Differentiate between Big Data and Business, Intelligence.
Big Data Analytics - Elective V Question Papers - SPPU University
Access and download Big Data Analytics - Elective V question papers from Savitribai Phule Pune University (SPPU). Our collection includes INSEM (Internal Semester) and ENDSEM (End Semester) exam papers.
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INSEM Papers for Big Data Analytics - Elective V
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Big Data Analytics - Elective V Questions
Pre-rendered question cards from available structured metadata.
2024 May Jun ENDSEM
2024 Mar INSEM
2024 May Jun ENDSEM
INSEM
2024 Mar INSEM
12 Questions
Q1
15 Marks
Q1
·
b
·
5 Marks
Explain the concept of Big Data Ecosystem and its components.
Q1
·
c
·
5 Marks
Write concept of Predictive Analytics and Hadoop.
Q2
15 Marks
Q2
·
a
·
5 Marks
Explain the types of Big Data Analytics with suitable example
Q2
·
b
·
5 Marks
Discuss the characteristics and challenges with Big Data.
Q2
·
c
·
5 Marks
Describe big data Ecosystem and looker with suitable example.
Q3
15 Marks
Q3
·
a
·
5 Marks
Explain Liner Regression with suitable example.
Q3
·
b
·
5 Marks
Describe the Apriori algorithm and its significance in association rule mining.
Q3
·
c
·
5 Marks
Explain Logistics Regression and its use in real world scenario.
Q4
15 Marks
Q4
·
a
·
5 Marks
Explain K means clustering algorithm with suitable example.
Q4
·
b
·
5 Marks
Explain the concept of association rule mining in the context of transaction data from a grocery store.
Q4
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c
·
5 Marks
Differentiate between Liner Regression and Logistics Regression.
| Subject Name | Big Data Analytics - Elective V |
|---|---|
| Semester | VIII |
| Pattern Year | 2019 |
| Subject Code | 417532(B) |
| Max Marks | 30 |
| Total Questions | 4 |
| Duration | 1 Hour |
| Paper Number | [6270]-196 |
| Academic Year | B.E. |
| Branch Name | Artificial Intelligence and Data Science |
| Exam Type | INSEM |
| Exam Session | 2024 Mar INSEM |
| Watermark | ['CEGP013091', '49.248.216.238 26/03/2024 13:38:40 static-238'] |
ENDSEM
2024 May Jun ENDSEM
16 Questions
Q1
18 Marks
Q1
·
a
·
8 Marks
What are the primary methods and functions for importing and exporting data in R and how can they be utilized effectively?
Q1
·
b
·
10 Marks
How can data analysts detect and address dirty data using visualizations and statistical techniques in R and what are the implications of anomalies in datasets on decision-making processes?
Q2
18 Marks
Q2
·
a
·
8 Marks
With the help of neat diagram explain the phases of Data Analytics life cycle.
Q2
·
b
·
10 Marks
What are the different attribute types in data analysis, and how are they categorized? How does R handle various data types, such as numeric, character, logical, and factors?
Q3
17 Marks
Q3
·
a
·
10 Marks
Explain the following terms with respect to Exploratory data analysis. i) Data Sourcing ii) Data Cleaning iii) Univariate analysis iv) Bi-variate/Multivariate analysis
Q3
·
b
·
7 Marks
What are the essential steps in data exploration and how do they contribute to uncovering insights and patterns within a dataset?
Q4
17 Marks
Q4
·
a
·
10 Marks
How do ensemble methods such as bagging, boosting, AdaBoost and Random Forest contribute to improving classification accuracy in machine learning models?
Q4
·
b
·
7 Marks
How does the utilization of a confusion matrix aid in the evaluation and selection of models.
Q5
18 Marks
Q5
·
a
·
10 Marks
What are the challenges associated with visualizing big data and how do these challenges impact the effectiveness and efficiency of data analysis and decision-making processes?
Q5
·
b
·
8 Marks
How does Tableau facilitate effective data visualization and what are some advanced techniques or features within Tableau that can be utilized to create insightful and interactive visualizations for complex datasets?
Q6
18 Marks
Q6
·
a
·
8 Marks
What are the key features and functionalities of the Google Chart API and how does it enable developers to create dynamic and interactive charts and visualizations for web applications?
Q6
·
b
·
10 Marks
What are the various types of data visualization techniques available to data analysts?
Q7
17 Marks
Q7
·
a
·
10 Marks
In what ways does financial data analytics leverage big data technologies to drive insights, mitigate risks and enhance decision-making processes within the financial industry?
Q7
·
b
·
7 Marks
How does Apache HBase contribute to efficient data storage and retrieval in big data environments
Q8
17 Marks
Q8
·
a
·
10 Marks
How does the Semantria tool streamline the data collection process and what features or capabilities does it offer that make it a valuable asset for businesses seeking to gather and analyze large volumes of unstructured data from various sources?
Q8
·
b
·
7 Marks
How does Mozenda serve as an effective data filtering and extraction tool?
| Subject Name | Big Data Analytics - Elective V |
|---|---|
| Semester | VIII |
| Pattern Year | 2019 |
| Subject Code | 417532B |
| Max Marks | 70 |
| Total Questions | 8 |
| Duration | 2½ Hours |
| Paper Number | [6263]-393 |
| Academic Year | B.E. |
| Branch Name | Artificial Intelligence and Data Science |
| Exam Type | ENDSEM |
| Exam Session | 2024 May Jun ENDSEM |
| Watermark | ['CEGP013091', '49.248.216.238 17/05/2024 14:07:39 static-238'] |
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