Course Code: datamanaandpresenttechnic
Duration: 56 hours
Course Outline:

Data Management
 What is Data Management
Data Ethics
 Introduction to Data Ethics
 Goals of Data Ethics
 Risks of Unethical Data Handling
 Common Types of Data Bias:
o Confirmation Bias
o Outliers Bias
o Selection Bias
o Rush-to-Solve Bias
o Availability Bias
o Anchor Bias
 Key Data Ethics Activities
 Establishing Ethical Data Culture
Data Governance
 What is Data Governance
 Why is Data Governance Important?
 Core Principles of Data Governance
 Roles and Responsibilities
Data Architecture
 What is Data Architecture
 Principles and Components
 Frameworks and Best Practices
 Roles in Data Architecture
Data Modelling
 What is Data Modelling
 Difference Between Data Architecture and Data Modelling
 The 3 Levels of Data Models:
o Conceptual model
o Logical Data Model
o Physical Data Model
 Data Modelling Process
 Benefits of Data Modelling
Data Storage and Operations
 What is Data Storage and Operations
 Importance and Daily Management Activities
 Key Attributes of Storage Management
 Best Practices
Data Security
 Introduction to Data Security
 Importance, Goals, and Principles
 Types of Data Security
 Data Security Risks and Threats:
o Accidental Exposure
o Phishing
o Malware
o Insider Threats
o Password Attacks
o Denial-of-Service (DoS)
o Man-in-the-Middle (MITM)
o SQL Injections
o Zero-day Exploits
 Data Security Policies and Best Practices
 Data Ethics and Compliance
Data Integration
 What is Data Integration
 Examples and Importance
 Techniques:
o Manual
o Middleware
o Application-Based
o Uniform Access
o Data Warehousing
o Virtualization
o ETL
o ELT
 Best Practices and Tools
Document and Content Management
 What is Document and Content Management
 Why It Is Needed
 Overview of DMS, CMS, and ECMS
 Best Solutions
WEEK 2
Master Data Management (MDM)
 What is Master Data and Reference Data
 Examples and Differences
 MDM Overview and Steps to Implement:
o Capabilities
o Styles
o Domains
o Current Data Environment
o Business Goals
o Sponsorship
o Teams
o Tools
o Quick Wins
o Monitoring
Data Warehousing and BI
 Introduction to Data Warehousing and BI
 Components, Data Marts, and Data Lakes
 Applications and Strategy Steps
Metadata Management
 What is Metadata and Metadata Management
 Types of Metadata
 Metadata Activities:
o Requirements
o Strategy
o Architecture
o Maintenance
o Reporting
 Tools
Data Quality
 Overview of Data Quality and Dimensions
 Improvement Process
Big Data and Data Science
 Introduction and Case Studies
 Key Activities, Deliverables, and Statistical Concepts
 Data Centrality and Dispersion
Data Communication and Presentation Techniques
 What is Data Communication
 Communicating with Data
 What Makes an Effective Data Communication
 Effective Communication Examples
 Visual Perception:
o Order
o Clarity
o Relationships
o Convention
 Visual Design and Its Application to Data Graphs
 Components of a Data Visualisation
 Different Types of Graphs
 Deadly Sins of Graph Design
 How to Avoid Being Misled with Graphs
 Create a Clear Graph
 Bringing Out the Story with Colour and Formatting
Data Analytics and Reporting
 Data Analytics and Reporting - using data to derive insights
 Generating meaningful reports
 Support decision-making processes
 Data analysis techniques
 Data visualization
 Reporting tools to enable effective communication of data insights to stakeholders
Data Narratives and Storytelling
 Analytics Value Chain
 Uncovering the Context
 Anecdote: Lessons from Work and Context
 Fundamental Data Narratives
 Turning Your Graph into a Story
 Steps to Creating a Powerful Visualisation
 Data Story Walkthroughs
Practical Applications of Data Management
 Business Decision Making
 Operational Efficiency
 Stakeholder / Customer Relationship Management
 Risk Management and Compliance
 Data-Driven Innovation
 Performance Monitoring and Reporting
 Data-backed Marketing Strategies
End