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Fundamentals of Big Data &
Business Analytics
April 2025 Examination
Q1. On Christmas
Eve in 2024,
American Airlines faced
a significant disruption, grounding all flights on a
critical travel day. Discuss how descriptive, predictive, and prescriptive
analytics can help the airline normalize operations in the aftermath and
prevent similar incidents
in the future.
Highlight specific analytical
approaches to optimize resource
allocation, identify potential risks, and enhance operational resilience.
Explain how leveraging historical data, real-time monitoring, and forecasting
techniques can improve decision-making during such crises. (10 Marks)
Ans 1.
Introduction
On
Christmas Eve in 2024, American Airlines experienced a significant disruption,
grounding all flights on a critical travel day, causing chaos for travelers and
substantial financial losses for the airline. In such crises, leveraging Big
Data and Business Analytics is crucial to restoring normalcy and preventing future
occurrences. Descriptive, predictive, and prescriptive analytics play a vital
role in analyzing past disruptions, forecasting potential risks, and
recommending optimal strategies. By utilizing historical data, real-time
monitoring, and forecasting models, American Airlines can enhance
decision-making, optimize resource allocation, and
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Q2. The fast fashion industry deals
with massive and complex datasets originating from multiple sources, such as
social media, e-commerce platforms, manufacturing units, and supply chain
systems. These datasets are generated at high velocity and in various formats.
Discuss how organizations in the fast fashion industry can effectively manage
and process this data using big data technologies. Highlight the role of
distributed storage systems, stream processing tools, and machine learning
techniques in deriving actionable insights from this data. Suggest a framework
for integrating structured and unstructured data to optimize inventory, predict
trends, and enhance sustainability efforts. (10 Marks)
Ans 2.
Introduction
The
fast fashion industry is characterized by rapid production cycles, dynamic
consumer preferences, and an extensive global supply chain. With digital
transformation, companies now generate vast amounts of data from e-commerce
platforms, social media interactions, inventory systems, and manufacturing
processes. Managing and analyzing this high-volume, high-velocity, and diverse
data is crucial for maintaining a competitive edge. Big data technologies provide
an effective way to handle such complex datasets, enabling fashion retailers to
optimize
Q3a. Governments strive to reduce
income inequality between urban and rural regions. Suggest the types of
datasets required to analyze historical trends and disparities. Explain how
descriptive analytics can be used to understand regional inequalities and
discuss how data visualization tools or techniques can effectively communicate
these insights to policymakers (5 Marks)
Ans 3a.
Introduction
Governments
strive to bridge income disparities between urban and rural regions by
analyzing historical trends and economic inequalities. Data-driven insights can
help policymakers understand the root causes of these disparities. By
leveraging descriptive analytics and data visualization tools, governments can
effectively identify patterns, track progress, and implement targeted socio-
b. The Mumbai city police
department is investigating a series of burglaries reported in different
neighborhoods over the past six months. They have collected the following data:
- Crime Locations: GPS coordinates of
burglary incidents, along
with timestamps.
- Suspect Profiles:
Witness descriptions, behavioral patterns, and prior criminal records.
- Social Media
Activity: Posts and discussions in local community groups about suspicious
activities.
- Environmental
Factors: Weather conditions, lighting, and proximity to high- traffic
areas during incidents.
- Neighborhood
Metrics: Demographics, foot traffic, and socioeconomic data for the
affected areas.
Please propose ways to use social
media activity to uncover potential suspects or accomplices.
Additionally, suggest predictive
analytics techniques to forecast future burglary hotspots and recommend
proactive measures for crime prevention. Outline the visualizations that would
best support your analysis and assist the police in their investigation (5 Marks)
Ans
3b.
Introduction
Mumbai
police are investigating a series of burglaries and analyzing various datasets,
including crime locations, suspect profiles, and social media activity. By
utilizing social media analytics and predictive models, law enforcement can
uncover potential suspects, anticipate future burglary hotspots, and implement
proactive measures to enhance crime prevention strategies across the
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