Course: B.Tech (Semester V)
Subject: Introduction to Data Analytics and Visualization
Subject Code: BCDS501
Exam Year: 2024–25
Maximum Marks: 70
Time: 3 Hours
Paper ID: 310469
Exam Structure Overview
SECTION A (2 × 7 = 14 Marks)
All short conceptual questions — definition-based or comparison-oriented.
Topics include scaling analytics, modern BI tools, Bayesian vs Neural networks, real-time sentiment analysis, clustering techniques (CLIQUE, ProCLUS), and visualization types.
SECTION B (3 × 7 = 21 Marks)
Attempt any three — descriptive questions requiring explanation and reasoning.
Topics: Big Data characteristics, rule induction, decaying windows, clustering comparisons, and visual interface design.
SECTION C (3 × 7 = 21 Marks)
Attempt one part from each question — application and analytical-type questions.
Covers IoT-driven analytics, analytic lifecycle, multivariate analysis, optimization methods, real-time analytics platforms, streaming clustering, and visualization techniques.
Key Topics for Preparation (From the Paper)
Big Data & Analytics Scaling
Volume, Velocity, Variety, Veracity, Value
Challenges: storage, processing speed, integration
Solutions: Hadoop, Spark, cloud analytics, data lakes
Modern BI Tools
Tableau, Power BI, Looker, Qlik Sense
Integration of data extraction, visualization, and reporting
Modeling Approaches
Bayesian models – probabilistic, interpretable, uncertainty estimates
Neural networks – deep learning, pattern recognition
Prediction Dynamics
Linear models (regression) vs Nonlinear (trees, neural nets)
Real-Time Sentiment Analysis
Social media data, NLP challenges, noise, slang, emojis
Clustering Techniques
CLIQUE / ProCLUS – subspace clustering for high-dimensional data
K-means vs Hierarchical – speed vs interpretability
Visualization Trade-offs
Static (reports, print) vs Dynamic (dashboards, interactivity)
Rule Induction
Decision trees, association rules (Apriori), pattern extraction
Decaying Windows (Stream Analytics)
Focus on recent data → efficient memory usage
Figure Captions in Visualization
Explain chart meaning, context, and message clarity
IoT & Real-Time Systems
Continuous sensor data → demand for real-time analytics
Analytic Lifecycle
Stages: data collection → preparation → modeling → evaluation → deployment
Multivariate & High-Dimensional Challenges
Dimensionality reduction (PCA, feature selection)
Optimization Techniques
Deterministic (gradient descent) vs Stochastic (genetic algorithms)
Real-Time Analytics Platforms (RTAP)
Examples: Kafka + Spark + Tableau; case study: stock prediction
Parallelism & Streaming Clustering
Distributed computing for large datasets; micro-clustering
Visualization Design
Navigation links, user interaction, human vision limits
