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

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Question Paper Code: BCSB06

M.Tech I Semester End Examinations (Supplementary) - May, 2019

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Regulation: R18

DATA SCIENCE

(CSE)

Time: 3 Hours Max Marks: 70

Answer ONE Question from each Unit

All Questions Carry Equal Marks

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All parts of the question must be answered in one place only

UNIT I

1. 1. Write the advantages of R programming? Explain various features of R language with necessary examples? [7M]
   2. Describe data science process in detail. List out loops in R language with suitable examples. [7M]
2. 1. Write various data types in R with necessary examples? Write about all summary statistics in R. [7M]

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   2. How to make data frames. Explain attach() and detach () function with suitable examples. [7M]

UNIT - II

1. 1. Discuss about NoSQL. Explain the features of NoSQL? [7M]
   2. Write a R script which include relevant packages and procedure to access .CSV and .exl files. Elaborate with necessary example. [7M]

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2. 1. Discuss heteroscedasticity in regression. How to identify heteroscedasticity? Explain the methods for resolving it. [7M]
   2. How to perform correlation analysis between multiple variables in R. Write a R script to get a linear equation y=mx+c from the heart weight and body weight in cats dataset. [7M]

UNIT - III

1. 1. Describe about the data model. Write any four learning techniques and in each case give the expression for weight - updating. [7M]
   2. Describe the perspectives and issues in machine learning, explain with an example. [7M]

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2. 1. State Bayes theorem. Discuss how Bayesian classification works and provide necessary example. [7M]
   2. List out different types of clustering? Explain about density based clustering with necessary example. [7M]

UNIT - IV

1. 1. Describe the null and alternative hypothesis with examples. What is p-value and give its importance. [7M]

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   2. List and explain the various activation functions used in ANN. Explain the difference between neuro computing and conventional computing. [7M]
2. 1. Describe the basic structure of back propagation. Explain steps involved in back propagation algorithm. [7M]
   2. Discuss quasi-Newton learning algorithm. Compare and contrast learning algorithms in neural network. [7M]

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UNIT - V

1. 1. How to produce effective presentations? Explain the procedure of presenting results to the sponsor, presenting model to end users and presenting work to data scientists. [7M]
   2. Summarize the importance of visualization in different types of data in exploration in data analysis. [7M]
2. 1. Discuss about the residuals with respect to observed values? State a case study to show the fitted line and residuals in logistic regression. [7M]
   2. How to make a matrix plot. Explain the procedure to partition the window to get more number of plots. [7M]

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