Hall Ticket No Question Paper Code: BCSB06
INSTITUTE OF AERONAUTICAL ENGINEERING
(Autonomous)
M.Tech I Semester End Examinations (Regular) January, 2019
Regulation: IARE-R18
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. What are the applications of R Programming in Real-World? Discuss in detail various stages in
data science project.
List out inbuilt summary functions to apply on vectors. Create vector, matrix and array data
object and apply inbuilt functions on it.
2. State how array indexing and subsection of an array can be done in Write a R script to matrix
multiplication.
Describe the probability distribution in Enumerate the steps for data cleaning and sampling.

UNIT II
3. How to perform an ANOVA in R. Discuss the way to perform repeated measures with ancova in
R with suitable example.
Discuss the multicollinearity. Assume a dataset and describe the procedure for finding hidden
relations among attributes in the dataset.
4. How to perform correlation analysis between multiple variables in R. Write a R script to get a
linear equation y=mx+c for the heart weight and body weight in cats dataset.
Describe linear regression. What are the performance evaluation metrics in regression? How to
implement regression in
UNIT III
5. Discuss about data model. How to create and evaluate a data model. Describe with one case
study.
List out different types of clustering. Write about means algorithm. Write a R script to
cluster the mtcars dataset using KNN algorithm.
6. What are the prerequisites for machine learning? Explain how is KNN different from k-means
clustering?
Describe about the data model. Write any four learning techniques and in each case give the
expression for weight updating.
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UNIT IV
7. Discuss about ANN. Explain how do neural networks work?
Describe the limitations on the back propagation algorithm. Explain the scope to overcome these
limitations
8. Describe the null and alternative hypothesis with examples. What is p-value and give its importance.

List out the various learning algorithms. Explain gradient descent learning algorithm .
UNIT V
9. Discuss about the residuals with respect to observed values? State a case study to show the fitted
line and residuals in logistic regression.
Describe KNITR. State how to produce milestone documentation using KNITR. Explain simple
markdown example.
10. How to make a matrix plot. Explain the procedure to partition the window to get more number
of plots.
List out the different plots with relevant packages to explore and summarize the multi-object
plots in R.