ANU UG/Degree 4th Sem(Y23) Data Visualization Unit Wise Important Questions

 ANU UG/Degree 4th Sem(Y23) Data Visualization  Unit Wise Important Questions are now available, these questions are very important for your semester exams. These questions are prepared by top qualified faculty. Read these questions for good marks.

 UNIT I:
Creating Visual Analytics with tableau desktop, connecting to your data-How to Connect to your data, What are generated Values? Knowing when to use a direct connection, Joining tables with tableau, blending different data sources in a single worksheet

Short Answer Questions

1. What is the purpose of connecting to a data source in Tableau Desktop?
2. Define "generated values" in Tableau.
3. What is a direct connection in Tableau and when might you use it?
4. How does Tableau Desktop join tables from the same data source?
5. What is data blending in Tableau?
6. When would you choose blending over joining tables in Tableau?

Long Answer Questions

1. Explain the process of connecting to your data in Tableau Desktop.
2. Discuss the differences between a direct (live) connection and an extract connection in Tableau.
3. Describe what generated values are in Tableau and provide examples of how they can be used in visual analytics.
4. Outline the steps involved in joining tables in Tableau and the importance of choosing the correct join type.
5. Explain data blending in Tableau, including its advantages and limitations compared to joining tables.
6. Critically analyze how Tableau Desktop’s capabilities in data connection, joining, and blending contribute to effective visual analytics.

 UNIT II:
Building your first Visualization- How Me works- Chart types, Text Tables, Maps, bar chart, Line charts, Area Fill charts and Pie charts, scatter plot, Bullet graph, Gantt charts, Sorting data in tableau, Enhancing Views with filters, sets groups and hierarchies.

Short Answer Questions

1. What is a text table in Tableau and what insights does it provide?
2. How are maps used in Tableau to visualize geographic information?
3. Differentiate between bar charts, line charts, area fill charts, and pie charts in Tableau.
4. What is a scatter plot and how does it help in identifying relationships between variables?
5. Explain the purpose of bullet graphs and Gantt charts in Tableau visualizations.
6. How do filters, sets, groups, hierarchies, and sorting enhance data visualization in Tableau?

Long Answer Questions

1. Explain the process of building your first visualization in Tableau, detailing the steps from data selection to creating the final chart.
2. Discuss the various chart types available in Tableau (including text tables, maps, bar charts, line charts, area fill charts, pie charts, scatter plots, bullet graphs, and Gantt charts) and describe the scenarios in which each type is most effective.
3. Analyze how filters, sets, groups, and hierarchies can be used to enhance a visualization in Tableau, providing examples for each.
4. Describe the methods for sorting data in Tableau and explain how effective sorting can improve the clarity and interpretability of visual analytics.
5. Evaluate the use of Gantt charts in Tableau for project management visualizations. What key elements are necessary to build a Gantt chart, and what insights can it offer?
6. Critically assess how the combined use of different chart types and data enhancement techniques (such as filtering and grouping) in Tableau contributes to effective data storytelling.

 UNIT III:
Creating calculations to enhance your data- What is aggregation, what are calculated values and table calculations, Using the calculation dialog box to create, Building formulas using table calculations, Using table calculation functions.

Short Answer Questions

1. What is aggregation in Tableau, and why is it important in data analysis?
2. Define calculated values in Tableau.
3. What are table calculations in Tableau, and how do they differ from regular calculated fields?
4. How is the Calculation Dialog Box used to create new calculations in Tableau?
5. Name two common table calculation functions in Tableau.
6. In what scenario might you choose to use a table calculation over a standard calculated field?

Long Answer Questions

1. Explain the concept of aggregation in Tableau and discuss its significance in summarizing data for visual analytics.
2. Compare and contrast calculated values with table calculations in Tableau, providing examples of when each is most appropriate.
3. Describe the process of creating a calculated field using the Calculation Dialog Box in Tableau, highlighting key features and functionalities.
4. Discuss how to build formulas using table calculations in Tableau, including an explanation of syntax and the role of functions.
5. Analyze the use of table calculation functions in Tableau (such as WINDOW_SUM and RUNNING_AVG) and explain how they can enhance data analysis.
6. Critically evaluate how the combination of aggregation, calculated values, and table calculations can be used to create more dynamic and insightful visualizations in Tableau.

 UNIT IV:
Using maps to improve insights-Create a Standard Map View, Plotting your own locations on a map, Replace Tableau’s standard maps, Shaping data to enable Point-to-Point mapping.

Short Answer Questions

1. What is a Standard Map View in Tableau, and how is it created?
2. How do you plot your own geographic locations on a Tableau map?
3. What steps are involved in replacing Tableau’s standard maps with a custom map?
4. Define point-to-point mapping in Tableau.
5. What data shaping techniques are necessary to enable point-to-point mapping?
6. How does customizing maps enhance insights in Tableau visualizations?

Long Answer Questions

1. Explain the process of creating a Standard Map View in Tableau, including how to configure geographic roles and customize map settings.
2. Describe the method for plotting your own locations on a Tableau map and discuss the importance of using latitude and longitude coordinates.
3. Discuss the procedures and benefits of replacing Tableau’s standard maps with custom maps, including potential challenges and solutions.
4. Outline the concept of point-to-point mapping in Tableau, detailing the data requirements and steps to achieve it.
5. Analyze the role of data shaping in enabling effective point-to-point mapping and how it contributes to more accurate visualizations.
6. Critically evaluate how using customized maps in Tableau can improve data insights, citing examples of when and why to implement these customizations.

 UNIT V:
Developing an Adhoc analysis environment- generating new data with forecasts, providing self evidence adhoc analysis with parameters, Editing views in tableau Server.

Short Answer Questions

1. What is an ad hoc analysis environment in Tableau, and why is it important for data exploration?
2. How does Tableau generate new data with forecasts, and what are the key components of this process?
3. What role do parameters play in enabling self-evident ad hoc analysis in Tableau?
4. Describe the process of generating forecasts in Tableau and how these forecasts aid in data analysis.
5. How can views be edited in Tableau Server to support ad hoc analysis?
6. What are the benefits of providing self-evident ad hoc analysis using interactive features in Tableau?

Long Answer Questions

1. Explain the concept of developing an ad hoc analysis environment in Tableau, including the benefits of integrating forecasting and interactive parameters.
2. Discuss the process of generating new data with forecasts in Tableau, detailing the steps involved and the importance of forecast accuracy.
3. Describe how parameters are utilized to facilitate self-evident ad hoc analysis in Tableau, and provide examples of their practical application.
4. Outline the methods and best practices for editing views in Tableau Server to enhance ad hoc analysis capabilities.
5. Analyze how forecasting in Tableau contributes to proactive decision-making, including a discussion on the assumptions and limitations of the forecasting models.
6. Critically evaluate how the combination of forecasting, parameters, and view editing in Tableau creates a robust environment for dynamic ad hoc analysis.