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Content Area 6

Data

Master data management, analysis, and visualization techniques. From data collection and transformation to advanced analytics and business intelligence systems.

5
Level
12
Subtopics
50-65 hours
Study Time

Advanced Data Management Skills

This advanced content area covers comprehensive data management, from basic concepts to enterprise-level analytics and business intelligence systems.

Core
Content Area
12
Subtopics
50-65
Study Hours
Pearson
Qualification

Data Management and Analytics

6.1 Data, information, knowledge and sources

Available

Including AI and sensors, ethical practices and value metrics for understanding the data hierarchy and modern data acquisition.

Key Learning:

  • Data, information, knowledge, and wisdom hierarchy (DIKW pyramid)
  • Traditional data sources: databases, files, user input, surveys
  • Modern data sources: AI-generated data, sensor networks, IoT devices
  • Social media data, web scraping, and API-based data collection
  • +3 more topics...
Learn 6.1

6.2 Transforming data

Available

Manipulate, analyse, process data through various transformation techniques and analytical methods.

Key Learning:

  • Data manipulation: filtering, sorting, grouping, aggregation
  • Data cleaning: handling missing values, outliers, duplicates
  • Data transformation: normalization, standardization, encoding
  • Data analysis techniques: statistical analysis, trend analysis
  • +3 more topics...
Learn 6.2

6.3 Taxonomy

Available

Quantitative and qualitative, structured and unstructured, representations discrete, continuous, categorical.

Key Learning:

  • Quantitative data: numerical measurements, counts, ratios
  • Qualitative data: categorical, ordinal, nominal classifications
  • Structured data: databases, spreadsheets, formatted files
  • Unstructured data: text documents, images, audio, video
  • +4 more topics...
Learn 6.3

6.4 Data types

Available

Integer, real, character, string, Boolean, date, Blob for different programming and database contexts.

Key Learning:

  • Integer types: signed, unsigned, different bit sizes (8, 16, 32, 64)
  • Real/floating-point types: single precision, double precision, decimal
  • Character and string types: ASCII, Unicode, UTF-8, variable/fixed length
  • Boolean types: true/false, binary representation, null handling
  • +4 more topics...
Learn 6.4

6.5 Data formats

Available

JSON, text, CSV, UTF-8, ASCII, XML and when to use them in different scenarios and applications.

Key Learning:

  • JSON (JavaScript Object Notation): web APIs, configuration, NoSQL databases
  • Plain text formats: human-readable, cross-platform compatibility
  • CSV (Comma-Separated Values): spreadsheet data, data exchange, bulk imports
  • Character encoding: UTF-8 (Unicode), ASCII, legacy encodings
  • +4 more topics...
Learn 6.5

6.6 Storage structures

Available

Files, directories, hierarchies, and metadata organization for efficient data management and retrieval.

Key Learning:

  • File systems: hierarchical directory structures, file allocation
  • Directory organization: logical grouping, naming conventions, permissions
  • File hierarchies: parent-child relationships, path resolution
  • Metadata: file attributes, creation dates, permissions, checksums
  • +3 more topics...
Learn 6.6

6.7 Data dimensions

Available

Six Vs volume, variety, variability, velocity, veracity, value, quality assurance validation, verification, reliability, consistency, integrity, redundancy, and maintenance factors time, skills, cost.

Key Learning:

  • Volume: data size challenges, storage scaling, processing capacity
  • Variety: multiple data types, sources, formats, integration challenges
  • Variability: inconsistent data flows, seasonal patterns, anomaly detection
  • Velocity: real-time processing, streaming data, latency requirements
  • +7 more topics...
Learn 6.7

6.8 Data systems

Available

Wrangling steps structure, clean, validate, enrich, output, core functions input, search, save, integrate, organise, output, feedback. Data entry errors transcription and transposition and ways to reduce them, and cost or time trade offs.

Key Learning:

  • Data wrangling pipeline: structure → clean → validate → enrich → output
  • Data structuring: schema design, normalization, data modeling
  • Data cleaning: error detection, correction, standardization processes
  • Data validation: business rules, format checking, completeness verification
  • +9 more topics...
Learn 6.8

6.9 Data visualisation

Available

Graphs, charts, tables, reports, dashboards, infographics, and selection factors for effective data presentation.

Key Learning:

  • Chart types: bar charts, line graphs, pie charts, scatter plots, histograms
  • Advanced visualizations: heat maps, treemaps, network diagrams, geographic maps
  • Table design: sorting, filtering, pagination, responsive layouts
  • Report generation: structured layouts, executive summaries, detailed analysis
  • +6 more topics...
Learn 6.9

6.10 Data models

Available

Hierarchical, network, relational, selection factors, and how to draw them for different use cases.

Key Learning:

  • Hierarchical data model: tree structures, parent-child relationships, XML
  • Network data model: many-to-many relationships, graph databases, social networks
  • Relational data model: tables, primary/foreign keys, SQL databases
  • NoSQL models: document stores, key-value pairs, column families, graph databases
  • +5 more topics...
Learn 6.10

6.11 Data access

Available

Across platforms permissions and mechanisms RBAC, rule based access, APIs for secure and controlled data access.

Key Learning:

  • Cross-platform access: web services, mobile apps, desktop applications
  • Permission systems: user roles, access levels, inheritance models
  • Role-Based Access Control (RBAC): roles, permissions, user assignments
  • Rule-based access control: dynamic permissions, context-aware security
  • +5 more topics...
Learn 6.11

6.12 Data analysis tools

Available

Warehouses, lakes, marts, data mining, reporting, and business intelligence such as CRM use cases.

Key Learning:

  • Data warehouses: dimensional modeling, OLAP, historical data storage
  • Data lakes: raw data storage, schema-on-read, big data processing
  • Data marts: departmental data subsets, specialized analytics, faster queries
  • Data mining techniques: pattern recognition, machine learning, predictive analytics
  • +6 more topics...
Learn 6.12

Key Data Management Areas

Data Analysis

Analytics, visualization, and business intelligence

Data Architecture

Warehouses, lakes, and storage systems

Data Integration

ETL processes and system connectivity

Data Quality

Validation, cleansing, and governance

Learning Resources

Data Management Guides
Comprehensive data handling and processing guides
Practical Exercises
Hands-on data analysis and visualization projects
Analytics Tools
SQL, Python, R, and BI platform tutorials
Case Studies
Real-world data projects and solutions

Assessment Information

Data skills are assessed through practical projects, data analysis tasks, visualization creation, and database design scenarios.

Data Analysis Projects
Real datasets, cleaning, transformation, and insights
Visualization Design
Creating effective charts, dashboards, and reports
System Design
Database modeling, ETL processes, and architecture

Master Data Management

Begin with data fundamentals and progress through transformation, modeling, and advanced analytics. These skills are essential for data-driven decision making and business intelligence.

Start with 6.1 Data FundamentalsBack to Core Papers