Exam Details
Subject | data analytics with r | |
Paper | ||
Exam / Course | mca(integrated) | |
Department | ||
Organization | Gujarat Technological University | |
Position | ||
Exam Date | May, 2017 | |
City, State | gujarat, ahmedabad |
Question Paper
1
Seat No.: Enrolment
GUJARAT TECHNOLOGICAL UNIVERSITY
MCA Integrated SEMESTER-VIII • EXAMINATION SUMMER 2017
Subject Code: 4480602 Date: 01/05/ 2017
Subject Name: Data Analytics with R
Time: 10.30 am to 01.00 pm Total Marks: 70
Instructions:
1. Attempt all questions.
2. Make suitable assumptions wherever necessary.
3. Figures to the right indicate full marks.
Q.1
What is data analytics? Explain the general categories of data analytics that are distinguished by the results they produce.
07
Explain the following packages (ii)Accessing built in datasets
07
Q.2
Explain data type in detail with example.
07
Explain classification of data in detail.
07
OR
What is data frame? Explain the functionally of data frame with example.
07
Q.3
i. What is variable? How to assign a value to a variable and removing variable in
ii. Explain list operation in brief.
04
03
What is Object? How to convert one object form to another object form?
07
OR
Q.3
Explain control statements with example.
07
Explain the following Vector Matrix
07
Q.4
What is Graph? Explain graphical elements in detail.
07
Explain decision tree with example.
07
OR
Q.4
What is prediction? Differentiate random forest and decision tree. Which decision model is best for decision making?
07
What is binomial distribution? Explain functions used to execute binomial distribution with example.
07
Q.5
Explain linear models with example.
07
Explain the following with arguments Histogram pie boxplot
07
OR
Q.5
How to read data from excel and database? Explain in detail
07
What is Prescriptive Analytics? How to Creating data for analytics through designed experiments?
07
Seat No.: Enrolment
GUJARAT TECHNOLOGICAL UNIVERSITY
MCA Integrated SEMESTER-VIII • EXAMINATION SUMMER 2017
Subject Code: 4480602 Date: 01/05/ 2017
Subject Name: Data Analytics with R
Time: 10.30 am to 01.00 pm Total Marks: 70
Instructions:
1. Attempt all questions.
2. Make suitable assumptions wherever necessary.
3. Figures to the right indicate full marks.
Q.1
What is data analytics? Explain the general categories of data analytics that are distinguished by the results they produce.
07
Explain the following packages (ii)Accessing built in datasets
07
Q.2
Explain data type in detail with example.
07
Explain classification of data in detail.
07
OR
What is data frame? Explain the functionally of data frame with example.
07
Q.3
i. What is variable? How to assign a value to a variable and removing variable in
ii. Explain list operation in brief.
04
03
What is Object? How to convert one object form to another object form?
07
OR
Q.3
Explain control statements with example.
07
Explain the following Vector Matrix
07
Q.4
What is Graph? Explain graphical elements in detail.
07
Explain decision tree with example.
07
OR
Q.4
What is prediction? Differentiate random forest and decision tree. Which decision model is best for decision making?
07
What is binomial distribution? Explain functions used to execute binomial distribution with example.
07
Q.5
Explain linear models with example.
07
Explain the following with arguments Histogram pie boxplot
07
OR
Q.5
How to read data from excel and database? Explain in detail
07
What is Prescriptive Analytics? How to Creating data for analytics through designed experiments?
07
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Subjects
- advanced c programming (adv – c)
- advanced python
- basic mathematics for it
- big data
- c++ with class libraries (cpp)
- communication skills - ii
- communication skills-1
- cyber security and forensics (csf)
- data analytics with r
- data structure
- database management systems
- discrete mathematics for computer science (dmcs)
- environmental studies
- fundamentals of computer
- fundamentals of database management systems
- fundamentals of networking
- fundamentals of programming – i
- fundamentals of web
- information security
- java programming
- machine learning
- management information systems (mis)
- mobile programming
- network security
- operating system
- operations research
- python (py)
- software engineering
- software testing
- statistical methods
- uml & object oriented modeling
- web development tools