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    FDS-Unit1-2 QB

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    Ram Aypn
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    FDS-Unit1-2 QB

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    Ram Aypn
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    qp

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    Copyright:

    © All Rights Reserved
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    Download as docx, pdf, or txt
    21 views3 pages

    FDS-Unit1-2 QB

    Uploaded by

    Ram Aypn
    qp

    Copyright:

    © All Rights Reserved
    Download as DOCX, PDF, TXT or read online from Scribd
    Download as docx, pdf, or txt
    You are on page 1of 3

    FDS – Question Bank – Unit1:

    Part – A:

    1. Define Big Data and Data Science.


    2. How Big Data and Data Science are related to each other?
    3. List the benefits of Big Data and Data Science.
    4. What is machine generated data?
    5. Give an example for Graph – based or network data?
    6. List the steps in the data science process.
    7. Define the following terms: Data preparation and exploration.
    8. What is cleansing data?
    9. What are outliers of data?
    10. How will you handle the missing data?
    11. What do you mean by Transforming data?
    12. Give example for multiple plots over multiple variables.
    13. What is a pareto diagram? Draw sample diagram.
    14. Write notes on Link and Brush technique or graph.
    15. What is KDD? List its iterative steps.
    16. Define Data Warehouse.
    17. What are Transactional data? Give example.
    18. Define the term “Data Characterization” with example.
    19. What is cluster analysis?
    20. List the kinds of applications are targeted by Data Mining.
    21. What are the major issues in Data Mining?
    22. How is a data warehouse different from a database? How are they similar?

    Part – B:

    1. Discuss about Facets of data in Data Science.


    2. Briefly discuss about the data science process.
    3. Explain about Exploratory data analysis.
    4. What kind of Patterns can be mined using Data Mining functionalities?
    5. Write vast notes of “kinds of data can be mined”.
    6. Explain the difference and similarity between discrimination and classification, between
    characterization and clustering, and between classification and regression.
    7. Explain the step Data Preparation - Cleansing, integrating, and transforming data of data science
    process
    Unit2:

    Part – A

    1. What is Measuring the Central Tendency of data?


    2. Define mean and median.
    3. Find the mean and median of the given data: 30, 36, 47, 50, 52, 52, 56, 60, 63, 70, 70, 110.
    4. Write about range and Quartiles.
    5. How a boxplot incorporates the five-number summary?
    6. Give example for Quantile–Quantile Plot.
    7. List and define the types of data in basic statistical data description.
    8. What are the types of variables in basic statistical data description? Define any two variables.
    9. List out the guidelines for frequency distributions with tables.
    10. What is a relative frequency distribution? Give example
    11. Define cumulative frequency distribution. Give example
    12. What do you mean by misleading graphs? Give example.
    13. Write the purpose of histogram with sample.
    14. Discuss about frequency polygon.
    15. Write notes on stem and leaf graph displays.
    16. List and draw the typical shapes for smoothed frequency polygons.
    17. Find the median for the following sets of scores:
    Set1 of five scores: 2, 8, 2, 7, 6
    Set2 of six scores: 3, 8, 9, 3, 1, 8
    18. Define variability.
    19. What is mean by range? Give example.

    Part – B:

    1. Brief about Measuring the Central Tendency and Measuring the Dispersion of Data.
    2. How will you describe data with tables? Give examples.
    3. Discuss about describe data with Graphs? Give examples.
    4. Construct and explain the frequency distributions with table using the given data

    5. GRE scores for a group of graduate school applicants are distributed as follows:
    Convert to a relative frequency distribution and cumulative frequency distribution. Explain the
    converting processes.

    6. Briefly discuss about describing data with graphics.


    7. How will you describe data with Averages?

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