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AKTU Data Analytics and Visualization Important Questions

The questions AKTU asks again and again in Data Analytics and Visualization, found by comparing every question in 4 previous year papers from 2022-23 to 2025-26 (subject codes BCDS501, KDS501).

How this list is made: we compared all 94 questions in these papers and grouped the ones that ask about the same thing. Each item shows how many papers it appeared in, which years, and other ways it was worded. Topics that come back often are a strong hint, not a guarantee, so study your full syllabus too.

Most repeated long questions

From Sections B and C (7 or 10 marks, depending on the year). Unit numbers come from Section C, where each question covers one unit.

  1. Demonstrate any algorithm for counting oneness in a window.
    Asked in 2 papersUnit 2 · 2023-24 KDS501 · 2022-23 KDS501
    Also asked as:
    • Explain any one algorithm for counting oneness in a window.
  2. Describe all the phases of data analytics life cycle.
    Asked in 2 papersUnit 1 · 2023-24 KDS501 · 2022-23 KDS501
    Also asked as:
    • Explain the phase of Data analytics life cycle in detail.
  3. Discuss k-means clustering? Explain its working. Write k-means algorithm for partitioning.
    Asked in 2 papersUnit 3 · 2023-24 KDS501 · 2022-23 KDS501
    Also asked as:
    • What do you mean by k-means clustering? How does the k-means algorithm work? Write k-meansalgorithm for partitioning.
  4. Discuss various classification of visualization techniques.
    Asked in 2 papers2023-24 KDS501 · 2022-23 KDS501
    Also asked as:
    • Discuss various visualization techniques with suitable examples.
  5. Draw and discuss the architecture of HDFS in detail.
    Asked in 2 papersUnit 5 · 2023-24 KDS501 · 2022-23 KDS501
    Also asked as:
    • Define HDFS. Discuss the HDFS architecture and HDFS commands in brief.

Most repeated short questions (2 marks)

From Section A. Learn a crisp definition plus one example or line of explanation for each.

  1. Discuss Sentiment Analysis.
    Asked in 2 papers2023-24 KDS501 · 2022-23 KDS501
    Also asked as:
    • What is sentiment analysis?
  2. Explain the need of Data Analytics.
    Asked in 2 papers2023-24 KDS501 · 2022-23 KDS501
    Also asked as:
    • Why there is need of data analytics?
  3. List any two visualization tools.
    Asked in 2 papers2023-24 KDS501 · 2022-23 KDS501
    Also asked as:
    • Write any two visualization tools.
  4. What are the features of Big Data?
    Asked in 2 papers2023-24 KDS501 · 2022-23 KDS501
    Also asked as:
    • List the features of big data analytics platform.

Unit-wise questions from recent papers

Section C questions from the latest papers (2025-26 BCDS501, 2024-25 BCDS501, 2023-24 KDS501), sorted by unit. Use these to practise one unit at a time.

Unit 1
  • Explain Data Analytics lifecycle with a neat diagram and example. (2025-26, BCDS501)
  • Analyze the role of data analytics in modern organizations. (2025-26, BCDS501)
  • How has the need for data analytics evolved with the advent of IoT and real-time systems? Provide relevant examples. (2024-25, BCDS501)
  • Define the term 'analytic lifecycle.' How do the roles of stakeholders vary across its different phases? (2024-25, BCDS501)
  • Explain the phase of Data analytics life cycle in detail. (2023-24, KDS501)
  • Write short note on the following- 1 i. Modern Data analytics tools ii. Application of data analytics (2023-24, KDS501)
Unit 2
  • Explain Bayesian modeling and in data analytics. (2025-26, BCDS501)
  • Compare regression modeling and Bayesian modeling. (2025-26, BCDS501)
  • Critically analyze the challenges of multivariate analysis in high- 2 K , dimensional datasets. How can these challenges be addressed? (2024-25, BCDS501)
  • How do stochastic search methods differ from deterministic 2 K 3 , optimization techniques? Provide use cases for each. (2024-25, BCDS501)
  • Demonstrate any algorithm for counting oneness in a window. (2023-24, KDS501)
  • What are the various stochastic search methods. Explain in detail with suitable example. (2023-24, KDS501)
Unit 3
  • Explain sampling and filtering techniques in data streams. (2025-26, BCDS501)
  • Analyze challenges in mining high-speed data streams. (2025-26, BCDS501)
  • Discuss the role of Real-Time Analytics Platforms (RTAP) in modern enterprises. Provide a case study to support your explanation. (2024-25, BCDS501)
  • Describe the steps involved in building a real-time stock market 3 K prediction system using stream analytics. What are the key considerations? (2024-25, BCDS501)
  • With the help of suitable example explain prediction error in classification and regression. (2023-24, KDS501)
  • Discuss k-means clustering? Explain its working. Write k-means algorithm for partitioning. (2023-24, KDS501)
Unit 4
  • Explain clustering techniques used in data mining. (2025-26, BCDS501)
  • Analyze the working of K-means clustering with an example. (2025-26, BCDS501)
  • What is parallelism in clustering algorithms? How does it improve 4 K , performance in handling large datasets? (2024-25, BCDS501)
  • How is clustering applied to streaming data? Discuss the modifications 4 K 2 , needed in traditional clustering algorithms for stream analytics. (2024-25, BCDS501)
  • Discuss as a case study – Real time sentiment Analysis. (2023-24, KDS501)
  • Explain the following- 4 i. Apriori Algorithm ii. Market based modeling (2023-24, KDS501)
Unit 5
  • Explain stages of data visualization. (2025-26, BCDS501)
  • Evaluate the impact of human vision limitations on visualization design. 5 (2025-26, BCDS501)
  • Explain the concept of navigation links in data visualization. How do they improve user interaction with complex datasets? K (2024-25, BCDS501)
  • Explain the role of human vision in designing effective data visualizations. How do space and time limitations influence this process? (2024-25, BCDS501)
  • Draw and discuss the architecture of HDFS in detail. (2023-24, KDS501)
  • What are the approaches to integrate the human in data exploration process to realize different types of approaches to visual data mining? (2023-24, KDS501)

Papers used for this list

How to use these questions

  1. Start with the questions asked in the most papers, and make short notes for each.
  2. Write 2–3 long answers by hand with a timer (about 12–15 minutes each), then compare them with your textbook.
  3. Revise all the 2-mark questions the night before the exam.
  4. Finish with one full paper from the list above, timed.

Full plan with AI prompts: How to prepare for AKTU exams using previous year papers. After results, check your marks with the AKTU CGPA calculator.

Frequently asked questions

Are these AKTU Data Analytics and Visualization questions guaranteed to come in the exam?
No. They are the questions that repeated most in past papers, which makes them a strong hint, but AKTU can ask anything from the syllabus. Use this list to prioritise, not to skip units.

How was this important questions list made?
We compared all 94 questions from 4 AKTU Data Analytics and Visualization papers (2022-23 to 2025-26) and grouped questions that ask about the same topic, then counted how many papers each appeared in.

Where can I download AKTU Data Analytics and Visualization previous year papers?
Every paper used here is on LearnAIFast with all questions as text and a link to the original PDF. See the list of papers above.

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