Assignment Set – 1

Q1. Compare and contrast bar charts and Pie charts in terms of their use cases, strengths, and limitations. 10           

Ans 1.

Comparison Between Bar Charts and Pie Charts

Bar and Pie Charts

Bar charts and pie charts are among the most widely used visualization tools for summarizing and presenting categorical data. Although both serve the purpose of comparing categories, they differ significantly in structure, interpretability, and the type of insights they can convey. Understanding their similarities and differences helps analysts choose the appropriate visualization method depending on dataset characteristics and communication needs.

Use Cases of Bar Charts

A bar chart displays category names on one axis and their corresponding values on the other.

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Q2. Describe the role of user interface (UI) and server function in an R-Shiny app.     10

Ans 2.

Role of User Interface (UI) and Server Function in an R-Shiny App

R-Shiny App Structure

R-Shiny is an interactive web application framework that allows analysts to build dashboards and data-driven tools directly from R. A Shiny app operates through two core components: the user interface (UI) and the server function. These components work together to create dynamic and responsive applications that enable users to interact with datasets, models, and visualizations in real time.

Role of the User Interface (UI)

The UI defines how the application appears visually to the user. It includes layout, input

Q3. Define a dashboard in the context of data visualization. Discuss its importance with suitable example. 10           

Ans 3.

Dashboards

A dashboard in data visualization refers to a consolidated visual display that compiles multiple charts, indicators, and analytical components into a single interface. Dashboards provide real-time insights by summarizing key metrics and trends associated with business operations, performance, customer behavior, or system status. They are widely used in decision-making environments because they present complex data in a structured and easily

Assignment Set – 2

Q4. What are the benefits of grouping data in Tableau when analyzing large datasets? 10    

Ans 4.

Benefits of Grouping Data in Tableau When Analyzing Large Datasets

Grouping in Tableau

Grouping in Tableau is a feature that allows users to combine related members of a dimension into a single higher-level category. When exploring large datasets with many unique values, grouping becomes an effective way to simplify the visualization, uncover hidden relationships, and provide a clearer analytical view. The process helps analysts convert granular information into meaningful segments, enabling more strategic

Q5. Explain how LOD expressions allow aggregations at different levels of granularity beyond the visualization’s view. 10         

Ans 5.

LOD Expressions

Level of Detail (LOD) expressions in Tableau are advanced analytical constructs that enable users to perform aggregations at different levels of granularity, independent of the visualization’s current view. LOD expressions allow analysts to define exactly how data should be grouped and aggregated, overcoming the limitations of traditional row-level or view-level computations. This results in highly flexible and precise analysis, especially when

Q6. Define data cleansing and explain why it is essential in Power BI.    10

Ans 6.

Data Cleansing in Power BI

Data cleansing in Power BI refers to the process of identifying, correcting, or removing inaccurate, inconsistent, incomplete, or irrelevant data before analysis. Since Power BI integrates data from multiple sources—such as Excel files, SQL databases, APIs, and cloud platforms—data quality issues are common. Clean data ensures the accuracy of dashboards, reports, and business decisions. Power Query, the data preparation engine in Power BI,