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EndsemMay 20242019 patternBE AI&DS

Big Data Analytics (BDA) SPPU Endsem Question Paper May 2024

All 8 questions from this SPPU endsem paper, as printed. Read them here or open the PDF.

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Subject code
417532B
Pattern
2019 pattern
Exam
Endsem
Session
May 2024
Max. marks
70
Time
2½ Hours
Semester
VIII
Paper code
[6263]-393

Printed for: B.E. (Artificial Intelligence and Data Science)

Instructions to the candidates

  1. Answer Q1 or Q2, Q3 or Q4, Q5 or Q6, Q7 or Q8.
  2. Neat diagrams must be drawn wherever necessary.
  3. Figures to the right side indicate full marks.
  4. Use of non programmable electronic calculator is allowed.
  5. Assume suitable/standard data if necessary.

Questions

  1. Q1

    1. a)

      What are the primary methods and functions for importing and exporting data in R and how can they be utilized effectively?

      [8]
    2. b)

      How can data analysts detect and address dirty data using visualizations and statistical techniques in R and what are the implications of anomalies in datasets on decision-making processes?

      [10]

  2. Q2

    1. a)

      With the help of neat diagram explain the phases of Data Analytics life cycle.

      [8]
    2. b)

      What are the different attribute types in data analysis, and how are they categorized? How does R handle various data types, such as numeric, character, logical, and factors?

      [10]

  3. Q3

    1. a)

      Explain the following terms with respect to Exploratory data analysis. i) Data Sourcing ii) Data Cleaning iii) Univariate analysis iv) Bi-variate/Multivariate analysis

      [10]
    2. b)

      What are the essential steps in data exploration and how do they contribute to uncovering insights and patterns within a dataset?

      [7]

  4. Q4

    1. a)

      How do ensemble methods such as bagging, boosting, AdaBoost and Random Forest contribute to improving classification accuracy in machine learning models?

      [10]
    2. b)

      How does the utilization of a confusion matrix aid in the evaluation and selection of models.

      [7]

  5. Q5

    1. a)

      What are the challenges associated with visualizing big data and how do these challenges impact the effectiveness and efficiency of data analysis and decision-making processes?

      [10]
    2. b)

      How does Tableau facilitate effective data visualization and what are some advanced techniques or features within Tableau that can be utilized to create insightful and interactive visualizations for complex datasets?

      [8]

  6. Q6

    1. a)

      What are the key features and functionalities of the Google Chart API and how does it enable developers to create dynamic and interactive charts and visualizations for web applications?

      [8]
    2. b)

      What are the various types of data visualization techniques available to data analysts?

      [10]

  7. Q7

    1. a)

      In what ways does financial data analytics leverage big data technologies to drive insights, mitigate risks and enhance decision-making processes within the financial industry?

      [10]
    2. b)

      How does Apache HBase contribute to efficient data storage and retrieval in big data environments

      [7]

  8. Q8

    1. a)

      How does the Semantria tool streamline the data collection process and what features or capabilities does it offer that make it a valuable asset for businesses seeking to gather and analyze large volumes of unstructured data from various sources?

      [10]
    2. b)

      How does Mozenda serve as an effective data filtering and extraction tool? 

      [7]