated

Suggested Approach

Start by aggregating net revenue by channel and month, then left join the targets table on channel and month so that all actuals are preserved. Use COALESCE to replace missing targets with zero, and compute the gap as actual net revenue minus the coalesced target. Validate the join grain and handle any duplicate targets before finalizing the metric.

Pro tip: Always validate that the targets table has a unique key on channel and month; if not, deduplicate or aggregate targets first to avoid fan-out and inflated gaps. Also, confirm whether the target is monthly or cumulative—if cumulative, you may need to adjust the comparison.

1. Aggregate actual net revenue

Group the revenue transactions by channel and month, summing net revenue to get one row per channel-month. Ensure the month is truncated to the first day or a consistent period.

2. Prepare the targets table

Check that the targets table has one row per channel-month. If duplicates exist, aggregate or deduplicate targets (e.g., sum or take max) to maintain a clean join key.

3. Left join actuals to targets

Left join the aggregated actuals to the targets table on channel and month, keeping all actual channel-month combinations. This ensures channels or months with no target are retained.

4. Handle missing targets

Use COALESCE (or IFNULL) to replace NULL targets with 0, so that the gap calculation treats missing targets as zero.

5. Compute the gap

Calculate the gap as actual net revenue minus the coalesced target. Optionally, also compute the gap percentage for further analysis.

Key Points to Mention

  • Left join preserves all actual channel-month rows, even when no target exists.
  • COALESCE (or equivalent) is used to treat missing targets as zero.
  • Grain of the join: one row per channel per month; validate uniqueness on both sides.
  • Potential duplicate targets must be handled before joining to avoid row multiplication.
  • Gap definition: actual net revenue minus target (or target minus actual, depending on business context).
  • Consider whether targets are monthly or cumulative and adjust accordingly.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

Q5

Produce two pivot tables: one showing net revenue by month and channel, and another showing the revenue target gap by month and channel. Channels with no orders in a given month should still appear with a value of zero.

Data ModelingProduct Analytics & Metrics
Author's notes

pivot_table with fill_value=0 handles the zero-fill requirement cleanly.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the data model and definitions (net revenue, target, channel, month). Then outline a SQL-based approach that aggregates orders and targets separately, uses a cross join or calendar table to ensure all month-channel combinations, and finally pivots the results into two tables. Emphasize handling missing data with COALESCE and zero-filling.

Pro tip: Mention that you would validate the pivot tables against a manual spot-check or a summary query to catch any discrepancies, especially around zero-filling and target gaps. This shows attention to data quality and business impact.

1. Clarify definitions and requirements

Confirm what 'net revenue' means (e.g., revenue after discounts/returns), how targets are stored, and the expected output format (e.g., SQL result set, BI tool pivot).

2. Aggregate revenue and targets

Write separate queries to sum net revenue by month and channel from orders, and to sum targets by month and channel from a targets table.

3. Ensure all month-channel combinations

Use a cross join between distinct months and channels, or a calendar table, to generate a complete grid. Left join the aggregated revenue and targets to this grid.

4. Compute target gap and zero-fill

Calculate the gap as target minus net revenue (or vice versa) and use COALESCE to replace nulls with zeros for both revenue and gap.

5. Pivot into two tables

Use conditional aggregation (CASE WHEN) or a pivot function to transform the data into two tables: one for net revenue and one for target gap, with months as rows and channels as columns.

Key Points to Mention

  • Definition of net revenue and target gap (e.g., target - actual revenue).
  • Use of a calendar table or cross join to ensure all month-channel combinations appear.
  • Handling of missing data with COALESCE or IFNULL to show zero instead of null.
  • Pivoting technique: conditional aggregation (SUM(CASE WHEN ...)) or PIVOT function.
  • Validation: compare totals with source data and check for unexpected zeros.
  • Performance considerations: indexing, filtering by date range, and avoiding unnecessary cross joins.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.