Introduction to Data Analytics and Visualization (BCDS501) AKTU previous year questions 2023–2026
Every Introduction to Data Analytics and Visualization question from 4 AKTU papers, tagged by unit, topic and marks. A few recent questions from each unit are listed below; open the page to filter by unit, topic or mark type.
Unit 1: Introduction to Data Analytics – AKTU PYQs
- Define Data Analytics and explain its importance in decision making. (2 marks, 2026, Analytics Foundations)
- Differentiate between structured, semi-structured and unstructured data. (2 marks, 2026, Data Basics)
- List and briefly explain the phases of Data Analytics lifecycle. (2 marks, 2026, Analytics Lifecycle)
- Explain sources and characteristics of data with suitable examples. (7 marks, 2026, Data Basics)
- Explain Data Analytics lifecycle with a neat diagram and example. (7 marks, 2026, Analytics Lifecycle)
Unit 2: Data Analysis – AKTU PYQs
- What is regression modeling? Mention one application. (2 marks, 2026, Regression And Multivariate)
- Explain regression modeling and discuss its limitations. (7 marks, 2026, Regression And Multivariate)
- Explain Bayesian modeling and in data analytics. (7 marks, 2026, Bayesian Methods)
- Compare regression modeling and Bayesian modeling. (7 marks, 2026, Regression And Multivariate)
- Compare and contrast Bayesian modeling with neural networks in terms of their applications. (2 marks, 2025, Bayesian Methods)
Unit 3: Mining Data Streams – AKTU PYQs
- Define data streams with one real-time example. (2 marks, 2026, Stream Basics)
- Describe stream data model and architecture with neat diagram. (7 marks, 2026, Stream Basics)
- Explain sampling and filtering techniques in data streams. (7 marks, 2026, Sampling And Filtering)
- Analyze challenges in mining high-speed data streams. (7 marks, 2026, Stream Basics)
- Analyze the challenges of real-time sentiment analysis. (2 marks, 2025, Real Time Analytics)
Unit 4: Frequent Itemsets and Clustering – AKTU PYQs
- What is frequent itemset mining? (2 marks, 2026, Frequent Itemsets)
- Explain Apriori algorithm and its role in frequent itemset mining. (7 marks, 2026, Frequent Itemsets)
- Explain clustering techniques used in data mining. (7 marks, 2026, Clustering Techniques)
- Analyze the working of K-means clustering with an example. (7 marks, 2026, Clustering Techniques)
- How do methods like CLIQUE and ProCLUS overcome traditional clustering limitations? (2 marks, 2025, High Dimensional Clustering)
Unit 5: Introduction to Visualization and Human Vision – AKTU PYQs
- State two principles of human vision used in data visualization. (2 marks, 2026, Human Vision)
- Explain challenges involved in effective data visualization. (7 marks, 2026, Visualization Basics)
- Explain stages of data visualization. (7 marks, 2026, Visualization Basics)
- Evaluate the impact of human vision limitations on visualization design. (7 marks, 2026, Human Vision)
- What are the trade-offs between static and dynamic visualizations? (2 marks, 2025, Visualization Basics)