Learning Aims and Objectives:
Aim: 

Objectives:
1. By the end of this week's page students will be able to demonstrate where to apply specific data types for data that appears in databases, tables or datasets
 
2. By the end of the week's page students will be able to explain and identify where specific data can be decomporsed and extracted from given scenarios in to appropriate data tables.

3. By the end of the week's page students will be able to demonstrate the process of normalisation and explain the purpose of it in given scenarios situations.

4. By the end of the week's page students will be able to reflect on the interrelationships between data type and data transformation.

3.4 Data types

3.4.1 The definition of common data types, their purpose, and when each is used:

Integer (Whole numbers)

What/why: Whole numbers (no decimal point). Efficient for counting, indexing, quantities, IDs.
When to use: Anything that can’t have fractions: number of users, attempts, port numbers, stock counts.
Gotchas: Watch out for range limits (e.g., 32-bit vs 64-bit) and accidental division that produces decimals.

Real (Floating-point / Decimal)

What/why: Numbers with fractional parts.
When to use: Measurements (temperature, CPU load), ratios, scientific values.
Gotchas: Floating-point rounding error (binary floating point). For money, prefer fixed-point/decimal types.

Character (Char)

What/why: A single textual symbol (one character).
When to use: Fixed-width codes (Y/N flags), single-letter grades, check digits.
Gotchas: In Unicode, a “character” users see may be multiple code points (accents/emoji). Many systems still treat CHAR as a single byte/letter in a given encoding.

String (Text)

What/why: Ordered sequence of characters (words, sentences, IDs).
When to use: Names, emails, file paths, JSON blobs-as-text, logs.
Gotchas: Validate length and content; normalise case; be mindful of Unicode, whitespace, and injection risks.

Boolean (True/False)

What/why: Logical truth value with two states.
When to use: Feature flags, on/off, pass/fail, access granted/denied.
Gotchas: In databases and CSVs, Booleans are often stored as 1/0, TRUE/FALSE, Y/N—be consistent when importing/exporting.

Date (and Date/Time)

What/why: Calendar date (optionally time and timezone).
When to use: Timestamps for logs, booking dates, certificate expiry, backups.
Gotchas: Time zones and daylight saving; choose UTC for servers, localise only for display. Use proper date types, not strings, for comparisons and indexing.

BLOB (Binary Large Object)

What/why: Arbitrary binary data (files) stored as a single value.
When to use: Images, PDFs, compressed archives, firmware, encrypted payloads—when you must keep the bytes intact.
Gotchas: Large size affects backups and query speed; consider storing large files in object storage (S3, Azure Blob) and keep only a URL/metadata in the database.

"Mia's Sandwich Shop"

Task 1.
Using the above video, in small groups of no larger than 3 discuss the issues that the company are having. 
Identify what data is being recorded
Suggest/Agree a solution for them.


Task 2. 
In your groups identify the tables that might need to appear in a database, use the process of Normalisation as well as the Computational Thinking principles of Decomposition, Abstractions and Pattern Recognition.

Task 3. 
Present in your groups the findings from your normalisation.Explain/justify your reasoning around the choices made.

 

create an infoamtive presentation that discusses and exaplains the the following areas of databases;
What a Primary key is and its function, use examples to further show your understanding
What a Foriegn key is and its function, use examples to further show your understanding
What a Composite key is and its function, use examples to further show your understanding
What a relational database is, and why would you use one.
 

3.4.2 The interrelationships between structured data, unstructured data and data type.

In today’s digital world, organisations gather information in many different forms – from neatly organised spreadsheets of customer transactions to complex streams of emails, images, and social media posts. To make sense of this, we look at three key concepts: structured data, unstructured data, and data types.

Structured data is highly organised, stored in predefined formats such as rows and columns within a spreadsheet or database. This makes it straightforward to search, filter, and analyse. Examples include account numbers, dates of birth, and purchase amounts.

Structured Data

• Organised in a predefined format (rows, columns, fields).

• Easily stored in databases (SQL, relational systems).

• Examples: customer IDs, transaction amounts, dates, sensor readings.

By contrast, unstructured data has no fixed format or schema, making it harder to process. It includes content such as emails, audio recordings, images, videos, or free-text survey responses. While it carries rich insights, it requires more advanced tools and techniques to interpret.

Unstructured Data

• No fixed schema or easily searchable structure.

• Stored in raw formats like documents, images, videos, social media posts.

• Examples: customer service call recordings, CCTV footage, email bodies.

At the foundation of both lies the concept of data types. A data type defines how a particular piece of information is stored and used – for instance, an integer for whole numbers, a string for text, or a blob for multimedia. Structured systems rely on data types to keep information consistent, while unstructured data is often stored in broader types like text fields or binary objects to preserve its form.

Together, these three elements form the backbone of how data is represented, stored, and ultimately transformed into meaningful information.

Examples in Practice

Case Studies

Case Study 1: Healthcare – NHS Patient Records

• Structured: Patient demographic data (NHS number, date of birth, appointment dates).

• Unstructured: Doctor notes, x-ray images, voice dictations.

• Interrelationship: Structured records (like appointment schedules) link to unstructured evidence (x-rays stored as BLOBs). The combination provides a holistic medical history.

• Application: AI systems analyse unstructured scans, while SQL systems schedule appointments. Both need data types (integer IDs, date, blob images).

Case Study 2: Cybersecurity – Threat Detection

• Structured: Firewall logs (IP addresses, timestamps, action taken).

• Unstructured: Email attachments, phishing attempts, PDF exploits.

• Interrelationship: Structured logs identify when and where data entered; unstructured payloads (attachments) must be analysed with ML tools. Data types (IP as string, timestamp as date, file as blob) define how each element is stored and processed.

• Application: SIEM (Security Information and Event Management) platforms like Splunk combine both data types to detect anomalies.

Case Study 3: Retail – Amazon Recommendations

• Structured: Order history (user ID, product ID, purchase date).

• Unstructured: Customer reviews, product images, Q&A responses.

• Interrelationship: Data types underpin storage (strings for reviews, integers for quantities, blobs for images). Machine learning models merge structured purchase histories with unstructured reviews to improve recommendations.

Linked to: Core Paper 2 – Data (1.2.1, 1.2.2, 1.2.3)
Topic focus: Understanding the interrelationship between structured data, unstructured data, and data types

By the end of this 25-minute activity, you will be able to:
1. Differentiate between structured and unstructured data.
2. Identify how data types exist within both forms of data.
3. Explain how these three concepts (structured, unstructured, and data types) interrelate in real-world digital systems.


In digital support and cyber security environments, you’ll often manage both structured and unstructured data.
Understanding how data types fit into these categories helps professionals make decisions about:
- Storage (e.g., database vs cloud object store),
- Processing (e.g., SQL query vs machine learning model),
- Security and access control (structured tables vs open media files).

These ideas are interconnected:
- Structured data relies heavily on defined data types (e.g., Integer, Boolean, Date).
- Unstructured data often contains or implies data types inside its content (e.g., text or images may include embedded timestamps or numbers).
- Effective data transformation or classification depends on identifying and linking these types together.

Discussion Starter
Ask:
“If you were the IT support technician for a hospital, what kinds of data would you need to store?”


Then ask:
“Which of those are structured and which are unstructured?”


Step 2 – The Sorting Challenge (10 minutes)
In Pairs or small groups (2–3 students)
Using the provide  mixed dataset samples like the one below, either printed or on screen.


Task Instructions
Each group should:
Categorise each example as: Structured data, Unstructured data, (or Semi-structured data, if appropriate).

Identify the data type(s) found or implied in each example
(e.g., text/string, integer, Boolean, date/time, float).
Draw or describe how structured/unstructured data and data types connect.

Sketch a small diagram showing arrows between:
Structured data → relies on → defined data types
Unstructured data → contains → mixed/hidden data types

Allow 10 minutes.
Each group should explain one example to the class.

Step 3 – Reflection Discussion (7 minutes)
Questions for Reflection


Step 4 – Mini Summary Task (3 minutes)
Writes a short paragraph in your own words to answer:

“Explain how structured data, unstructured data, and data types interrelate in digital systems. Give an example from a real-world situation.”

Example student response:
“Structured data, like a customer database, uses fixed data types such as integers and dates to ensure consistency. Unstructured data, such as customer emails, still contains text and time stamps but lacks a fixed schema. Both can be linked — for instance, a support system may combine structured ticket records with unstructured message logs to identify issues faster.”
 

3.4.3 Understand the interrelationships between data type and data transformation.

In digital support and cyber security roles, you’ll often manage data that comes from multiple sources — databases, websites, sensors, and even user input forms.

For that data to be useful, reliable, and secure, it must be stored in the correct data type and transformed into the right structure or format for use.

The interrelationship between these two ideas — data types and data transformation — is crucial to maintaining accuracy, preventing data corruption, and securing systems from attack.

Understanding Data Types

A data type defines what kind of data can be stored and how it’s processed.
Computers must know how to interpret the information they are given.

Think of data types as the “containers” that hold different kinds of information.
Just as you wouldn’t pour soup into a paper bag, you wouldn’t store a date as text if you plan to sort by time later

What Is Data Transformation?

Data transformation means converting, cleaning, or reshaping data so that it becomes usable, accurate, or compatible with another system.

Transformations can include:

• Changing data from one type to another (e.g. String → Integer).
• Reformatting dates (MM/DD/YYYY → DD/MM/YYYY).
• Cleaning messy data (Y, Yes, TRUE → TRUE).
• Combining or splitting fields (e.g. First name + Surname → Full Name).

These transformations make data usable, comparable, and secure.

How Data Types and Data Transformation Are Connected?

These two concepts constantly interact:

In cyber security, knowing data types helps prevent:

• SQL Injection: A hacker could enter malicious text in a numeric field.
• Buffer Overflow: Supplying too much text to a field expecting a smaller data type.
• Data Leakage: Incorrect transformations might expose sensitive data.

Proper transformations with correct data typing protect systems from these risks.

You are helping your college’s IT department combine attendance data from two different systems.

One system exports CSV data like this:

Before the data can be analysed, you must:

1. Convert "00123" → Integer (to remove text formatting and leading zeros).
2. Convert "Yes" → Boolean TRUE.
3. Convert "6.5" → Float (so you can calculate averages).

The transformations are only possible if you understand the data types involved.

3.4.4 Be able to make judgements about the suitability of using structured data, unstructured data, data types, and data transformations in digital support and security.

Data Decisions in Digital Support and Security
Duration: 30 minutes
Level: Pearson T-Level in Digital Support and Security
Format: Small group task (3-4 learners)
Final Output: Short presentation (3-5 minutes per group)

Learning Objective
By the end of this session, you will be able to:
Make reasoned judgements about the suitability of structured vs unstructured data.
Evaluate how different data types (e.g., integer, string, Boolean, date/time) and data transformations (e.g., normalisation, aggregation, cleaning) impact digital support and security decisions.
Communicate your findings effectively to both a technical and non-technical audience.

Stage 1 - Scenario Briefing (5 mins)
You are part of a digital support and security team at a college that manages:
A ticketing system for IT support requests (structured data).
Incident reports written by users and technicians (unstructured data).
Network logs collected from servers and routers (semi-structured data).

Your manager has asked you to decide which type of data and data transformations are most suitable for improving the college’s cyber-incident response system.

Stage 2 - Research & Discussion (10 mins)
As a group:
Identify examples of structured, unstructured, and semi-structured data in the scenario.
2. Discuss how data types (e.g., integers, text, Boolean) influence how the information is stored and analysed.
3. Explore what data transformations (e.g., cleaning, filtering, converting formats, normalising) could make the data more useful.
4. Evaluate the benefits and drawbacks of using each data form in the context of:

Security monitoring (e.g., threat detection, auditing)

Data integrity and compliance

System performance

Use this guiding question:
“Which data type and transformation process gives us the most secure and useful insight for decision-making?”

Stage 3 – Judgement and Decision (10 mins)Create a short decision table or mind map comparing your options.
 

Use your table to justify your final judgement about which type(s) of data and transformations are most suitable for the college’s digital support and security needs.

Stage 4 - Mini Presentation (5 mins per group)
Each group presents:

Their chosen data type(s) and transformation(s)
The judgements made and the reasoning behind them
How their approach supports security operations (e.g., faster response, data reliability, GDPR compliance)

Presentation audience: The class (acting as the IT management team).

Extension / Differentiation

Stretch: Ask students to link their decision to real-world tools (e.g., Splunk, Wireshark, SQL Server, Power BI).
Support: Provide example datasets and a glossary of data types and transformation methods.

Exam Questions:
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