Jawaharlal Nehru Technological University Hyderabad

Jawaharlal Nehru Technological University Hyderabad B.Tech III DATA SCIENCE Previous Question Paper (August - 2022 - R18)

B.Tech | III | III-I | R18 | August - 2022

Answer PaperPaper Code: 155DUMaximum Marks: 75Time: 3 Hours

Answer any five questions. All questions carry equal marks.

Main

115 marks
a

What is Data Science? Explain its role.

7 marks

Answer

Data Science is an interdisciplinary field that utilizes scientific methods, processes, algorithms, and systems to extract knowledge and actionable insights from structured and unstructured data. It integrates concepts from statistics, mathematics, computer science, and domain expertise to solve complex problems.

Its role is fundamental in modern decision-making by enabling organizations to:

  • Identify patterns and trends that are not visible through traditional analysis.
  • Predict future outcomes through predictive modeling and machine learning.
  • Optimize business operations and improve efficiency.
  • Personalize user experiences by analyzing behavioral data.
  • Drive innovation by uncovering hidden opportunities within large datasets.
b

Explain Data Science process in detail.

8 marks

Answer

The Data Science process is an iterative lifecycle used to transform raw data into meaningful insights. It typically follows these key stages:

  1. Problem Definition: Clearly defining the business objective or research question to be addressed.
  2. Data Collection: Gathering relevant data from various sources such as databases, APIs, web scraping, or logs.
  3. Data Cleaning and Preparation: The most time-consuming stage, involving handling missing values, removing duplicates, correcting inconsistencies, and formatting data for analysis.
  4. Exploratory Data Analysis (EDA): Using statistical summaries and visualizations to understand data distributions, correlations, and outliers.
  5. Data Modeling: Selecting and applying machine learning algorithms (e.g., regression, classification, clustering) to build predictive or descriptive models.
  6. Model Evaluation: Assessing the performance of the model using metrics like accuracy, precision, recall, or RMSE to ensure it meets the defined objectives.
  7. Deployment and Communication: Presenting findings to stakeholders through reports or dashboards and deploying the model into a production environment for real-time decision support.
215 marks
a

List out various Data Science tool kits and explain its characteristics.

7 marks

Answer

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b

Explain the role of Data Science in IT industry.

8 marks

Answer

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315 marks
a

Explain the primary and secondary data collection methods.

7 marks

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b

Give a brief account of REST API in data collection.

8 marks

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415 marks
a

What is data exploring in data science? Explain various ways and purpose of exploring the data.

7 marks

Answer

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b

What are various sources of data? Why is it important to have multiple sources of data?

8 marks

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515 marks
a

What is data distribution in data science? What are the main properties of distribution?

7 marks

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b

Describe K-means clustering algorithm and explain its advantages and disadvantages.

8 marks

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615 marks
a

What is Support Vector Machine? Explain its working principle.

7 marks

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b

Explain Hyperfine in SVM and its role and normalization in SVM.

8 marks

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715 marks
a

Describe data for visualization. How to choose the right data visualization?

7 marks

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b

Explain visual encoding with an example. Which visual encoding is the most effective and why?

8 marks

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815 marks
a

Outline various technologies used for data visualization in data science.

7 marks

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b

Summarize various applications of data science. What is the role of data science in modern world?

8 marks

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