DCA6112 DATA VISUALIZATION

Description

SESSION	JULY/SEPTEMBER 2025
PROGRAM	MASTER OF COMPUTER APPLICATIONS (MCA)
SEMESTER	I
COURSE CODE & NAME	DCA6112 DATA VISUALISATION

 

 

Assignment SET – I

 

 

1.a. Define the concept of data visualization and list its key applications across different industries with suitable examples. 5  

1. Explain the importance of understanding data types and how they affect the accuracy and clarity of visual representation in Excel. 5

Ans 1.

(a) Concept of Data Visualization and Its Key Applications Across Industries

Data Visualization

Data visualization is the process of representing raw data in a graphical or pictorial form to make information easier to understand and interpret. It converts complex data into meaningful visuals such as charts, graphs, dashboards, and infographics that help in identifying trends, correlations, and patterns. In a world dominated by data, visualization acts as a vital communication tool that bridges the gap between data and decision-making.

Purpose and Relevance

The primary purpose of data visualization is to simplify analysis and present insights that can drive strategic action.

 

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2.a. Explain the various types of bar charts available in Excel. Differentiate among them with examples of when each type is most appropriate. 5       

1. Demonstrate the process of creating a Gantt chart in Excel and explain its role in project management and scheduling. 5

Ans 2.

(a) Types of Bar Charts in Excel and Their Appropriate Use

Bar Charts

Bar charts are among the most widely used data visualization tools in Excel. They represent categorical data with rectangular bars whose lengths are proportional to the values they depict. Bar charts are ideal for comparing quantities across different categories or tracking changes over time when the differences are

 

3.a. What is a Scatter Plot in Excel? Explain its main purpose and write the steps to create it. Also discuss how it helps in showing relationships between two variables.   5         

1. Analyze the process, structure, and applications of Waterfall Charts in Excel. Explain how they help in understanding changes in financial data, such as profit and loss statements or budget analysis. 5

Ans 3.

(a) Scatter Plot in Excel and Its Use in Showing Variable Relationships

Scatter Plots

A scatter plot, also known as an XY chart, is a visualization used to examine the relationship between two numerical variables. In Excel, it plots data points on horizontal (X-axis) and vertical (Y-axis) coordinates, making it ideal for identifying correlations, trends, and outliers within datasets.

Purpose and Interpretation

The primary purpose

 

Assignment SET – II

 

4. a. Discuss the advantages and disadvantages of using Python for data visualization, providing relevant examples from real-world contexts. 5
5. Illustrate the step-by-step process of generating a Word Cloud in Python and explain the significance of each step. 5

Ans 4.

(a) Python for Data Visualization: Advantages and Disadvantages

Strengths and Ecosystem Power

Python’s biggest advantage in visualization is its rich ecosystem. Libraries such as Matplotlib, Plotly, Seaborn, and Altair span from publication-grade static plots to highly interactive, browser-ready visuals. Seamless integration with NumPy, pandas, and scikit-learn enables an end-to-end pipeline—from cleaning and modeling to charting—inside one language. In real-world analytics, a data scientist can aggregate sales data with pandas and immediately produce comparative trend lines or cohort heatmaps, while a product team might deploy Plotly Dash to ship interactive KPI dashboards without switching stacks. Python’s readability also helps teams collaborate and iterate

 

5.a. Compare different imputation techniques used for handling missing   data, and analyze their impact on the quality of data visualization. 5  

1. Evaluate the key steps involved in time series analysis, from data collection to forecasting, and justify the importance of each stage in trend prediction. 5

Ans 5.

(a). Imputation Techniques for Missing Data and Their Impact on Visualization Quality

Simple Imputation and Its Visual Risks

Mean or median imputation is quick and preserves dataset size, but it compresses variance and can flatten distributions. Visuals such as histograms and box plots may misleadingly suggest tighter spreads, while relationships in scatter plots can be diluted.

Hot-Deck and KNN Approaches

Hot-deck imputation draws plausible values from similar cases, retaining realistic distributional shapes. K-Nearest

 

 

6.a. Explain in brief about the best practices for designing effective 3D visualizations, focusing on design principles, interactivity, and accessibility to ensure clarity and user engagement. 5      

1. Design an effective data storytelling strategy for dashboard development. Explain how storytelling enhances communication, insight generation, and decision-making in dashboards. 5

Ans 6.

(a) Best Practices for Effective 3D Visualizations: Design, Interactivity, Accessibility

Design Principles for Clarity

Use 3D only when depth encodes genuine information—such as spatial coordinates or volumetric density. Minimize occlusion by thoughtful camera angles and transparent surfaces, and avoid perspective exaggeration that distorts magnitude perception. Provide consistent lighting and restrained textures so form, not gloss, carries meaning. Axes, ticks, and legends must remain legible from typical viewing angles.

Interactivity that