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Data Science Final Year Topic: Evaluation of Spreadsheet Data Validation Rules

This Data Science final year project investigates detecting common structural errors before spreadsheet tables enter an analysis, with explicit data definitions and reproducible analysis rather than invented findings.

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Why choose this project topic?

This topic turns detecting common structural errors before spreadsheet tables enter an analysis into a measurable analytical question. Comparing error recall, false flags and review workload gives a student material for visual results, methodological criticism and a discussion of how the data's limitations change the conclusion.

How does spreadsheet data validation rules affect detecting common structural errors before spreadsheet tables enter an analysis, as measured by error recall, false flags and review workload?

Choose an accessible licensed or synthetic dataset, agree the unit of analysis and narrow detecting common structural errors before spreadsheet tables enter an analysis to a reproducible comparison your supervisor can review.

Proposed project objectives

  1. 01Define the data, assumptions and comparison design for detecting common structural errors before spreadsheet tables enter an analysis.
  2. 02Create synthetic workbooks with planted type and range faults, compare rule sets and measure preservation of valid unusual values.
  3. 03Compare error recall, false flags and review workload and report uncertainty and sensitivity to analytical choices.

A suggested research approach

Create synthetic workbooks with planted type and range faults, compare rule sets and measure preservation of valid unusual values. Confirm the data licence and variable definitions before analysis. Preserve an untouched evaluation set where relevant, document exclusions and missingness, and report error recall, false flags and review workload with uncertainty or sensitivity checks rather than selecting only favourable results.

What you will need

  • Synthetic workbooks
  • Annotated error catalogue
  • Spreadsheet parsing tools

Keep your project scope clear

Validation rules reflect the documented schema and cannot establish factual accuracy of every value.

Data Science project chapter outline

Use this outline as a starting point. You can edit the chapter titles to match your department’s format during setup.

  1. Chapter 1Introduction
  2. Chapter 2Literature Review
  3. Chapter 3Research Methodology
  4. Chapter 4Presentation and Analysis of Results
  5. Chapter 5Summary, Conclusion and Recommendations

Turn this topic into your own final year project.

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