JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD B.Tech III Year DATA SCIENCE Previous Question Paper (March - 2024 - R18)
i) Question paper consists of Part A, Part B. ii) Part A is compulsory, which carries 25 marks. iii) In Part B, Answer any one question from each unit.
Part A
Define Big Data.
Answer
Big Data refers to datasets that are so voluminous, complex, and rapidly changing that traditional data processing software and database management systems are inadequate to capture, store, manage, and analyze them. It is typically characterized by the 5Vs: Volume, Velocity, Variety, Veracity, and Value.
How is Data Science different from traditional statistics?
Answer
- Scope: Traditional statistics focuses primarily on inference and hypothesis testing on smaller, structured datasets. Data Science integrates statistics with computer science, machine learning, and domain expertise to handle massive, unstructured datasets.
- Goal: Statistics aims to describe data and validate models, whereas Data Science focuses on predictive modeling, pattern discovery, and building automated systems.
- Tools: Data Science relies heavily on programming languages like Python/R, big data frameworks (Hadoop/Spark), and cloud computing, while traditional statistics often uses specialized statistical software like SPSS or SAS.
What is Exploratory Data Analysis (EDA)?
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How can histograms be useful in EDA?
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What role do probabilities play in Naive Bayes spam filtering?
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How does the choice of tokenization affect Naive Bayes performance in spam filtering?
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Define web scraping.
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Why is data wrangling important in data analysis?
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List the tools commonly used for data visualization.
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Give an example of a data visualization project from industry.
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Part B
Discuss the reasons behind the emergence of Datafication.
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Explain how organizations can effectively navigate through the hype surrounding Data Science.
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Differentiate between Population and Sample in statistical analysis.
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Write an overview of R programming language and its relevance to Data Science.
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Explain the concept of exploratory data analysis and its importance in the data science process.
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Analyze Real Direct (online real estate firm) as a case study in the context of data science, focusing on its data handling and analysis methods.
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Compare and contrast Linear Regression, k-Nearest Neighbors (k-NN), and k-means algorithms in terms of their applications and underlying principles.
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Compare and contrast Naive Bayes with other machine learning algorithms for spam filtering, highlighting its advantages and disadvantages.
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Investigate real-world applications of Naive Bayes in spam filtering and discuss their effectiveness in different contexts.
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Discuss the role of decision trees in feature selection and their advantages/disadvantages.
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Discuss the significance of random forests in feature selection with a detailed example.
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Discuss the process of web scraping using APIs versus traditional methods.
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Compare filter, wrapper, and embedded methods in feature selection.
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Discuss the challenges faced by next-generation data scientists in a rapidly evolving technological landscape.
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Explain how data visualization can aid in identifying patterns in data.
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Evaluate the impact of data science on society, considering both positive and negative implications.
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