DAV PYQ 2024-25 AKTU Question Paper

AKTU · BTECH · Semester 5 · Introduction to Data Analytics and Visualization · Session 2024-25 · PYQ

AKTU Introduction to Data Analytics and Visualization previous year question paper 2024-25 for B.Tech Semester 5. Covers Introduction to Data Analytics…

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Introduction to Data Analytics and Visualization AKTU syllabus

  1. Unit 1: Introduction to Data Analytics
  2. Unit 2: Data Analysis
  3. Unit 3: Mining Data Streams
  4. Unit 4: Frequent Itemsets and Clustering
  5. Unit 5: Introduction to Visualization and Human Vision

Questions in Introduction to Data Analytics and Visualization AKTU PYQ 2024-25

  1. Q1a. What are the challenges and solutions associated with scaling analytics in modern organizations? (2 marks, 2024-25)
  2. Q1b. How do modern tools bridge the gap between analysis and reporting? (2 marks, 2024-25)
  3. Q1c. Compare and contrast Bayesian modeling with neural networks in terms of their applications. (2 marks, 2024-25)
  4. Q1d. How do linear and nonlinear dynamics influence the accuracy of predictions? (2 marks, 2024-25)
  5. Q1e. Analyze the challenges of real-time sentiment analysis. (2 marks, 2024-25)
  6. Q1f. How do methods like CLIQUE and ProCLUS overcome traditional clustering limitations? (2 marks, 2024-25)
  7. Q1g. What are the trade-offs between static and dynamic visualizations? (2 marks, 2024-25)
  8. Q2a. Describe the key characteristics of Big Data. How do these characteristics necessitate the development of advanced analytics platforms? (7 marks, 2024-25)
  9. Q2b. Explain the concept of rule induction in data mining. How is it used to derive meaningful patterns from datasets? (7 marks, 2024-25)
  10. Q2c. Describe the concept of decaying windows in stream analytics. How does it help in managing memory and computational resources? (7 marks, 2024-25)
  11. Q2d. Compare and contrast hierarchical clustering and K-means clustering. What are the advantages and disadvantages of each? (7 marks, 2024-25)
  12. Q2e. Discuss the importance of figure captions in visual interfaces. How do they enhance the interpretability of data visualizations? (7 marks, 2024-25)
  13. Q3a. How has the need for data analytics evolved with the advent of IoT and real-time systems? Provide relevant examples. (7 marks, 2024-25)
  14. Q3b. Define the term 'analytic lifecycle.' How do the roles of stakeholders vary across its different phases? (7 marks, 2024-25)
  15. Q4a. Critically analyze the challenges of multivariate analysis in high-dimensional datasets. How can these challenges be addressed? (7 marks, 2024-25)
  16. Q4b. How do stochastic search methods differ from deterministic optimization techniques? Provide use cases for each. (7 marks, 2024-25)
  17. Q5a. Discuss the role of Real-Time Analytics Platforms (RTAP) in modern enterprises. Provide a case study to support your explanation. (7 marks, 2024-25)
  18. Q5b. Describe the steps involved in building a real-time stock market prediction system using stream analytics. What are the key considerations? (7 marks, 2024-25)
  19. Q6a. What is parallelism in clustering algorithms? How does it improve performance in handling large datasets? (7 marks, 2024-25)
  20. Q6b. How is clustering applied to streaming data? Discuss the modifications needed in traditional clustering algorithms for stream analytics. (7 marks, 2024-25)
  21. Q7a. Explain the concept of navigation links in data visualization. How do they improve user interaction with complex datasets? (7 marks, 2024-25)
  22. Q7b. Explain the role of human vision in designing effective data visualizations. How do space and time limitations influence this process? (7 marks, 2024-25)

AKTU paper codes: BCDS501, KDS501

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