Exam Details
| Subject | data analytics | |
| Paper | ||
| Exam / Course | b.tech. - information technology | |
| Department | ||
| Organization | SETHU INSTITUTE OF TECHNOLOGY | |
| Position | ||
| Exam Date | May, 2017 | |
| City, State | tamil nadu, pulloor |
Question Paper
Reg. No.
B.E. B.Tech. DEGREE EXAMINATION, MAY 2017
Elective
Information Technology
01UIT909 DATA ANALYTICS
(Regulation 2013)
Duration: Three hours Maximum: 100 Marks
Answer ALL Questions
PART A (10 x 2 20 Marks)
1. State the significance of resampling.
2. State the challenges of conventional systems in handling big data.
3. Differentiate between examples and instances from rule induction.
4. What is Language bias?
5. State the definition of data stream.
6. State the different filtering schemes used in streams.
7. What is the purpose of Apriori Algorithm?
8. Compare the Agglomerative and Divisive Hierarchical Clustering.
9. State the general form of Map and Reduce function in Hadoop Map Reduce.
10. What is the use of Hive Metastore?
Question Paper Code: 31889
2
31889
PART B x 16 80 Marks)
11. Analyze sampling distributions and explain types sampling.
Or
Discuss about statistical inference in detail.
12. What are the various inference in Bayesian network? Explain in detail.
Or
Write a short notes on fuzzy decision trees.
Explain the fuzzy decision tree algorithm.
13. Sketch the stream data model diagram and explain in detail.
Or
Describe in detail about estimating moments.
14. Write a short note on hierarchical methods in the clustering.
List the hierarchical methods in the clustering techniques and explain its type.
Or
Discuss about Clustering High-Dimensional Data.
Distinguish between CLIQUE and PROCLUS method.
15. Illustrate the basics of Hadoop Distributed File System with its architecture.
Or
Evaluate the techniques of visual data analysis.
B.E. B.Tech. DEGREE EXAMINATION, MAY 2017
Elective
Information Technology
01UIT909 DATA ANALYTICS
(Regulation 2013)
Duration: Three hours Maximum: 100 Marks
Answer ALL Questions
PART A (10 x 2 20 Marks)
1. State the significance of resampling.
2. State the challenges of conventional systems in handling big data.
3. Differentiate between examples and instances from rule induction.
4. What is Language bias?
5. State the definition of data stream.
6. State the different filtering schemes used in streams.
7. What is the purpose of Apriori Algorithm?
8. Compare the Agglomerative and Divisive Hierarchical Clustering.
9. State the general form of Map and Reduce function in Hadoop Map Reduce.
10. What is the use of Hive Metastore?
Question Paper Code: 31889
2
31889
PART B x 16 80 Marks)
11. Analyze sampling distributions and explain types sampling.
Or
Discuss about statistical inference in detail.
12. What are the various inference in Bayesian network? Explain in detail.
Or
Write a short notes on fuzzy decision trees.
Explain the fuzzy decision tree algorithm.
13. Sketch the stream data model diagram and explain in detail.
Or
Describe in detail about estimating moments.
14. Write a short note on hierarchical methods in the clustering.
List the hierarchical methods in the clustering techniques and explain its type.
Or
Discuss about Clustering High-Dimensional Data.
Distinguish between CLIQUE and PROCLUS method.
15. Illustrate the basics of Hadoop Distributed File System with its architecture.
Or
Evaluate the techniques of visual data analysis.
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- analysis and design of algorithms
- applied statistics and queuing networks
- building enterprise application
- coding and information theory
- compiler design
- computer networks
- computer organization
- cryptography and network security
- data analytics
- data mining concepts and techniques
- database systems
- discrete mathematics
- embedded computing systems
- environmental science and engineering
- graphics with opengl
- high performance networks
- information storage management
- java programming
- management information systems
- microprocessors and microcontrollers
- mobile communication systems
- object oriented analysis and design
- object oriented programming
- object oriented software engineering methodologies
- principles of operating systems
- principles of software testing
- programming with data structures
- project management and finance
- qualitative and quantitative aptitude
- service oriented architecture
- software architecture
- transforms and partial differential equations
- user interface framework development
- web technology
- wireless communication