Fundamentals of Big Data & Business Analytics JUNE 2025

Description

Fundamentals of Big Data & Business Analytics

Jun 2025 Examination

 

 

Q1. Indian Railways: Operations and Passenger Experience Optimization Indian Railways manages    millions of daily passengers    and collects vast amounts of data from    ticket bookings, train schedules, GPS tracking, passenger complaints, and real-time weather reports. Discuss how    descriptive analyticscan help monitor train delays and passenger demand, how    predictive analytics    can anticipate congestion, maintenance issues, and peak travel periods, and how    prescriptive analytics    can optimize train scheduling, crowd control, and service reliability. (10 Marks)

Ans 1.

Introduction

Indian Railways, one of the world’s largest railway networks, serves millions of passengers daily and generates vast volumes of operational and passenger-related data. From ticket reservations and train GPS systems to weather data and passenger feedback, the system offers a rich source of information that, if analyzed correctly, can significantly improve service quality and operational efficiency. With increasing passenger expectations and infrastructure constraints, data-driven decision-making becomes crucial. By applying business analytics—

 

 

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Q2. Big Data in Financial Markets

A stock trading platform collects vast amounts of data from    market transactions, investor sentiment on social media, economic reports, and algorithmic trading bots. Explain how firms handle    data ingestion, processing, storage, and management    using    big data frameworks    such as    distributed computing, cloud storage, and real-time streaming. Discuss how this data is analyzed to detect fraud, optimize trading strategies, and provide personalized investment recommendations (10 Marks)

Ans 2.

Introduction

The modern stock trading ecosystem is highly data-driven, with platforms processing enormous volumes of structured and unstructured data in real time. From market tick data and economic indicators to social media sentiment and algorithmic trading patterns, financial firms rely on big data technologies to make informed, fast, and secure decisions. Traditional systems are no longer sufficient to handle the speed, scale, and complexity of such data. Advanced big data frameworks such as distributed computing, cloud-based storage, and real-time data

 

 

Q3 (A)  Visualization for Urban Traffic Management

A city’s traffic control system collects data from  GPS trackers, CCTV feeds, road sensors, and mobile navigation apps  to ease congestion. Explain how  descriptive analytics  can identify high-traffic zones and discuss how    visualization tools (heatmaps, real-time dashboards, interactive reports)  can help city planners make data-driven decisions. (5 Marks)

Ans 3a.

Introduction

In modern cities, traffic congestion is a persistent challenge that affects mobility, air quality, and productivity. With technological advancements, urban traffic systems now collect vast data through GPS trackers, road sensors, CCTV cameras, and mobile navigation apps. This data is crucial in understanding traffic patterns and enabling real-time decisions. Descriptive analytics combined with advanced visualization tools can transform raw data into meaningful insights, helping city planners identify high-traffic zones, analyze trends

 

 

Q3 (B) Social Media and Web Analytics for E-Commerce

An online retailer wants to leverage    customer reviews, social media trends, browsing behavior, and mobile app data    to improve sales. Discuss how    social media sentiment analysis, mobile analytics, and web tracking tools    can generate insights on customer preferences. Explain how    predictive and prescriptive analytics    can drive personalized marketing, optimize product recommendations, and improve customer retention. (5 Marks)

Ans 3b.

Introduction

In the competitive world of e-commerce, understanding customer behavior is key to driving sales and building loyalty. Online retailers today have access to rich data sources such as customer reviews, social media activity, browsing patterns, and mobile app usage. By analyzing this data using advanced web and mobile analytics, businesses can uncover valuable insights into customer