JNTU
JNTU B.Tech IV DATA ANALYTICS Previous Question Paper (July - 2023 - R16)
Note: i) Question paper consists of Part A, Part B. ii) Part A is compulsory, which carries 25 marks. In Part A, Answer all questions. iii) In Part B, Answer any one question from each unit. Each question carries 10 marks and may have a, b as sub questions.
Part A
List the Elements of Data Architecture.
Answer
The core elements of data architecture include:
- Data Strategy: Defining the vision and alignment with business goals.
- Data Governance: Establishing policies, standards, and security protocols for data management.
- Data Integration: The processes and tools used to combine data from disparate sources.
- Data Modeling: Creating conceptual, logical, and physical representations of data structures.
- Data Storage and Infrastructure: The selection of databases, data warehouses, or data lakes to house information.
Which source of Data is more reliable and why?
Answer
Primary data is considered more reliable than secondary data. This is because primary data is collected directly by the researcher for a specific purpose, ensuring that the methodology, data quality, and relevance are controlled and tailored to the current investigation.
In contrast, secondary data is collected by someone else for a different objective, which may lead to issues regarding outdated information, lack of context, or misalignment with the specific requirements of the current analysis. Since the origin and collection process of primary data are transparent and verified, it provides a higher level of accuracy and authenticity for decision-making.
Outline the Data Analytics techniques.
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What is the difference between Spark, Scala and Impala?
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What is Model Theory?
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List the BLUE property assumptions.
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What are the methods to measure Forecast accuracy?
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Write about multiple decision tree.
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Define data visualization.
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List out data visualization applications in finance?
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Part B
List and explain about the Sources of generating Secondary Data.
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What is an outlier in data set? Illustrate how to detect and remove outliers.
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Discuss about Characteristics of effective Data Architecture.
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Illustrate techniques of missing values treatment.
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Explain the application of modeling in business and Elaborate any one.
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Explain how Key-Value and Column Oriented databases address the volume of the data and the scalability.
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Define Data Analytics. Give are the applications of Analytics.
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Illustrate the Model-Based Techniques for dealing with Missing Data with suitable examples.
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Demonstrate linear regression with suitable example.
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Explain Logistic Regression model and analyze Best Fit Statistics with example.
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Explain briefly about variable rationalization in Regression modeling.
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What is Overfitting? Why Overfitting happens? Give its Preventing techniques?
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Discuss in detail about STL approach.
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Compare and contrast between Supervised and unsupervised Learning.
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Discuss about Univariate Non-Stationary Processes and its forecast.
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Discuss briefly about Icon based Visualization Techniques.
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Discuss about visualizing the complex data and relations.
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List and explain the steps in Data Visualization in Tableau.
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