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1. Examine the following datasets:
Quantitative Data:
Number of petals on a flower: Height of flowers (in centimeters):
Rose: 32 Rose: 45
Lily: 24 Lily: 55
Sunflower: 89 Sunflower: 120
Tulip: 16 Tulip: 30
Qualitative Data:
Colour of flowers: Fragrance intensity:
Rose: Red Rose: Strong
Lily: White Lily: Mild
Sunflower: Yellow Sunflower: None
Tulip: Pink Tulip: Moderate
These datasets contain both quantitative data (number of petals and height) and qualitative data (colour and fragrance
intensity) for different types of flowers (Rose, Lily, Sunflower, Tulip).
a. Discuss the differences between quantitative and qualitative data interpretation.
b. Describe the methods and techniques commonly used for interpreting quantitative and qualitative data, highlighting
their respective strengths and limitations.
2. You are tasked with analysing the performance of a company's sales across different regions over the past year. How
would you utilise data visualisation techniques to present this information effectively to the company's stakeholders
during a quarterly review meeting? Describe the types of visualisations you would use and explain how they would help
convey the sales trends and patterns to the audience.
#Experiential Learning
Lab
Ask students to collect data of different coloured objects in the Lab and record it in a spreadsheet. Create a
basic bar chart to visualise the collected data using spreadsheet software. Later, ask students to present their
bar charts, followed by a brief discussion on the importance of data quality and ethical considerations in AI.
180 Artificial Intelligence Play (Ver 1.0)-IX

