Foundation in IOTBiz, DSBiZ, AIBiZ, IRBiZ, DEBiZ

Durations:

5 Days

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Fully Funded

Benefits Obtained :

About Course

5-day course covering IoT, Data Science, AI, Incident Response, and Data Ethics. Designed for business leaders and decision-makers. Prerequisites include basic business knowledge and understanding of IT and cybersecurity concepts.

Course Objectives:

Who Should Attend?

Business leaders and decision makers, including C-level executives, project managers, HR leaders, Marketing and Sales leaders, and technical sales consultants.

Pre-requisite

  • Should have a working knowledge of general business concepts and practices.
  • Should also have a basic understanding of information technology (IT) resources and systems, including networks, computers, and other digital devices used in an enterprise setting
  • General understanding of cybersecurity concepts.
  • Should have a working knowledge of general business concepts and practices.
  • Should also have a basic understanding of Artificial Intelligence and or Data Science.

Course Curriculum

Module 1 – IoTBIZ™: Internet of Things for the Business Professional (Exam IOZ-110)

Topic A: Defining IoT for Business Leaders
• IoT Ingredients
• IoT Business Strategies

Topic B: IoT Infrastructure
• System of Systems
• Identify Resources

Topic C: Business Benefits and Challenges of IoT
• Business Considerations
• Security Concerns
• Organizational and Societal Impacts

Topic D: Real World Applications for IoT
• IoT Market Sectors
• IoT Implementation

Topic E: Best Practices for IoT Implementation

Topic A: Data Science Fundamentals
• What is Data Science?
• Types of Data
• The Data Science Lifecycle

Topic B: Data Science Implementation
• Data Acquisition and Preparation
• Data Modeling and Visualization
• Data Science Roles

Topic C: The Impact of Data Science
• Benefits of Data Science
• Challenges of Data Science
• Business Use Cases for Data Science

Topic A: AI Fundamentals
• A Brief History of AI
• AI Concepts

Topic B: Functions of AI in Business
• Improve User Experiences
• Segment Audiences
• Secure Assets
• Optimize Processes

Topic C: Implementing Business Requirements for AI
• Identify Design Requirements
• Identify Data Requirements
• Identify Risks in Implementing AI
• Develop an AI Strategy

Topic A: Assessment of Information Security Risks

  • The Importance of Risk Management
  • Integrating Documentation into Risk Management

 

Topic B: Response to Cybersecurity Incidents

  • Deployment of Incident Handling and Response Architecture
  • Containment and Mitigation of Incidents
  • Preparation for Forensic Investigation as a CSIRT

 

Topic C: Investigating Cybersecurity Incidents

  • Use a Forensic Investigation Plan
  • Securely Collect and Analyze Electronic Evidence
  • Follow Up on the Results of an Investigation

 

Topic D: Complying with Legislation

  • Examples of Legislation (if this is covered in above topics, no need to include here) GDPR, HIPPA, Elections
  • Case study: Incident Response and GDPR (Using GDPR legislation, create a response that is compliant with it – this could be discussion-based activity as well.)
  • State Legislation Resources and Example – Search terms to find state legislation
  • Using NYS as example use the NYS Privacy Response act or other legislation to create a similar case study as previous.
  • Provide answers on when to use federal versus state and do you have to follow both?

Topic A: Introduction to Data Ethics

  • Define Ethics
  • Define Data
  • Define Data Ethics
  • Principles of Data Ethics
  • The Case for Data Ethics
  • Identifying Ethical Issues

 

Topic B: Ethical Principles

  • Ethical Frameworks
  • Applying Ethical Frameworks
  • Privacy, Fairness, and Safety
  • Applying Privacy, Fairness, and Safety Principles
  • Algorithms and Human-Centered Values
  • Discussing True and False Positives and Negatives
  • Discussing Accuracy and Precision
  • Discussing Correlation and Causation
  • Transparency and Explainability: The Black Box Problem
  • Discussing Black Box Parallels
  • Inclusive Growth, Sustainable Development, and Well-Being
  • Examining a Tech for Good Organization
  • Improving Ethical Data Practices

 

Topic C: Sources of Ethical Risk

  • Bias and Discrimination
  • Case Study: Allegheny Family Screening Tool
  • Data Surveillance
  • Safety and Security
  • Case Study: PredPol

 

Topic D: Business Considerations

  • Data Legislation
  • Manage the Effects of Data
  • Case Study
  • Embed Organizational Values in the Data Value Chain
  • Building a Data Ethics Culture/Code of Ethics
  • Stakeholder Checklist

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