Use The File Attached Visualization Plan Please Include It
Use The File Attachedvisualization Plan Please Include the Followingy
Use the file attached Visualization Plan. Please include the following You will make at least six charts, including at least one geographic map, at least one bar, at least one table, and at least one line chart. Describe how your data set(s) will provide enough variables to create the required charts. (2pts) Propose at least four chart types listing the variables that you want to use for each chart. Please make it easy for the reader to understand the variables that you are using. Tell the reader which dataset each variable comes from, the units (or categories) of the variable, and any other additional information. (3pts) At least two draft charts in Tableau.
Similar to the finding topics assignment, the purpose is to make sure your datasets will work in Tableau and begin making charts. If appropriate, you are welcome to use the draft chart that you made in Part I. Please paste these graphs into your document. (3pts) Comment on how your proposed and draft charts will address your goals and objectives. (2pts)
Paper For Above instruction
Introduction
Data visualization plays a crucial role in transforming complex datasets into understandable insights. Effective visual representations such as maps, bar charts, tables, and line charts help communicate trends, patterns, and relationships within data. This paper discusses the utilization of datasets to create at least six different types of visualizations, including specific variables, datasets, and the rationale behind each choice. The goal is to develop an insightful visualization plan that not only demonstrates technical proficiency but also aligns with analytical objectives.
Data Sets and Variables for Visualization
For this project, I will use two datasets: a demographic data set and a sales performance dataset. The demographic dataset includes variables such as population size, geographic locations, education levels, and income brackets. The sales dataset contains variables like sales volume, revenue, product categories, and sales dates. These datasets provide ample variables to create diverse visualizations fulfilling the project requirements.
Chart 1: Geographic Map
Variables:
- Location (from demographic dataset): Geographic regions or states, units in geographic regions
- Population (from demographic dataset): Number of residents, units in hundreds or thousands
Purpose: The geographic map will visualize population distribution across different regions, highlighting areas with high or low populations. This helps identify demographic concentrations and regional differences.
Chart 2: Bar Chart
Variables:
- Product Category (from sales dataset): Categories like electronics, clothing, home goods, units are categories
- Total Sales (from sales dataset): Sum of sales volume or revenue per category
Purpose: The bar chart will compare sales performance across different product categories, aiding in understanding which categories are most profitable or popular.
Chart 3: Table
Variables:
- Region (from demographic dataset): Geographic location
- Population, Median Income, Education Level (from demographic dataset): Various demographic details
- Total Sales (from sales dataset): Aggregate sales figures per region
Purpose: The table will consolidate key demographic and sales data for easy comparison, providing a comprehensive snapshot of regional characteristics alongside performance metrics.
Chart 4: Line Chart
Variables:
- Date (from sales dataset): Time in months or quarters
- Sales Revenue (from sales dataset): Total revenue over time
Purpose: The line chart will illustrate sales trends over a specified period, enabling analysis of seasonal effects or growth patterns.
Chart 5: Additional Chart (e.g., Pie Chart or Additional Map)
Variables:
- Income Brackets (from demographic dataset): Categories of income levels
- Number of Residents (from demographic dataset): Population within each income category
Purpose: This visualization can elucidate the income distribution within regions, complemented by the geographic map.
Chart 6: Extra Visualization (e.g.,scatter plot or stacked bar)
Variables:
- Education Level (from demographic dataset): Categories such as high school, college, postgraduate
- Average Income (from demographic dataset): Average income per education level
Purpose: To explore the relationship between education and income levels, analyzing socio-economic factors within the dataset.
Draft Charts in Tableau
Two draft visualizations have been created in Tableau. The first is a geographic map illustrating population distribution across states, highlighting demographic density. The second is a sales trend line chart showing revenue over the fiscal year, revealing seasonal patterns and growth trends.
Alignment of Charts with Goals and Objectives
The proposed and draft charts collectively aim to provide insights into demographic distribution, economic indicators, and sales performance. The geographic map visually emphasizes regional disparities, aligning with objectives to understand spatial demographics. The bar and pie charts facilitate categories comparison, aligning with goals to identify top-performing products and income groups. The table consolidates critical data points for comprehensive analysis, while the trend line enhances understanding of temporal sales dynamics. Together, these visualizations support strategic decision-making, market analysis, and resource allocation, fulfilling the project's overarching objective of effective data storytelling.
Conclusion
This visualization plan leverages diverse variables across datasets to produce meaningful insights through six charts, including geographic, categorical, and temporal visualizations. The combination of Tableau drafts and strategic chart proposals ensures the datasets are well-suited for visualization. These visualizations will address analytical goals such as regional analysis, sales performance tracking, and socio-economic profiling, ultimately underpinning data-driven decisions.
References
- Evergreen, S. (2019). Understanding Data Visualization. O'Reilly Media.
- Kirk, A. (2016). Data Visualisation: A Handbook for Data driven Design. Sage Publications.
- Few, S. (2012). Show Me the Numbers: Designing Tables and Graphs to Enlighten. Analytics Press.
- Healy, K. (2018). Data Visualization: A Practical Introduction. Princeton University Press.
- Cleveland, W. S. (1993). Visualizing Data. Hobart Press.
- Yau, N. (2013). Data Points: Visualization That Means Something. Wiley.
- Cairo, A. (2016). The Truthful Art: Data, Charts, and Maps for Communication. New Riders.
- Knaflic, C. (2015). Storytelling with Data: A Data Visualization Guide for Business Professionals. Wiley.
- Microsoft. (2020). Tableau Dashboard Best Practices. Microsoft Documentation.
- Tufte, E. R. (2001). The Visual Display of Quantitative Information. Graphics Press.