IT-702-A Data Science - Jun 2025 Question Paper

Under each question, How to answer lists the points an answer should cover, outlined by CampusPrep from the syllabus. Check the details against your textbook.

1. 7 Marks
   aExplain the types of data in details.
   How to answer

   • Define data and its role in data science
   • Explain structured, unstructured, semi-structured data
   • Explain qualitative vs quantitative and nominal, ordinal, interval, ratio scales
   • Give examples for each type
2. 7 Marks
   bDiscuss the various sources of data.
   How to answer

   • Define data sources concept
   • Discuss primary vs secondary sources
   • Explain internal, external, sensor, web, social media, transactional sources
   • Give examples and relevance to data science
3. 7 Marks
   aDescribe the data storage and management in brief.
   How to answer

   • Define data storage and management need
   • Describe databases, data warehouses, data lakes
   • Brief on distributed storage and DBMS role
   • Summarize management tasks like security and metadata
4. 7 Marks
   bExplain the characterization of big data.
   How to answer

   • Define big data
   • Explain Volume, Velocity, Variety
   • Explain Veracity, Value and Variability
   • Give examples for each characteristic
5. 7 Marks
   aDescribe the additional clustering algorithm with example.
   How to answer

   • Define clustering and need for additional clustering algorithm
   • Explain algorithm steps
   • Illustrate with example dataset
   • Mention applications and limitations
6. 7 Marks
   bHow the text can be analyze? Also explain text analysis steps.
   How to answer

   • Define text analysis and its need
   • Explain how text can be analyzed
   • List and explain steps: collection, preprocessing, mining, evaluation
   • Give example application
7. 7 Marks
   aDistinguish between Hadoop and traditional database.
   How to answer

   • Define Hadoop and traditional RDBMS
   • Compare on architecture, scalability, data type, cost
   • Compare on processing paradigm and fault tolerance
   • Conclude with use-case suitability
8. 7 Marks
   bExplain various components of Hive.
   How to answer

   • Define Hive and its role in Hadoop ecosystem
   • Explain metastore, driver, compiler, execution engine
   • Explain HiveServer, HiveQL and integration with HDFS
   • Give example query flow
9. 7 Marks
   aExplain the key components of HBase.
   How to answer

   • Define HBase as NoSQL column-oriented store
   • Explain HMaster, RegionServer, Regions, ZooKeeper
   • Explain HFile, WAL and data model
   • Mention use cases
10. 7 Marks
    bHow is scoop different from flume.
    How to answer

    • Define Sqoop and Flume purposes
    • Compare data sources: structured vs streaming/log data
    • Compare architecture and working
    • Conclude with use-case difference
11. 7 Marks
    aDescribe the Hadoop core components in brief.
    How to answer

    • Define Hadoop core components overview
    • Explain HDFS for storage
    • Explain MapReduce/YARN for processing and resource management
    • Mention interaction between components
12. 7 Marks
    bExplain the basic architecture of HDFS.
    How to answer

    • Define HDFS and design goals
    • Explain NameNode, DataNode, blocks and replication
    • Explain read/write architecture flow
    • Mention fault tolerance and rack awareness
13. 7 Marks
    aExplain the data visualization basics.
    How to answer

    • Define data visualization and its importance
    • Explain types of charts and selection criteria
    • Explain principles of effective visualization
    • Give examples of tools
14. 7 Marks
    bDescribe the operationalizing an analytics project.
    How to answer

    • Define operationalizing analytics project
    • Explain deployment, monitoring and governance steps
    • Explain integration with business processes
    • Mention challenges and best practices
15. 7 Marks
    aCompare R and Python programming languages for predictive modeling.
    How to answer

    • Define predictive modeling context
    • Compare on libraries, syntax, performance and community
    • Compare on visualization and ML support
    • Conclude suitability for data science tasks
16. 7 Marks
    bWrite short note on :

    1. i)YARN
    2. ii)Decision tree
    How to answer

    • Define YARN and its need in Hadoop 2.x
    • Explain YARN architecture: ResourceManager, NodeManager
    • Define Decision tree, splitting and pruning
    • Give applications of both