Data Visualization Techniques — DU B. Sc. (Hons) Electronics Previous Year Question Papers

Instruction for Candidates 1. 2. a. What is Data Visualization? Mention any two objectives of data visualization in data analysis. b. What is Exploratory Data Analysis (EDA)? State any two visualization techniques commonly used in EDA. c. Define Word Cloud. How does the size and frequency of words represent information in a word cloud? d. What is a Box Plot? List any two statistical parameters represented by a box plot. e. Describe the process of data cleaning and preparation. Highlight the techniques used for handling missing data. f. Define Data Story in Tableau. How does it help in communicating data insights effectively to decision makers? (3 x 6 = 18) b. Write a program code to: i. Create a Pandas dataframe for employee data shown below ii. Set Employee_ID as index and sort the data according to Experience iii. Display a scatter plot for Salary vs. Experience and compute average salary by Employee_ID Name Age Salary Department Experience 103 Neha 28 42000 Finance 4 106 Priya 32 36000 Finance 1 c. Differentiate between Histogram and Bar Chart. Plot a histogram for Salary distribution. a. Explain how Plotly provides more advanced and interactive visualization features as compared to Matplotlib. Discuss at least three major differences between the two libraries in terms of interactivity, visualization capabilities, and usability. b. Write a program code to: i. To store the student dataset shown below in a Pandas dataframe ii. Add two new columns ‘Total Marks’ and ‘Average Marks’ iii. Add a new row showing mean values of all the numerical columns Student Study Hours Attendance (%) Physics Chemistry Maths c. Write a program code to: i. Display a scatter plot of Average Marks vs. Study Hours ii. Display a bar chart of Total Marks for all students a. Explain the Seaborn library used for data visualization in Python. Discuss its key features and advantages over Matplotlib. Also explain any two types of plots that can be created using Seaborn with suitable examples. b. What is a Heatmap? Mention two practical applications of heatmaps in data visualization? Explain how heatmaps help in understanding patterns, correlations, and intensity of data values. c. The following dataset represents marks obtained by students in three subjects: Student Physics Chemistry Maths Write a program code to: i. Create a Pandas DataFrame for the given dataset. ii. Generate a heatmap representing marks of students in different subjects. iii. Display annotations and a suitable colormap in the heatmap. a. Describe the main components of a violin plot and how it combines the features of box plot and kernel density estimation (KDE). Write a Python code to generate a violin plot for the following dataset: [45, 50, 60, 55, 70, 65, 58, 62, 68]. Briefly interpret what information about the data distribution can be obtained from the generated violin plot.