Introduction: Data Science (CARDS)

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Explain how hospitals can use time-series data to prepare for flu season.

(3 marks)

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Sample Answer: Hospitals record flu cases each week over many years. By analysing this time-series data, they can predict when flu cases will increase. This helps them prepare by ordering vaccines, scheduling extra staff, and ensuring medicine is ready for patients.

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Discuss the benefits and risks of using data science to manage hospital patient records.

(5 marks)

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Sample Answer:
Benefits: Data science helps doctors find patient history quickly, predict risks, and improve treatments. It also reduces errors in medication.
Risks: Patient records may be hacked, leading to privacy breaches. There may also be errors in data that give incorrect results. Hospitals must protect data with encryption and strict access rules.

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A hospital introduces a data science system that uses AI to predict patient recovery time. Justify how this system can improve patient care and also explain the ethical issues.

(5 marks)

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Sample Answer:
The system can improve patient care by helping doctors make faster and more accurate decisions about treatment plans. It can reduce hospital stay time and improve use of resources. However, ethical issues include privacy of patient data, bias in AI predictions, and the need for patient consent before using their information. Hospitals must balance benefits with strong ethical practices.

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What is Data Science?

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• Data Science is the study of data (facts, numbers, information) to find patterns and make decisions.

• Enterprises (businesses, schools, hospitals, shops) use data science to improve their work.

• Data can be numbers (sales, temperature, exam marks) or words/images (customer reviews, photos, social media posts).

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Steps in Data Science

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1. Collect data – gather information (e.g., survey results, sales records, website visits).

2. Clean data – remove mistakes, duplicates, or missing parts.

3. Analyse data – use tools like Excel, SQL, or Python to look for trends.

4. Visualise data – create graphs, charts, dashboards to explain the information.

5. Make decisions – use the insights to improve systems (e.g., marketing, customer service, stock control).

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Examples of Data Science use in Enterprises

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• School: analysing student attendance to see which days students miss most.

• Healthcare: hospitals predicting flu season from past patient records.

• Retail: online shops recommending products based on past purchases.

• Sport: soccer clubs using data science to track player performance.

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Numerical Data (Quantitative)

• Definition: Data that is measured in numbers.

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Examples:

• Number of truanting (41)

• Days absent; (43)

• Age of students (14, 15, 16)

• Temperature (22°C, 30°C)

• Sales numbers (120 shirts sold)

• Exam marks (85%, 60%)

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Categorical Data (Qualitative)

• Definition: Data that describes groups, names, or categories.

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Examples:

• Favourite colour (red, blue, green)

• Type of phone (Samsung, Apple, Oppo)

• Food choice (vegetarian, vegan, meat eater)

• Vehicle features: (auto matic, reverse cam, leather seats, adaptive cruise control, blindspot alert)

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Categorical Data (Qualitative)

• Definition: Data that describes groups, names, or categories.

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Examples:

Attendance details: (late to class, early leaver, truanting)

• Favourite colour (red, blue, green)

• Type of phone (Samsung, Apple, Oppo)

• Food choice (vegetarian, vegan, meat eater)

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Text Data

• Definition: Words and sentences collected as data.

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Examples:

• Customer reviews (“Great product!”)

• Social media comments

• Emails and SMS