Pearson Edexcel IAL IT – Unit 3 & 4 Comprehensive Notes

Specification Coverage

  • Unit 3 (IA2 – Written, 2 h)
    • Topic 12: Manipulating Data
    • Topic 13: Enabling Technologies
    • Topic 14: Using IT Systems in Organisations
    • Topic 15: Systems Development
    • Topic 16: Emerging Technologies
    • 80 marks ⇒ 50%50\% IA2 | 25%25\% IAL
  • Unit 4 (IA2 – Practical, 3 h)
    • Topic 17: Features of DB Solutions
    • Topic 18: Relational DB Concepts
    • Topic 19: Database Solutions
    • 80 marks ⇒ 50%50\% IA2 | 25%25\% IAL

Assessment Overview

  • Both papers assess all four AOs:
    1. Knowledge & Understanding
    2. Application
    3. Analysis
    4. Evaluation / Design
  • Practical exam supplies data files; answers recorded on electronic template.

Unit 3 – Introduction

  • IT integrates into communication, shopping, decision-making and study.
  • Course builds analytical, logical, problem-solving & practical skills.

Topic 12 – Manipulating Data

Learning Objectives

  • Maintain data integrity & governance.
  • Create/interpret data dictionaries.
  • Design validation, normalisation (1NF–3NF).
  • Build logical data models & ERDs.
  • Explain Big Data (5 Vs), infrastructure, mining, warehousing & analytics.

Data Integrity & Governance

  • Components: accuracy, consistency, completeness, reliability, security.
  • Tools: backups, validation, access control, audit trails (timestamp, user, action, source).

Data Dictionaries

  • Blueprint listing Table, Field, Description, Data-Type, Length, Validation, Constraints.
  • Supports ‘secure-by-design’.
  • Common SQL types: INT, DECIMAL, DATE, CHAR, VARCHAR, BOOLEAN, JSON.

Validation Techniques

  • Presence, Range, Lookup, List, Length, Format, Check-digit.
  • 3-phase cycle: Input → Check → Post-action (accept/ error).

Redundancy & Normalisation

  • Problems: storage cost, inconsistency, maintenance overhead.
  • 1NF1\text{NF} → atomic columns, primary key.
  • 2NF2\text{NF} → remove partial dependency.
  • 3NF3\text{NF} → remove transitive dependency.
  • Boyce-Codd (BCNF) stricter than 3NF3\text{NF} (exam not required).

Logical Data Models

  • Entities ↔ Tables; Attributes ↔ Fields; Relationships (1:1,1:n,n:m1:1, 1:n, n:m).
  • ERD shows crow-foot notation, PK/FK.

Big Data

  • 5 Vs: Volume, Velocity, Variety (structured / semi / un-str), Veracity, Value.
  • Collection sources: social media, IoT sensors, web logs, transactions, public data.
  • Storage: distributed file systems, NoSQL, warehouse, cloud (AWS, Azure, GCP).
  • Processing: Hadoop / Spark; real-time vs batch.
  • Security & access: silos, latency, privacy, compliance (GDPR).
  • Analytics: descriptive, predictive, prescriptive.

Topic 13 – Enabling Technologies

Virtualisation

  • VM vs Containerisation
    • VM: full OS, hypervisor; higher overhead; strong isolation.
    • Container: app-level, share kernel; lightweight; portable.
  • Benefits: resource utilisation, snapshots, scalability.
  • Drawbacks: complexity, cost, performance contention.

Distributed Systems

  • Independent nodes cooperate; used in web services, CDNs, blockchain.
  • Characteristics: concurrency, replication, fault-tolerance.
  • Issues: data consistency, latency, geo-location, security.

Human-Computer Interaction (HCI)

  • UX pillars: usability, consistency, feedback, efficiency, learnability, accessibility, aesthetics.
  • Modalities: Visual (layout, colour, hierarchy, responsive), Audio (speech I/O, auditory feedback), Haptic (force, tactile, biometric sensors).
  • Ergonomics: posture, device design, cognitive load, ISO 9241.

Cloud & Security

  • DBMS role-based controls, ACLs, encryption Symmetric  &  Asymmetric\text{Symmetric}\;\&\;\text{Asymmetric}, SSL/TLS certificates.

Topic 14 – Using IT Systems in Organisations

  • Operational support: automation, monitoring, customer service, data analysis, collaboration (cloud suites, PM software).
  • Transaction Processing ACID=(Atomicity, Consistency, Isolation, Durability)\text{ACID}=(\text{Atomicity, Consistency, Isolation, Durability}); EPOS; financial BACS/ACH.
  • CRM: marketing sync, loyalty schemes, retention, upselling.
  • MIS: record-keeping, dashboards, project decisions.
  • ITS: scheduling, routing, fleet telematics.
  • Expert systems: knowledge-base + inference engine.
  • Governance: continuity, disaster-recovery, risk (prob×impact), AUP.
  • Changeover: Phased, Big-bang, Parallel, Pilot.
  • Maintenance: perfective, adaptive, corrective; archiving; data silos.

Topic 15 – Systems Development

Project Management Essentials

  • Need for PM: complexity, resources, risk mitigation, QA, communication.
  • Success factors: clear objectives, stakeholder engagement, planning, skilled team, adaptability, on-time/on-budget.
  • SMART targets: Specific, Measurable, Achievable, Relevant, Time-bound.
  • Tools: Gantt, Network/PERT, Critical Path, Precedence tables, RISKS Score=Prob×ImpactScore=Prob\times Impact.

Methodologies

  • Waterfall phases: Requirements → Design → Implementation → Testing → Deployment → Maintenance.
  • Agile: iterative + incremental; Scrum events (Sprint plan, Daily stand-up, Review, Retrospective); Kanban, Lean.

Diagrams

  • IFD (context, L1, L2) show information sources, flows, processes, data stores.

Topic 16 – Emerging Technologies

Machine Learning

  • Supervised (labelled) vs Unsupervised (clustering, dimensionality).
  • Applications: NLP, speech recognition, image recognition, fraud detection.
  • Training → Model → Prediction; use of AI\text{AI} algorithms & big datasets.

AR / VR

  • VR: immersive headsets, training, design, therapy.
  • AR: overlays via mobiles / glasses; navigation, retail preview, Pokémon GO case.

Internet of Things (IoT)

  • Concept: sensors + connectivity + automation.
  • Infrastructure: sensors, networks (Wi-Fi, 4G/5G, LPWAN), embedded systems, storage (edge, fog, cloud).
  • Security challenges: privacy, unauth access, DoS, firmware, API\text{API}.
  • Design: high-level plan + IFD, risk mitigation, compliance.

Topic 17 – Uses & Features of DB Solutions

  • Why DB over spreadsheet: structured records, multi-user, scalability, queries, ACID.
  • Common apps: stock, ticketing, banking, e-commerce, research.
  • Features: CRUD, input forms, validation, reports, relational links.
  • User needs: data structure, interface type (GUI/CLI), accessibility (WCAG), evaluation.
  • UX considerations: clear labels, help, error msg, list/combo, protected PK, intuitive hierarchy.

Topic 18 – Relational DB Concepts

Structuring Data

  • Tables → Records → Fields (atomic).
  • Datatypes: Short/Long Text, Number (byte,int,long,double,decimal), DateTime, Currency, Boolean, AutoNumber.
  • Keys: Primary, Foreign, Composite.
  • Relationships: 1:1,1:n,n:m1:1, 1:n, n:m (resolved with junction table).
  • Referential integrity; cascade update/delete.

Forms & Validation

  • Presence Is Not NullIs\ Not\ Null, Range, InputMask (e.g. >L000 product code), Defaults (Now())(Now()), Lookups, Lists.
  • Macros for save/cancel, conditional visibility, MsgBox, RunMenuCommand.

Topic 19 – Database Solutions

Queries

  • Filters vs Saved queries; criteria: wildcards (*, ?), Between, Date(), DatePart.
  • Aggregate functions: COUNT,SUM,AVGCOUNT, SUM, AVG; Grouping.
  • Calculated fields e.g. Total:[Quantity]∗[Itemprice]Total:[Quantity]*[Item_price].

Reports

  • Wizard, grouping, totals, headers/footers, layout editing.
  • Calculated controls: =Sum([Price])=Sum([Price]), =Now()=Now(), expressions in labels.

Forms & UI

  • Menus, dashboards, command buttons.
  • Combo/list boxes, option groups, checkboxes, sub-forms.
  • Macros: navigation, filtering, import/export (ImportExportSpreadsheet), program flow (If…ElseIf), UI management (OpenForm).

Data Import / Export

  • CSV, Excel, PDF, HTML.
  • Integration with cloud & other apps via APIs.

Testing & Evaluation

  • Test plan: test ID, description, expected, actual, pass/fail.
  • Data validation boundary tests, error-path, user acceptance (UAT).
  • Evaluation criteria: meets requirements, usability, performance, security, maintainability; suggest improvements.