Data science

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Last updated 3:54 AM on 10/12/26
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13 Terms

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Quantitative data

numerical data that can be measured. E.g age, height. Represented through numerical data types.

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Qualitative data

Descriptive data that can not be measured.Focuses on description and feelings E.g opinions, emotions. Also called categorical since it can sorted by category.

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Uses of quantitative data

Comparisons, trends,

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Nominal data

Categorical data that is used to label unranked data.E.g. gender

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Ordinal data

Categorical, statistical data that has an order. E.g Ranks and tiers

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Interval data

Quantitative data that is measured by equal intervals. E.g atar, IQ scores

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Ratio data

A numerical measurement scale with ordered,equal intervals and no negative values (absolute zero), and full mathematical operability. E.g height, weight. Any quantitative data that allows for comparisons.

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Define data sampling

process of selecting a subset of data from a larger population for analysis

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What is manual active collection

involves actively engaging with participants or customers to collect data. E.g surveys, interviews

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What is manual passive collection

involves collecting data without actively engaging with participants or customers. E.g observations, and existing data

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What are active computerised methods

involves using technology to actively collect data from participants or customer. E.g online surveys and mobile apps

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What are passive computerised data collection

involves collecting data automatically through technology, without actively engaging with participants or customers. E.g web analytics, tracking devices

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Pros and cons of computerised data collection

Pros:

  • Efficiency and Accuracy due to lower likelihood of human error + automated validation

  • Cost-effectiveness: Delivers long-term cost savings by reducing the need for manual labor, paper, and physical resources.

Cons:

  • High Upfront Costs: Initial investment in software, hardware, infrastructure, and setup can be expensive

  • Training & Adaptation Required: Staff need time and training to adapt to new automated systems and tools.