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

Question Paper Code: BCSB06

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Time: 3 Hours

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M.Tech I Semester End Examinations (Regular) - January, 2019

Regulation: R18

FOUNDATIONS OF DATA SCIENCE

(CSE)

Answer ONE Question from each Unit

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All Questions Carry Equal Marks

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

Max Marks: 70

UNIT I

1. (a) What are the applications of R Programming in Real-World? Discuss in detail various stages in data science project. [7M]
   

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   (b) List out inbuilt summary functions to apply on vectors. Create vector, matrix and array data object and apply inbuilt functions on it. [7M]
2. (a) State how array indexing and subsection of an array can be done in R? Write a R script to matrix multiplication. [7M]
   (b) Describe the probability distribution in R? Enumerate the steps for data cleaning and sampling. [7M]

UNIT - II

1. (a) How to perform an ANOVA in R. Discuss the way to perform repeated measures with ancova in R with suitable example. [7M]
   

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   (b) Discuss the multicollinearity. Assume a dataset and describe the procedure for finding hidden relations among attributes in the dataset. [7M]
2. (a) 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. [7M]
   (b) Describe linear regression. What are the performance evaluation metrics in regression? How to implement regression in R? [7M]

UNIT - III

1. (a) Discuss about data model. How to create and evaluate a data model. Describe with one case study. [7M]
   

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   (b) List out different types of clustering. Write about K-means algorithm. Write a R script to cluster the mtcars dataset using KNN algorithm. [7M]
2. (a) What are the prerequisites for machine learning? Explain how is KNN different from k-means clustering? [7M]
   (b) Describe about the data model. Write any four learning techniques and in each case give the expression for weight - updating. [7M]

UNIT - IV

1. (a) Discuss about ANN. Explain how do neural networks work? [7M]
   

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   (b) Describe the limitations on the back propagation algorithm. Explain the scope to overcome these limitations [7M]
2. (a) Describe the null and alternative hypothesis with examples. What is p-value and give its impor- tance. [7M]
   (b) List out the various learning algorithms. Explain gradient descent learning algorithm . [7M]

UNIT - V

1. (a) Discuss about the residuals with respect to observed values? State a case study to show the fitted line and residuals in logistic regression. [7M]
   

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   (b) Describe KNITR. State how to produce milestone documentation using KNITR. Explain simple markdown example. [7M]
2. (a) How to make a matrix plot. Explain the procedure to partition the window to get more number of plots. [7M]
   (b) List out the different plots with relevant packages to explore and summarize the multi-object plots in R. [7M]

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