Hall Ticket No Question Paper Code: BCS001
INSTITUTE OF AERONAUTICAL ENGINEERING
(Autonomous)
M.Tech I Semester End Examinations (Supplementary) January, 2019
Regulation: IARE-R16
FOUNDATIONS OF DATA SCIENCE
Time: 3 Hours Max Marks: 70
Answer ONE Question from each Unit
All Questions Carry Equal Marks
All parts of the question must be answered in one place only
UNIT I
1. Draw the life cycle of data science project and summarize the stages of data science project.

Describe how to explore data in R. Explain graphics and visualization in spotting problems.

2. List the different R functions to read and write the data from disk and R object. Write R script
to choose the character data dynamically from user.
Explain and write the appropriate statements in R for the following operations.
• Input as CSV File(input.csv)
• Reading a CSV file
• Apply different analysis on the file
• Writing into a CSV File
UNIT II
3. Differentiate SQL and No SQL databases in detail. Give example of XML data extraction and
operations using R.
What is JSON file? Explain the input, output operations of JSON file using R.
4. Compute covariance matrix and correlation matrix for the four numerical attributes. Interpret
the statistical findings to know more about hidden nature in data.
Summarize multiple regression in R and create equation for regression model.
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UNIT III
5. How to predict whether an email is a spam and should be delivered to the junk folder. Suggest
suitable data model.
Calculate the Jaccard coefficient for the given data
p 1 0 0 0 0 0 0 0 0 0
q 0 0 0 0 0 0 1 0 0 1.
6. Outline about the learning of a model? Write any four learning techniques and in each case give
the expression for weight- updating.
Write short notes on Hierarchical clustering with hclust().
UNIT IV
7. Give the basic structure of neural network and different artificial neural network with real time
examples.
Discuss the difference of error in two hypotheses. Differentiate the MAP (maximum a posteriori)
and ML (maximum likelihood) hypothesis. Give an example of a scenario in which a MAP
hypothesis is preferable to an ML hypothesis.
8. Describe the prediction model in terms of the following measures for best fit: Residual standard
error, Multiple R-squared, F-statistic, p-value
Compare the learning algorithms with example in terms of problem nature, accuracy and error
rate.
UNIT V
9. Write R script to plot a data frame having: {df1:{red,green,blue,pink,black} df2:
using relevant plot.
Generalize the graphical analysis in data analysis. List the various plots in R and explain in
detail.
10. How would you get the multiple plots in single window? Plot the regression model along with
residuals.Write a R script for creating a boxplot of iris sepal length attribute.
Elaborate how to export a graph using graphics parameters.How to export the text data to plot
with example.
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