EDA and Data Visualization - NMIMS SOLVED ASSIGNMENTS June 2026

 

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EDA and Data Visualization

Jun 2026 Examination

 

 

Q1. A major healthcare provider is analyzing hospitalization records across different departments to optimize resource allocation. The dataset contains thousands of individual patient visits with information including department, admission and discharge timestamps, patient demographics, and diagnostic codes. The provider's leadership wants to identify which departments consistently have the highest patient load per day, as well as trends in severity and patient age profiles. However, the raw data's granularity makes it challenging to see actionable patterns or anticipate capacity bottlenecks. How would you apply aggregation and derived variable techniques to summarize this data and identify both the highest-load departments and department-specific patient characteristics? Outline which aggregation methods and feature engineering approaches you would use, and explain why these choices would provide insights for operational planning. (10 Marks)

Ans 1.

Introduction

Raw healthcare data at the patient-visit level is too granular to be directly useful for operational planning. A dataset with thousands of individual admission records tells you everything about each patient but nothing about the patterns that matter for resource allocation decisions. Aggregation and feature engineering are the tools that transform this raw granularity into actionable intelligence. In this scenario, the healthcare provider needs to move from individual records to department-level summaries, time-based patterns, and derived indicators of severity and patient profile. Applying these techniques correctly will allow leadership to see exactly where capacity pressure is building and which departments need what kind of

 

Q2 (A). A multinational retail company is analyzing its sales and customer data to improve marketing effectiveness and operational efficiency. During data profiling, the team finds thousands of duplicate records, caused by inconsistent spelling of names, system integration errors, and different date formats. Senior management is concerned that simply deleting duplicates might result in the loss of valuable information, yet keeping them could distort key analytics such as customer loyalty and sales frequency. The company is considering options that include merging records, standardizing formats, or eliminating duplicates. Evaluate the consequences of each proposed solution for handling duplicate records in this scenario. Justify which approach or combination of approaches would best balance data accuracy, business value preservation, and operational feasibility for the company. (5 Marks)

Ans 2(A).

Introduction

Duplicate records distort every downstream metric in a retail analytics system. In a company where loyalty scoring and purchase frequency drive marketing decisions, duplicates do not just create noise. They actively mislead the business. Each proposed solution has distinct consequences that must be evaluated before a

 

Q2 (B). A multinational consumer goods company recently unveiled a quarterly performance report to its board. The dashboard, filled with stacked bar charts showing hundreds of product lines, made use of vibrant color palettes, overlapping annotations, and complex gridlines. Several board members expressed confusion over sales trends, category comparisons, and the relevance of certain highlights. Debates arose about whether the core message was lost amid the visual overload, potentially obstructing effective executive decision-making. Evaluate the visualization approach adopted in the company's quarterly dashboard. Critique the use of visual elements in terms of cognitive load, data-to-ink ratio, and clarity for executive stakeholders. What alternative strategies would you recommend to optimize decision-making while ensuring insights are both clear and actionable? (5 Marks)

Ans 2(B).

Introduction

A dashboard that confuses its audience has failed its purpose regardless of how much data it contains. For an executive board making strategic decisions under time pressure, visual clarity is a functional requirement, not a design preference. The quarterly dashboard described here violates

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