AD-404 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 differences between structured and unstructured data with examples.
   How to answer

   • Define structured data with characteristics and examples
   • Define unstructured data with characteristics and examples
   • Compare on format, storage, processing and analysis
   • Give examples for each type
2. 7 Marks
   bDraw two graphical representations (e.g., Pie Chart, Bar Graph, Histogram - whichever fits) of data using the following sample dataset:

   How to answer

   • Present given branch-wise dataset in tabular form
   • Draw first graphical representation e.g. Bar Graph / Pie Chart with labels
   • Draw second graphical representation e.g. Histogram / Pie Chart with labels
   • Mention which representation fits and interpretation
3. 7 Marks
   aCalculate and interpret the mean, median and mode for the given quantitative data: 5, 8, 12, 15, 20, 20, 25.
   How to answer

   • Calculate mean using sum/n formula
   • Calculate median as middle value of ordered data
   • Identify mode as most frequent value 20
   • Interpret skewness and central tendency
4. 7 Marks
   bExplain the importance of unstructured data and its use in healthcare, finance and social media.
   How to answer

   • Define unstructured data
   • Explain importance in data science
   • Give use in healthcare, finance and social media
   • Mention tools for processing unstructured data
5. 7 Marks
   aExplain how t-SNE is used to visualize high-dimensional data with an unstructured data example.
   How to answer

   • Define t-SNE and need for dimensionality reduction
   • Outline steps: similarity, low-dimensional embedding, KL divergence minimization
   • Give unstructured data example e.g. text/images visualization
   • Mention interpretation of clusters
6. 7 Marks
   bExplain data validation in Excel and its role in improving data accuracy and reducing errors.
   How to answer

   • Define data validation in Excel
   • List validation types: list, range, custom, input message
   • Explain role in accuracy and error reduction
   • Give example steps to apply validation
7. 7 Marks
   aExplain the difference between Type I and Type II errors in multiple hypothesis testing with an example.
   How to answer

   • Define Type I error (false positive) and Type II error (false negative)
   • Compare on definition, cause and consequence
   • Explain issue in multiple hypothesis testing
   • Give example illustrating both errors
8. 7 Marks
   bExplain logistic regression with an example of binary classification, such as predicting loan approval (Yes/No) using applicant features.
   How to answer

   • Define logistic regression and sigmoid function
   • Explain binary classification setup for loan approval Yes/No
   • Outline training with applicant features and interpretation
   • Mention evaluation of classification
9. 7 Marks
   aExplain how SUMIFS, INDEX + MATCH and Dynamic Array Formulas can be used for customer segmentation in Excel.
   How to answer

   • Explain SUMIFS for conditional aggregation by segment
   • Explain INDEX + MATCH for lookup of customer attributes
   • Explain Dynamic Array formulas e.g. FILTER, SORT, UNIQUE
   • Show combined use for customer segmentation in Excel
10. 7 Marks
    bExplain the structure and features of a Pandas DataFrame. How is it different from a NumPy array?
    How to answer

    • Define Pandas DataFrame structure: rows, columns, index
    • List features: labeled axes, heterogeneous types, handling missing data
    • Compare with NumPy array on dimensionality and typing
    • Give example operations
11. 7 Marks
    aIllustrate how to create pie charts, bar charts, histograms, and scatter plots in Python using matplotlib or seaborn.
    How to answer

    • Mention matplotlib/seaborn functions for each plot
    • Show code for pie chart and bar chart
    • Show code for histogram and scatter plot
    • Mention customization: labels, title, display
12. 7 Marks
    bWrite the logic to perform string operations in Pandas and apply regular expressions to extract specific patterns.
    How to answer

    • Explain Pandas string accessor str methods
    • Show logic for cleaning, splitting, case conversion
    • Apply regular expressions with extract, contains, match
    • Give example to extract specific pattern
13. 7 Marks
    aDefine Business Intelligence (BI). Explain its types and write the logic of how BI supports strategic decision-making.
    How to answer

    • Define Business Intelligence (BI)
    • Explain its types e.g. descriptive, predictive, prescriptive
    • Explain BI architecture / workflow for decision-making
    • Show how BI supports strategic decisions with example
14. 7 Marks
    bCompare any two modern Business Intelligence tools based on usability, key features, and data handling capabilities.
    How to answer

    • Select two modern BI tools e.g. Power BI, Tableau
    • Compare on usability and interface
    • Compare on key features and visualization
    • Compare on data handling capabilities
15. 14 Marks
    Write short notes on any two of the following:

    1. a)Reinforcing human biases
    2. b)Measures of Variability of Quantitative Data
    3. c)Pivot table and Scenario manager
    4. d)Maximum Likelihood Estimation (MLE)
    How to answer

    • Select any two topics from a-d
    • Give definition and key concepts for each
    • Explain with example / formula / use-case
    • Conclude with significance in data science