Exam Details
| Subject | foundations of data science | |
| Paper | ||
| Exam / Course | m.tech | |
| Department | ||
| Organization | Institute Of Aeronautical Engineering | |
| Position | ||
| Exam Date | July, 2017 | |
| City, State | telangana, hyderabad |
Question Paper
Hall Ticket No Question Paper Code: BCS001
INSTITUTE OF AERONAUTICAL ENGINEERING
(Autonomous)
M.Tech I Semester End Examinations (Supplementary) July, 2017
Regulation: IARE-R16
FOUNDATIONS OF DATA SCIENCES
(Computer Science and Engineering
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. Discuss the stages of a data science project.
What is difference between data frame and a matrix in
2. Write R script to check whether the given number is prime or not?
Discuss the issues to be considered in writing out a data frame to a text file.
UNIT II
3. What are the benefits of NoSQL over RDBMS.
State different ways to access different types of data files? Discuss the relevant packages and
methods to access .csv, exl files.
4. Distinguish simple and multiple regression analysis and its applications working with numerical
and categorical data?
Describe the functions used in R for Correlation analysis and Covariance analysis with examples.
UNIT III
5. Discuss some common classification methods
Describe the process to create and evaluate the data model for the given data. To predict whether
an email is a spam and should be delivered to junk folder. Suggest some data model.
6. Discuss k-means algorithm with a suitable example.
Describe the limitations of the perception model. How to create and evaluate a data model?
Describe with one case study.
UNIT IV
7. Give the basic structure of neural network and different types of ANN with real time examples.
Page 1 of 2
Compare the learning algorithms with example in terms of problem nature, accuracy and error
rate.
8. State different types of learning algorithms with suitable example. Elaborate lazy learning algorithms
Discuss the difference of error in two hypotheses. Differentiate the MAP and ML hypothesis.
UNIT V
9. What is knitr? How to produce milestone documentation using knitr with an example for Markdown?
How to write effective comments in R to generate effective documentation
10. Generalize the graphical analysis in data analysis? List the various plots in R and explain in
detail.
List out different plots with relevant package to explore and summarize the numerical text data
in R.
INSTITUTE OF AERONAUTICAL ENGINEERING
(Autonomous)
M.Tech I Semester End Examinations (Supplementary) July, 2017
Regulation: IARE-R16
FOUNDATIONS OF DATA SCIENCES
(Computer Science and Engineering
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. Discuss the stages of a data science project.
What is difference between data frame and a matrix in
2. Write R script to check whether the given number is prime or not?
Discuss the issues to be considered in writing out a data frame to a text file.
UNIT II
3. What are the benefits of NoSQL over RDBMS.
State different ways to access different types of data files? Discuss the relevant packages and
methods to access .csv, exl files.
4. Distinguish simple and multiple regression analysis and its applications working with numerical
and categorical data?
Describe the functions used in R for Correlation analysis and Covariance analysis with examples.
UNIT III
5. Discuss some common classification methods
Describe the process to create and evaluate the data model for the given data. To predict whether
an email is a spam and should be delivered to junk folder. Suggest some data model.
6. Discuss k-means algorithm with a suitable example.
Describe the limitations of the perception model. How to create and evaluate a data model?
Describe with one case study.
UNIT IV
7. Give the basic structure of neural network and different types of ANN with real time examples.
Page 1 of 2
Compare the learning algorithms with example in terms of problem nature, accuracy and error
rate.
8. State different types of learning algorithms with suitable example. Elaborate lazy learning algorithms
Discuss the difference of error in two hypotheses. Differentiate the MAP and ML hypothesis.
UNIT V
9. What is knitr? How to produce milestone documentation using knitr with an example for Markdown?
How to write effective comments in R to generate effective documentation
10. Generalize the graphical analysis in data analysis? List the various plots in R and explain in
detail.
List out different plots with relevant package to explore and summarize the numerical text data
in R.
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