Download JNTUH M-Tech I Semester 2019 May Data Science Question Paper

Download JNTU Hyderabad (Jawaharlal Nehru Technological University Hyderabad) M Tech (Master of Engineering) I Semester 2019 May Data Science Question Paper

Hall Ticket No Question Paper Code: BCSB06
M.Tech I Semester End Examinations (Supplementary) ? May, 2019
Regulation: .?R18
DATA SCIENCE
Time: 3 Hours (CSE) 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
1. (a)
3. (a)
(b)
4. (a)
(b)
5. (a)
(b)
6. (a)
UNIT ? I
Write the advantages of R programming? Explain various features of R language with necessary
examples? [7M]
Describe data science process in detail. List out loops in R language with suitable examples.
[7M]
Write various data types in R with necessary examples? Write about all summary statistics in
R. [7M]
How to make data frames. Explain attach() and detach () function with suitable examples. [7M]
UNIT ? II
Discuss about NOSQL. Explain the features of NOSQL? [7M]
Write a R script which include relevant packages and procedure to access .CSV and .eXl ?les.
Elaborate with necessary example. [7M]
Discuss heteroscedasticity in regression. How to identify heteroscedasticity? Explain the methods
for resolving it. [7M]
How to perform correlation analysis between multiple variables in R. Write a R script to get a
linear equation yzmx?l?c from the heart weight and body weight in cats dataset. [7 M]
UNIT ? III
Describe about the data model. Write any four learning techniques and in each case give the
expression for weight ? updating. [7M]
Describe the perspectives and issues in machine learning, explain with an example. [7M]
State Bayes theorem. Discuss how Bayesian classi?cation works and provide necessary example.
[7M]
List out different types of clustering? Explain about density based clustering With necessary
example. [7M]
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10.
(a)
(b)
(a)
(b)
(8?)
UNIT ? IV
Describe the null and alternative hypothesis With examples. What is p?Value and give its impor?
tance. [7M]
List and explain the various activation functions used in ANN. Explain the difference between
neuro computing and conventional computing. [7M]
Describe the basic structure of back propagation. Explain steps involved in back propagation
algorithm. [7M]
Discuss quaSi?Newton learning algorithm. Compare and contrast learning algorithms in neural
network. [7M]
UNIT ? V
HOW to produce effective presentations? Explain the procedure of presenting results to the
sponsor7 presenting model to end users and presenting work to data scientists. [7 M]
Summarize the importance of Visualization in different types of data in exploration in data anal?
ysis.
[7M]
Discuss about the residuals with respect to Observed values? State a case study to show the ?tted
line and residuals in logistic regression. [7M]
How to make a matrix plot. Explain the procedure to partition the window to get more number
of plots. [7M]
?00000?
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