AD-404 Data Science - Jun 2024 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. 9 Marks
   aExplain the following.

   1. i)Pie-Chart
   2. ii)Bar Graph
   3. iii)Histogram
   How to answer

   • Define Pie-Chart with use for proportion/composition
   • Define Bar Graph with use for categorical comparison
   • Define Histogram with use for frequency distribution of continuous data
   • Mention suitable example and diagram for each
2. 5 Marks
   bExplain the concept of Conditional Probability.
   How to answer

   • Define conditional probability P(A|B)
   • Give formula P(A|B) = P(A cap B)/P(B)
   • Explain with simple example
   • Mention application in data science
3. 8 Marks
   aExplain the few data validation techniques in Advance Excel.
   How to answer

   • Define data validation in Advanced Excel
   • Explain techniques like data validation rules, drop-down lists, input messages
   • Explain error alerts, circle invalid data, custom formulas
   • Give steps/example for each
4. 6 Marks
   bExplain the following Data Analysis methods.

   1. i)Predictive
   2. ii)Prescriptive
   How to answer

   • Define predictive analysis with forecasting future outcomes
   • Define prescriptive analysis with recommending actions
   • Give techniques/examples for each
   • Differentiate use-cases
5. 7 Marks
   aExplain the role of Regression Analysis in Data Science.
   How to answer

   • Define regression analysis
   • Explain role in prediction and relationship modelling
   • Mention types like linear, logistic, multiple regression
   • Give data science applications
6. 7 Marks
   bExplain the operations of VLOOKUP and XLOOKUP for data analysis.
   How to answer

   • Explain VLOOKUP syntax and working
   • Explain XLOOKUP syntax and working
   • Compare advantages of XLOOKUP over VLOOKUP
   • Give example steps for data analysis
7. 8 Marks
   aWrite about String manipulation operations in Pandas.
   How to answer

   • Introduce Pandas string accessor .str
   • Explain operations like lower, upper, split, replace, contains, len
   • Give syntax examples
   • Mention application in data cleaning
8. 6 Marks
   bList and explain visualization tools in Python.
   How to answer

   • List tools: Matplotlib, Seaborn, Plotly, Bokeh
   • Explain features of each
   • Mention use-cases for statistical and interactive plots
   • Give example
9. 9 Marks
   aWhat is Business Intelligence? Explain the types of Business Intelligence.
   How to answer

   • Define Business Intelligence
   • Explain components/process of BI
   • Explain types e.g., descriptive, predictive, prescriptive, strategic, operational
   • Give business applications
10. 5 Marks
    bWhat is unfair discrimination? Explain.
    How to answer

    • Define unfair discrimination / bias
    • Explain sources like biased data, algorithms
    • Give examples in data science models
    • Mention mitigation
11. 7 Marks
    aWrite about Data Wrangling.
    How to answer

    • Define data wrangling / munging
    • Explain steps: discovering, structuring, cleaning, enriching, validating
    • Mention tools like Pandas, Excel
    • Give example workflow
12. 7 Marks
    bWhat is Macro? Write down the steps to create a macro in Excel.
    How to answer

    • Define macro as recorded automation in Excel
    • Explain enabling Developer tab
    • Give steps: Record Macro, perform actions, stop, run
    • Mention VBA and saving as macro-enabled workbook
13. 8 Marks
    aWhat is Correlation analysis? Explain.
    How to answer

    • Define correlation analysis and correlation coefficient
    • Explain types: positive, negative, zero; Pearson, Spearman
    • Give formula and interpretation of r values
    • Mention scatter plot example
14. 6 Marks
    bExplain the concept of Random Forest.
    How to answer

    • Define Random Forest as ensemble of decision trees
    • Explain bagging, random feature selection, voting
    • Give steps of training and prediction
    • Mention advantages and applications
15. 5 Marks
    aExplain the following in detail: Ethical Issues in Data Science
    How to answer

    • Define ethics in data science
    • Discuss privacy, consent, bias, transparency, accountability
    • Give examples of ethical violations
    • Mention best practices
16. 5 Marks
    bSecurity issues in Data Science
    How to answer

    • Define data security needs
    • Explain threats: breaches, unauthorized access, leakage
    • Discuss measures: encryption, access control, anonymization
    • Give examples
17. 4 Marks
    cStructured Data
    How to answer

    • Define structured data with schema
    • Explain characteristics: tabular, relational databases, SQL
    • Give examples vs unstructured data
    • Mention applications