DSSemester 6

Data Science Previous Year Questions

Previous year question papers for Data Science

Author: Deepak Modi
Last Updated: 2025-06-15

Course Title: Data Science
Course Code: PEC-CSE-320G
Semester: B.Tech. 6th Semester (CSE)

May—2024 Examination

Short Answer Questions (5 × 3 = 15 marks)

  1. Explain the following terms: [5 × 3 = 15]
    (a) What is Data Science? List the differences between supervised and unsupervised learning.
    (b) What is Modeling?
    (c) Discuss Prediction analysis.
    (d) Explain data Collection Process.
    (e) What is Scatterplots. How it work?

UNIT - I (15 marks)

  1. (a) What is Big Data? What are the different types of Big Data? What are the five V's of Big Data? [7]
    (b) What are advantages of Big Data? Who is using Big Data? State 5 Applications of it. [8]

  2. (a) What is Data Preprocessing? Explain and discuss in details the various steps involved in the Data Preprocessing. [7]
    (b) Reporting vs. Analysis: Do you know the Difference? Discuss various difference. [8]

UNIT - II (15 marks)

  1. What is web scraping? Explain Web Scraping Procedure? What are the preferred programming languages for web scrapping? Give an example of web scraping you worked on. What are the Python libraries you have used for web scrapping? What is the purpose of the request module in Python? [15]

  2. (a) A Shivalik restaurant has recorded the following data into their register for their income by Drinks and Food. Plot them on the line chart. [10]

    DayMondayTuesdayWednesdayThursdayFriday
    Drinks450560400605580
    Food490600425610625

    Apply following customization to the line chart:

    • Write a title for the chart "The Weekly Restaurant Orders"
    • Write the appropriate titles of both the axes
    • Write code to Display legends
    • Display your choice of colors for both the lines drinks and food
    • Use the line style - dotted for drinks and dashdot for food
    • Display plus markers on drinks and x markers of food

    (b) Why you should use NumPy arrays instead of nested Python lists? [5]

UNIT - III (15 marks)

  1. (a) What is Data understanding data science? Why data understanding is important in data science? Discuss Data understand phases. [8]
    (b) What is the data requirements? What are the 5 methods of collecting data? How to make data from Understanding to Preparation? [7]

  2. Discuss the following in details: [15]
    (i) Modelling and evaluation
    (ii) Deployment and feedback

UNIT - IV (15 marks)

  1. Explain the following terms: [15]
    (i) Clustering analytics
    (ii) Text analytics

  2. Discuss any Recommendations and Business analytics Application of data science in details. [15]

May—2023 Examination

Short Answer Questions (5 × 3 = 15 marks)

  1. Explain the following terms: [6 × 2.5 = 15]
    (a) Traits or characteristics of big data
    (b) EDA
    (c) Datatypes in NumPy (d) Data cleaning
    (e) Myths about data science
    (f) Clustering

UNIT - I (15 marks)

  1. (a) Define data science and discuss data science components in detail. [7.5]
    (b) Illustrate data reporting vs data analysis in data science. [7.5]

  2. Discuss data science process in detail with diagrams and examples. [15]

UNIT - II (15 marks)

  1. (a) Discuss the various programming toolkits available in Python data science with suitable examples. [7.5]
    (b) What do you understand by web scrapping? Discuss web scrapping mechanism to collect data using python library. [7.5]

  2. How Matplotlib is useful in data visualization in Python? Discuss Bar, Line and Scattered plotting using Matplotlib with suitable examples. [15]

UNIT - III (15 marks)

  1. What is Data Science? Discuss from problem to Approach from Requirements to Collection stage of methodology helpful for data scientists. [15]

  2. Discuss the significance of Modeling, Evaluation, Deployment and feedback of the data science projects. [15]

UNIT - IV (15 marks)

  1. Data science tools are useful in election campaign and predicting results in real world? Discuss in detail. [15]

  2. What Do You Understand by Business Analytics? Discuss Its Usage in Business Goals Achievements. [15]

July—2022 Examination

Short Answer Questions (6 × 2.5 = 15 marks)

  1. Explain the following: [6 × 2.5 = 15]
    (a) Traits or characteristics of big data
    (b) Components of data Science
    (c) Tokenization in NLTK library in Python
    (d) Data Collections
    (e) Web scrapping
    (f) Text Analytics

UNIT - I (15 marks)

  1. (a) Define Data science and discuss its needs in the current world of data sources. [8]
    (b) Differentiate Artificial Intelligence & Data science with suitable diagram. [7]

  2. Discuss Data science process in details with diagrams and examples. [15]

UNIT - II (15 marks)

  1. Discuss the various programming toolkits available in Python for data science with suitable examples. [15]

  2. How Numpy is useful in scientific computation in python? Also discuss arrays various data types used in Numpy library with suitable examples. [15]

UNIT - III (15 marks)

  1. What do you understand by Data Science Methodology? Discuss methodology from problem to approach and from requirements to collection in data system. [15]

  2. Illustrate role of evaluation, Deployments and Feedback stages of the data science projects. [15]

UNIT - IV (15 marks)

  1. How Data science tools are useful in recommendations system in real world? Discuss in details. [15]

  2. What do you understand by Business Analytics? Discuss its usage in Business Goals Achievements. [15]

July—2021 Examination

Short Answer Questions (5 × 3 = 15 marks)

  1. Write short notes on the following: [5 × 3 = 15]
    (a) What are the differences between supervised and unsupervised learning?
    (b) What is AI? What is use of AI in data science?
    (c) Explain the Myth of Data Science.
    (d) Discuss Bar Charts.
    (e) Discuss the various data science applications.

UNIT - I (15 marks)

  1. (a) Define Data science? List the differences between supervised and unsupervised learning. [8] (b) Define big data and explain and discuss the characteristics of big data and application of big data. [7]

  2. (a) What is data preprocessing? Explain and discuss in detail the various steps involved in the data preprocessing. [8] (b) What is statistical modeling and how is it used? What are the reasons to learn statistical modeling? Discuss important statistical techniques in data analysis. [7]

UNIT - II (15 marks)

  1. (a) What is Python Matplotlib? What is Matplotlib used for? What are the basic elements/components of the chart? Discuss the various types of plots with suitable examples. [8]
    (b) What is an array and how is it different from a list? What is the name of the built-in array class in NumPy? Create the following NumPy arrays: [7]
    (i) a 1D array called zeros having 10 elements and all the elements are set to zero
    (ii) a 1D array called vowels having the element 'a', 'e', 'i', 'o', 'u'
    (iii) A 2-D array called ones having 2 rows and 5 columns and all the elements are set to 1 and dtype as int

  2. (a) Plot the following data on line chart: [10]

    DayMondayTuesdayWednesdayThursdayFriday
    Income510350475580600

    Apply the following customizations:

    • Write the title of the chart "The Weekly Income Report"
    • Write the appropriate titles of both the axes
    • Write code to Display legends
    • Display red color of the line
    • Use the line style - dashed
    • Display diamond style markers on data points

    (b) What do you mean by file? List out the basic file modes available. Write a statement to create a dat.txt file with the following text: [5]
    (i) python file handling is very interesting and useful
    (ii) This is a text file created through python

UNIT - III (15 marks)

  1. (a) What is business understanding in data science? Why business understanding is important in data science? Discuss business understand phases and process in detail. [8]
    (b) How to prepare data is used from Modeling to Evaluation. [7]

  2. (a) What is data requirement? What are the five methods of collecting data? How to prepare data from understanding to preparation. [8]
    (b) Discuss the following in detail: [7]
    (i) Analytic Approach
    (ii) Deployment and feedback

UNIT - IV (15 marks)

  1. Discuss the following application of data science in detail: [15]
    (a) Prediction
    (b) Elections

  2. Discuss the clustering and text analytics Application of data science in detail. [15]

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