Page 154 - CodePilot V5.0 C7

INTRODUCTION TO DATA AND ITS TYPES
Data refers to a collection of facts and information in various forms, such as numbers, text, sound

or images. It can be measured, collected and analysed, often visualised with graphs. Raw data is
unprocessed and may contain errors, which are corrected during processing.
Data is categorised into two types:

Structured data: Organised in a specific format, making it easy to search and analyse, often
found in relational databases.

Unstructured data: Does not follow a specific format, including text documents, images and
videos, requiring extra processing to organise and analyse.

EXAMPLES OF DATA SCIENCE
Here are some examples of data science:

Healthcare: Data science helps predict disease outbreaks by
analysing past data, environmental factors and population
information. This enables early detection, quicker responses

and resource management.

Finance: In finance, data science helps with credit scoring. By
using machine learning, we analyse a person’s financial history,
job status and income to predict how likely they are to repay
loans. This helps create more accurate credit scores.

Retail: In retail, data science helps businesses understand
customers by analysing their shopping habits, personal details
and online behaviour. This allows for personalised offers and
recommendations tailored to each customer’s preferences.

E-commerce: Data science is used to create recommendation
systems that suggest new products based on user preferences
and past interactions. This enhances user engagement, boosts

sales and improves the shopping experience.

Entertainment: Data science is used to recommend films, shows or
music by analysing user interactions, preferences and viewing habits.
This personalisation increases satisfaction and keeps users engaged.

152
CodePilot (V5.0)-VII