Certified Strategic Artificial Intelligence Leader Using Python x R


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  • Duration: 4 Day / 32 Hours
  • Certification: Participants will receive a Certificate of Competency upon successfully completing the course and passing the examination
  • Who Should Attend: Managers, Executives, Artificial Intelligence professionals, Data Analysts, Computer Scientists, Programmers, and Anyone interested in pursuing a career in the areas of Data Analytics, Data Science, and Artificial Intelligence (AI).

Course Objective

Acquire advanced knowledge and skills on how to apply AI-related technologies based on governance policies, and ethical, and regulatory requirements.

Learn how to use Python and R to develop and deploy AI-related technologies such as machine learning, deep learning, natural language processing, and computer vision.


No pre-requisite. Certified Strategic Artificial Intelligence is suitable for everyone.


Participants are required to attempt an examination upon completion of the course. This exam tests a candidate’s knowledge and skills related to Artificial Intelligence, Data Analytics, and Data Governance based on the syllabus covered

Participants are expected to score a minimum of 70% to pass the examination

Course Outline

Module 1
Introduction to Strategic Artificial Intelligence Leadership

Strategic Artificial Intelligence Leadership (SAIL) refers to the ability of organizations to effectively manage and leverage AI to achieve their strategic goals. This involves developing a clear understanding of AI's potential, identifying the right use cases, building the necessary infrastructure and talent, and ensuring ethical and responsible use of AI.

Topics Covered

  • Introduction to Strategic Artificial Intelligence
  • Understanding the Business Context of Artificial Intelligence Application
  • Assessing Organization Artificial Intelligence Readiness
  • Developing an Artificial Intelligence Roadmap
  • Creating Value and Application of Artificial Intelligence (AI)
  • Evaluating the Impact of Artificial Intelligence
  • Artificial Intelligence Governance Framework

Module 2
Artificial Intelligence Project Management

AI project management is crucial because AI projects are often complex, require a high level of expertise, and involve various stakeholders. Effective management of AI projects can help ensure their success and prevent costly mistakes.

Topics Covered

  • Introduction to Project Management and Project Planning
  • Resource allocation for Artificial Intelligence (AI) Project Management
  • Risk Management in Artificial Intelligence (AI) Project
  • Monitoring and Evaluating Outcome
  • Managing Artificial Intelligence (AI) Project Team
  • Communication and Stakeholder Management
  • Project Management Method (Agile, Lean, Design Thinking, Waterfall)

Module 3
Data Management and Data Analytics Using Python and R

Data management and data analytics are critical components of data science. Python and R are popular programming languages used for data management and data analytics. In this topic, learners will have the opportunity to learn how to use Python and R for Data Management and Data Analytics.

Topics Covered

  • Python Programming for Data Management and Analytics (Python Syntax, Data Structures, Control Structures, Functions, and Modules)
  • R Programming for Data Management and Analytics (R Syntax, Data Structures, Control Structures, Functions, and Modules)
  • Data Ingestion and Storage Techniques using Python and R
  • Data Pre-processing Techniques using Python and R
  • Exploratory Data Analysis using Python and R
  • Descriptive Statistics using Python and R
  • Time Series Analysis using Python and R
  • Data Analysis and Visualization using Python and R

Module 4
Artificial Intelligence Technology and Tools

Artificial Intelligence (AI) is a rapidly evolving field that encompasses a wide range of technologies and tools. Both Python and R are popular programming languages used in AI development. Both Python and R provide a wealth of tools and resources for AI development.

Topics Covered

  • Deep Dive into Python and R for Artificial Intelligence
  • Machine Learning Models Using Python and R Libraries (Linear Regression, Logistic Regression, Decision Trees, Neural Networks)
  • Natural Language Processing (NLP) Overview Using Python Libraries
  • Computer Vision (CV) Using Python Libraries
  • Overview of Deep Learning Architectures - Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Generative Adversarial Networks (GAN)

Module 5
Ethical Artificial Intelligence (AI)

Ethical Artificial Intelligence (AI) refers to the development and use of AI systems in a manner that aligns with ethical principles and values. It is crucial in ensuring that AI systems are developed and used in a manner that benefits society and does not cause harm.

Topics Covered

  • Introduction to Ethical Artificial Intelligence
  • Understanding the Regulatory Landscape
  • Risk Management and Auditability of Artificial Intelligence (AI) Projects
  • Engaging Stakeholders, Addressing Concerns and Expectations
  • Corporate Social Responsibility (CSR) in Artificial Intelligence (AI) Context

Module 6
Data Governance for Artificial Intelligence

Effective data governance for AI requires ensuring that the data used to develop AI systems is accurate, reliable, and representative, and that it is appropriately secured and protected.

Topics Covered

  • Introduction to Data Governance Principles and Practices for Artificial Intelligence
  • Artificial Intelligence (AI) Data Management (Data profiling, data lineage, and Data Catalogue, Metadata)
  • Application of Data Security and Privacy Regulations to Artificial Intelligence (AI) Data
  • Data Ethics and Bias in Artificial Intelligence
  • Implementation of Data Governance in Artificial Intelligence (AI) Project
  • Emerging Trends in Artificial Intelligence (AI) Governance

Module 7
Organizational Change Management in Artificial Intelligence (AI) Projects

Effective organizational change management for AI requires engaging stakeholders, communicating the benefits and risks of AI, and providing training and support to enable employees to use AI technologies effectively.

Topics Covered

  • Introduction to Change Management Principles and Its Application to AI Projects
  • Change Readiness Assessment
  • Change Management Planning
  • Communicating Changes to Stakeholders
  • Artificial Intelligence (AI) and Digital Literacy Training
  • Artificial Intelligence (AI) Ethics Training
  • Change Support Strategies

Certified Strategic Artificial Intelligence involves rigorous usage of real-time case studies, hands-on exercises and group discussion

Some Reasons Why Learners Choose CASUGOL

  • International Certification Body

  • Presence in 38 Countries

  • Developed by Industry Experts

  • More than 42,000 professionals passed through our education system

  • Flexible program design for all individuals

  • Learn from internationally renowned leading industry experts, academics, and researchers

  • Support for participants during and after training

  • Enhance competency of workforce and improve individual career prospect

  • Customization of programs for specific industry, organization, government agencies, statutory boards

  • Learn in a highly interactive, supportive and encouraging environment

  • Regular invitation to attend courses / workshops / seminars / events at complimentary rate

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Certificate Verification

All certificates issued by CASUGOL are to individuals who have successfully completed CASUGOL Certification Programs / Executive Workshops and have fulfilled all requirements by demonstrating proficiency in applying the knowledge and skills acquired.

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