Which of the following are advisable strategies when working with datasets?

(Multiple answers possible).

Question

Which of the following are advisable strategies when working with datasets?

(Multiple answers possible).

BlackTom AI · Accepted Answer

Modularizing data—keeping a single type of information in one column—and using data validation for manually entered fields are best practices because they preserve consistency and enable reliable analysis.

1) Relevant knowledge points and concepts
- Atomic/normalized data: each column should represent one variable (e.g., "Sale Price", not "Sale Price / Currency").
- Data validation: techniques like dropdown lists, allowed ranges, type checks, and regex prevent typos and invalid values at entry.
- Effects on tools: merged cells, mixed-data types, and ad‑hoc placeholders interfere with sorting, filtering, pivot tables, CSV/SQL import, and automated analyses.

2) Step‑by‑step reasoning
- If a column holds only numeric sale prices, every cell should be numeric. This enables arithmetic (sums, averages) and correct type inference on import.
- Data validation (step: create a dropdown or set numeric-only rule) reduces incorrect entries: e.g., prevents typing "one hundred" or "N/A" into a numeric field.
- Keeping one atomic value per column (step: split combined fields into separate columns) allows grouping and aggregation without parsing strings.

3) Intermediate steps / examples
- Example problem: Sale Price column entries: 100, 200, N/A (text). If imported into a strict schema (e.g., SQL numeric column), the row with "N/A" will fail or cause the entire column to be treated as text. Using validation to restrict entry to numbers avoids this. Splitting "Full Name" into "First Name" and "Last Name" (modularization) lets you sort and analyze names efficiently.

4) Why the correct answers are correct
- "Use data validation techniques..." enforces correct types and valid ranges at input, reducing cleaning effort later.
- "Modularize your data..." ensures each column is homogeneous and analyzable, supporting reliable operations (filters, pivots, joins).

5) Why the other options are incorrect
- "Merge similar headings into one cell." Merged header cells break the rectangular tabular structure and cause parsing/import problems; headers should be single, distinct labels.
- "Don't leave any blank cells... type in N/A." Filling numeric fields with the text "N/A" introduces mixed types and can break imports or calculations. Better to leave true missing values blank or use a documented sentinel recognized by your tools.

Conclusion: The advisable strategies are to use data validation techniques for manually entered data and to modularize data so each column contains a single data type—matching the provided correct answers.

Which of the following are advisable strategies when working with datasets? (Multiple answers possible).多项选择题

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