Select, create and present data visualisations based on a dataset
Assessment
Report
Select, must include:
an appropriate visualisation method for the data set (for example, when looking at the relationship between height and weight data, a line graph may be most appropriate)
Create, must include:
an appropriate visualisation software (for example, MS Excel)
You complete this Learning Outcome by doing this one first: Unit 03: LO 5 - 5.4 - Data (source, cleanse, save). The visualisations you create at the end are for this standard
How to select, create, and present data visualizations based on a dataset.
This is a theoretical guide and can be applied in various tools like MS Excel or Python libraries like Matplotlib or Seaborn.
Understand the Data: First, make sure you understand what the data is about. Is it showing a trend over time, parts of a whole, or categories? This understanding will guide your choice of visualization method.
Determine the Goal: What's the main point you want to get across? Knowing this will help narrow down which type of visualization to use. For example, if you're looking at the relationship between height and weight, a scatter plot or line graph might be the most appropriate.
Assess Data Type and Size: The type and amount of data you have will influence your choice. Some methods are better suited for large datasets, while others work well for showing a few simple points.
Choose Software: You can use various tools to create the visualization. Excel is a straightforward option, especially for basic visualizations like bar, line, and pie charts. Python, with libraries like Matplotlib or Seaborn, is more suitable for complex or large-scale data.
Prepare Data: Before you can visualize the data, it usually needs to be cleaned and structured. You may need to remove outliers, handle missing values, or aggregate data points.
Create the Visualization: Use the software to make the chart or graph. Add labels, titles, and legends to make it easy to understand. Make sure to also choose color schemes that are easily interpretable.
Test: Before finalizing your visualization, test it to make sure it accurately represents the data and is easy to understand. You may need to make adjustments to improve clarity.
Context: Always present your visualization within the context of the data you're using. Explain any potential limitations of the dataset or the method you used.
Narrative: Help your audience understand what they're looking at by creating a narrative around the data. Explain the key takeaways and what actions should be taken based on the data.
Accessibility: Ensure that your presentation medium allows for all audience members to easily see and interpret the visualization. This might involve adjusting the size or contrast of the visual elements.
Review and Revise: After presenting, be open to questions that may help you identify any points of confusion and refine the visualization for future use.
Following these steps can help you create a data visualization that is both effective and insightful.