• Understand the Fundamentals of Data Management and Governance
• Effectively Integrate Data Architecture with Modern Technologies
• Implement Data Management Strategies
• Master Key Data Management Skills
• Navigate Advanced Data Management Topics

• Data management: relation with DAMA-DMBOK and the role of data governance
• Introduction of knowledge areas
• Data architecture: business, functional and technical architecture and the relation with enterprise and application architecture
• Data architecture in relation with big data, data lakes, Data Warehouses (DWH), data virtualization and analytics/data science
• Positioning, coherence and added value of the business and IT
• Examples, use cases

• The new role of data: ‘data as an asset’
• Action plans for implementation based on context diagrams
• DMBOK as framework: backgrounds
• Introduction and in-depth discussion of knowledge areas metadata, reference & master data, Data Warehousing (DWH) & Business Intelligence (BI)

• Introduction and in-depth discussion of data modelling and design, data quality, data integration, storage, security

• Introduction and in-depth discussion of documents and content management, big data and data science, data privacy and ethics
• Introduction and application of the DAMA Data Management Maturity Model
• CDMP exam preparation

Cost: 4760 USD

Duration: 4 Days

• Understand key AI concepts, terminologies, and technologies.
• Identify opportunities for AI implementation in different business processes and functions.
• Develop strategies for integrating AI into organisational workflows and decision-making processes.
• Evaluate the ethical and societal implications of AI adoption and deployment.
• Lead AI-driven initiatives effectively, fostering innovation and collaboration across teams.
• Cultivate a culture of data-driven decision-making and continuous learning within the organisation.

• Introduction to AI: Concepts and Terminologies
• AI Applications Across Industries
• Case Studies: Successful AI Implementations

• Developing an AI Strategy for Business
• Assessing Organisational Readiness for AI Adoption
• Managing Change: Leadership in AI-driven Transformation

• Ethical Implications of AI: Bias, Privacy, and Accountability
• Implementing Ethical AI Principle
• Building Trust in AI: Transparency and Fairness

• Cultivating a Data-driven Culture
• Innovation and Collaboration: AI-driven Opportunities
• Building the Future with AI: Closing Remarks and Next Steps.

Cost: 5995 USD

Duration: 4 Days

• Gain proficiency in fundamental machine learning concepts, algorithms, and methodologies.
• Apply machine learning techniques to analyse and interpret data for business insights.
• Develop and evaluate machine learning models using popular tools and frameworks.
• Implement machine learning solutions to optimise business processes and enhance decision-making.
• Assess the performance and effectiveness of machine learning models in different business contexts.
• Incorporate machine learning into business strategy, driving innovation and competitive advantage.

• Overview of Machine Learning Concepts and Algorithms
• Hands-on: Exploratory Data Analysis with Python
• Building Your First Machine Learning Model

• Feature Engineering and Model Selection
• Model Evaluation and Validation
• Introduction to Supervised and Unsupervised Learning

• Predictive Modelling for Business: Case Studies
• Implementing Machine Learning Pipelines
• Hands-on: Building and Deploying Machine Learning Models

• Automating Decision-making with Machine Learning
• Scalability and Performance Considerations
• Measuring ROI: Evaluating the Impact of Machine Learning Projects.

Cost: 5995 USD

Duration: 4 Days

• Understand the architecture and principles of deep neural networks.
• Implement deep learning models using libraries such as TensorFlow and PyTorch.
• Apply deep learning techniques to solve business challenges in areas such as image classification and sentiment analysis.
• Fine-tune pre-trained models for specific business domains and use cases.
• Explore advanced topics in deep learning, including generative adversarial networks (GANs) and reinforcement learning.
• Develop strategies for deploying and scaling deep learning solutions in production environments.

• Fundamentals of Neural Networks
• Introduction to TensorFlow and Keras
• Hands-on: Building Your First Neural Network

• Understanding CNN Architecture
• Image Classification with CNNs
• Case Studies: CNN Applications in Business

• Introduction to RNNs and LSTM Networks
• Natural Language Processing (NLP) with RNNs
• Hands-on: Sentiment Analysis with RNNs

• Introduction to GANs and Reinforcement Learning
• Case Studies: Advanced Deep Learning Applications
• Deploying Deep Learning Models in Production Environments.

Cost: 5995 USD

Duration: 4 Days

• Understand the ethical implications of AI technologies on society and individuals.
• Identify and mitigate biases in AI algorithms and datasets to ensure fairness and equity.
• Implement ethical AI principles and guidelines in AI development and deployment processes.
• Develop policies and frameworks for AI governance and compliance with regulatory requirements.
• Foster transparency and accountability in AI-driven decision-making processes.
• Engage stakeholders and promote responsible AI practices across the organisation.

• Ethical Principles in AI Development and Deployment
• Bias and Fairness in AI Systems
• Case Studies: Ethical Dilemmas in AI

• Regulatory Landscape: GDPR, AI Ethics Guidelines
• Developing AI Governance Policies and Procedures
• Building Trustworthy AI Systems

• Ethical Considerations in AI Research and Development
• Implementing Ethical AI Practices in Organisations
• Tools and Techniques for Ethical AI Development

• Ethics Training and Awareness Programs
• Stakeholder Engagement and Collaboration
• Closing Remarks: Towards Ethical AI Transformation

Cost: 5995 USD

Duration: 4 Days

• Overview of AI Techniques for Marketing
• Customer Behaviour Analysis with AI
• Case Studies: AI-powered Marketing Campaigns

• Building Customer Profiles with AI
• Personalised Marketing Strategies
• Recommendation Systems and Customer Engagement

• A/B Testing and Optimisation Techniques
• Predictive Analytics for Customer Segmentation
• Hands-on: Implementing AI-driven Marketing Strategies

• Performance Metrics and KPIs
• AI-powered Analytics Tools and Platforms
• Closing Remarks: Leveraging AI for Marketing Success.

Cost: 5995 USD

Duration: 4 Days

• Understand the strategic importance of AI for business competitiveness and growth.
• Develop an AI strategy aligned with organisational goals, priorities, and capabilities.
• Assess organisational readiness for AI adoption, including data infrastructure, talent, and culture.
• Plan and execute AI projects using best practices and methodologies, managing risks and uncertainties effectively.
• Monitor and measure the impact of AI initiatives on business performance, iterating and refining strategies as needed.
• Foster a culture of innovation and continuous learning, driving organisational transformation and agility.

• AI Landscape: Trends and Opportunities
• Developing an AI Strategy Aligned with Business Goals
• Case Studies: Successful AI Transformation Stories

• Evaluating Data Infrastructure and Talent
• Identifying AI Use Cases and Priorities
• Overcoming Barriers to AI Adoption

• Developing AI Roadmaps and Implementation Plans
• Project Management Best Practices for AI Initiatives
• Building Cross-functional Teams for AI Projects

• Measuring AI Impact: KPIs and Metrics
• Continuous Improvement and Iteration
• Closing Remarks: Building a Culture of AI Innovation.

Cost: 5995 USD

Duration: 4 Days

• Identify key AI applications in various industries.
• Understand the core principles and limitations of machine learning.
• Define rational agents and their decision-making processes.
• Grasp the basics and applications of fuzzy logic.
• Gain an Overview of robotics and AI integration.
• Comprehend key concepts and challenges in NLP.
• Understand the fundamentals and applications of neural networks.

• AI in healthcare, finance, transportation, entertainment
• Real-world use cases and societal impact
• Strengths and Limitations of Machine Learning
• Core principles of machine learning
• Limitations: data dependency, interpretability

• Rational agents in AI
• Defining rationality in AI
• Implications for decision-making
• Fuzzy Logic
• Approximate reasoning with fuzzy logic
• Applications in uncertainty handling

• Introduction to robotics
• AI integration in robots
• Robot perception, motion planning, navigation

• Enabling machines to understand human language
• Key concepts: text analysis, language generation
• Challenges in conversational agents

• Neural networks as AIs backbone
• Brain-inspired models
• Uses: image recognition, speech processing, gaming.

Cost: 5995 USD

Duration: 5 Days

• Understand AI's capabilities and limitations within a business context.
• Develop strategies to leverage AI for competitive advantage.
• Identify the necessary skills and capabilities needed to support AI initiatives.
• Foster an AI-ready culture within organisations to facilitate seamless adoption.
• Measure and optimise the impact of AI through appropriate metrics and KPIs.
• Navigate ethical considerations and sustain responsible AI practices in business.

• Introduction to AI: Overview of AI technologies and their fundamental concepts
• AI in Operations: How AI can optimise operational efficiency, from manufacturing to services
• Interactive Session: Identifying AI opportunities in participants operations

• AI and Business Strategy: Integrating AI into business strategy for competitive advantage
• Case Studies: Real-world examples of strategic AI integration
• Workshop: Developing AI-aligned strategies for hypothetical businesses

• Skills Assessment: Identifying required AI skills and capabilities within a business
• Training Programs: Designing and implementing training for AI proficiency across roles
• Group Activity: Mapping out a skills development plan for an AI-driven business

• Cultural Change: Strategies for fostering an AI-ready culture in the workplace
• Leadership in AI: Role of top management in driving AI initiatives
• Discussion: Barriers to AI adoption and how to overcome them

• AI and ROI: Metrics and KPIs to measure AI impact
• Sustainability and Ethics: Ensuring responsible AI use in business practices
• Panel Discussion: Future of AI in business with industry experts

Cost: 5995 USD

Duration: 5 Days

• Understanding AI Technologies and Frameworks: Gain a comprehensive understanding of AI, Machine Learning (ML), and Deep Learning (DL) concepts, along with proficiency in using popular AI frameworks like TensorFlow, PyTorch, and Scikit-Learn.
• Application of Advanced AI Techniques: Acquire practical knowledge of advanced ML algorithms such as Neural Networks, Transfer Learning, and Natural Language Processing (NLP) to solve real-world problems across various industries.
• Deployment and Optimization Strategies: Learn techniques for deploying AI models in production environments and optimizing these models through strategies like hyperparameter tuning and model pruning for enhanced performance.
• Utilization of AI Services from Vendors: Explore and apply pre-built AI services from leading vendors (AWS, Azure, GCP) for practical implementations in domains like Computer Vision, Speech Recognition, and Recommendation Systems.
• Ethical Considerations in AI: Understand the ethical considerations associated with AI development and deployment, including addressing bias in algorithms, ensuring fairness, and leveraging open-source AI tools/platforms.
• Application of AI in Industry Contexts: Apply AI technologies specifically within industrial contexts, identifying challenges and opportunities across sectors like manufacturing, supply chain, logistics, and predictive maintenance.
• Future Trends and Continuous Learning: Explore emerging trends in AI, such as Explainable AI, AutoML, and AI in Edge Computing, while fostering an understanding of the impact of AI on industries and society. Additionally, access resources for continuous learning and staying updated in the evolving field of AI.

• Overview of AI, Machine Learning (ML), and Deep Learning (DL)
• Types of AI: Narrow vs. General AI
• Practical applications and industries benefiting from AI
• Overview of popular AI frameworks: TensorFlow, PyTorch, Scikit-Learn
• Hands-on: Setting up the environment and running basic ML/DL models using TensorFlow or PyTorch
• Case Study: Image Classification using TensorFlow or PyTorch

• Deep Dive into Neural Networks: CNNs, RNNs, and their applications
• Transfer Learning and its significance in various industries
• AI vendors and their specialised offerings (, IBM Watson, Google Cloud AI)
• Fundamentals of NLP and its applications (Sentiment Analysis, Named Entity Recognition)
• Hands-on: Text Generation or Sentiment Analysis using NLP tools (NLTK, spaCy)
• Case Study: Implementing NLP for Customer Sentiment Analysis using AWS Comprehend or Google Cloud Natural Language

• Techniques for deploying models in production environments
• Model optimisation approaches (Hyperparameter tuning, Model pruning)
• AI service providers and their deployment platforms (AWS, Azure, GCP)
• Hands-on: Utilizing pre-built AI services from vendors like AWS Rekognition, Azure Cognitive Services, or Google Cloud Vision API
• Case Study: Implementing a Recommendation System using AI services from AWS or Azure

• Ethical considerations in AI development and deployment
• Addressing bias in AI algorithms and ensuring fairness
• Open-source AI tools and platforms (TensorFlow Extended, Hugging Face Transformers)
• Emerging trends in AI (Explainable AI, AutoML, AI in Edge Computing)Emerging trends in AI (Explainable AI, AutoML, AI in Edge Computing)
• Discussion on the impact of AI on various industries and society
• Course Conclusion, Q&A, and Resources for Continuous Learning.

Cost: 6400 USD

Duration: 4 Days

• Understand the key AI technologies and their applications in the finance industry.
• Learn about the impact of AI on various aspects of finance including trading, risk management, fraud detection, and customer service.
• Gain practical skills in using AI tools and techniques relevant to finance.
• Analyse real-world case studies of AI implementations in finance.
• Discuss the ethical and regulatory considerations of using AI in finance.

• Overview of AI Technologies (Machine Learning, NLP, Robotics, etc.)
• Overview of the Finance Sector and its Key Challenges

• Algorithmic Trading and AI
• AI for Portfolio Management and Investment Strategies
• Hands-on Workshop using AI Trading Simulations

• AI for Credit Scoring and Risk Assessment
• Predictive Analytics for Risk Management

• AI for Fraud Detection and Prevention
• Regulatory Compliance and AI

• Chatbots and Virtual Assistants in Finance
• Robo-Robo-advisors Personalized Financial Planning.

Cost: 5995 USD

Duration: 5 Days

• Understand the vocabulary, principles, framework and risk management process in accordance with ISO 31000
• Understand the benefits of ISO 31000 as an internationally recognized standard, adopted by over 40 countries
• Interpret these ISO 31000 guidelines and integrate them into your already existing management systems
• Work in a structured way with the implementation and optimization of dealing with opportunities and threats within your organization
• Acquire the competence to communicate effectively about ISO 31000 with people having different backgrounds and experience in the field of risk management

• Introduction to risk management
• The benefits of risk management to every organization
• The ISO 31000 risk management principles and how they influence the risk management framework and process
• The ISO 31000 risk management framework
• Exercises, case studies and exam questions on the elements learned during day 1

• Overview of the risk management process
• Risk identification, risk analysis and risk evaluation
• Risk treatment
• Communication and consultation
• Monitoring and review
• Exercises, case studies and exam questions on the elements learned during day

• Review of the highlights of the ISO 31000 standard
• Hints and tips for the examination
• Additional exercises, case studies and exam questions
• Examination (ISO 31000 Foundations Level - CRMF Certificate).

Cost: 2815 USD

Duration: 3 Days

• Master industrial cybersecurity management
• Implement security policies per national and international laws
• Protect networks using IEC 62443 guidelines
• Apply various cybersecurity standards
• Comply with the new European NIS2 Directive

• Trends in ICT and Industrial Control Systems (ICS)
• Relevant standards and frameworks (IEC 62443, NIST SP 800-82)
• Industrial Control Systems (ICS): Overview
• Examples and topologies
• Embedding risk management in your organisation
• Special points of attention regarding Industrial Control Systems (ICS)

• How to develop a business case
• Required expertise and skills
• Defining a scope
• Policy, standards and procedures
• Awareness

• Logical network segmentation
• Firewalls
• TCIP/IP protocol suite
• Defense-in-Depth architecture
• RESTful APIs and JSON (JavaScript Object Notation)
• Specific SCADA and ICS protocols
• Remote Support Access (RSA), Incident response

• Integration of risk management and Industrial Control Systems (ICS)
• Awareness
• Audits
• Configuration management
• Business Continuity Management (BCM)
• Protection of storage media
• Physical security, Privacy controls.

Cost: 4700 USD

Duration: 4 Days

• Explain key cybersecurity concepts and components
• Identify components of cybersecurity network architecture
• Describe risk management processes
• Identify security tools, threats, and vulnerabilities
• Describe different classes of attacks
• Explain emerging technologies
• Analyze threats and risks
• Assess cybersecurity incidents

• Cyber security objectives
• Cyber security roles
• Differences between information security and cyber security
• Confidentiality, integrity, and availability
• Authentication and non-repudiation

• Lifecycle management landscape
• Security architecture processes
• Security architecture tools
• Intermediate lifecycle management concepts
• Basics of risk management
• Operational threat environments
• Classes of attacks

• Incident categories
• Incident response
• Incident recovery
• New and emerging IT and IS technologies
• Mobile security issues, risks, and vulnerabilities
• Cloud concepts around data and collaboration

• During the exam preparation day you will learn the critical skills to perform many of the technical tasks vital to your success in the field of IT.
• In the morning you will practice hands-on with performance-based questions.
• In the afternoon you will practice with knowledge-based questions.

Cost: 4730 USD

Duration: 4 Days

• Determine if a procurement scheme violates legal statutes or civil laws
• Identify the red flags of bribery and corruption
• Recognize bid-rigging and market-allocation schemes
• Reduce the risk of vendor fraud
• Develop an investigation plan to respond to suspected procurement fraud

• Public versus private procurement
• Methods used to acquire goods and services
• Four phases in the procurement (tendering) process
• E-procurement
• Employees involved in the purchasing process
• Civil actions
• Administrative actions
• Penalties and legal remedies
• Principal criminal violations in procurement
• Principal civil violations in procurement
• Bribery
• Kickbacks
• Bid rigging schemes
• Market division
• Preventing bid rigging, market allocation, and other types of collusion
• Need recognition
• Bid tailoring

• Unjustified sale-source awards
• Purchases for personal use
• Bid splitting
• P-card schemes
• Petty cash abuse

• Invoicing schemes
• Nonconforming goods or services
• Change order abuse
• Cost mischarging
• Commingling of contracts

• Vendor due diligence
• Managing vendor risks via contracts
• Risk of shell companies

• Instituting an ethics and compliance program
• The principles of fraud risk management
• Establishing a procurement fraud detection and prevention program
• Step one: conduct a fraud risk assessment
• Step two: develop a tailored anti-fraud program
• Step three: establish a response plan

• Develop an investigation plan
• Investigating procurement fraud schemes

Cost: 2140 USD

Duration: 2 Days

• Understand the role of contracts and contract professionals
• Apply practical concepts and techniques in contract management
• Master the contracting lifecycle and legal considerations
• Identify and manage contract risks and opportunities
• Conduct effective contract negotiations and pre-signature activities
• Ensure successful contract delivery and maintain supplier/customer relationships

• Introduction
• The contract lifecycle
• Core principles

• The first step of the lifecycle
• Understanding the requirements

• The RFx documents and the buyer perspective
• Bid and proposal management – the seller perspective

• Selecting a contract type
• Preliminary agreements and sale of goods

• Selling goods and services; licenses and leases
• Other business relationships and complex agreements

• Strategy and style

• Transitioning to a new contract
• Managing changes and disputes

• Managing performance
• Delivering, invoicing, and benchmarking

Cost: 3780 USD

Duration: 3 Days

Overview

Artificial intelligence (AI) and machine learning (ML) have become essential parts of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, all while following a methodical workflow for developing data-driven solutions.

Objective

• Solve business problems with AI and ML
• Prepare and process data for ML
• Train, evaluate, and tune ML models
• Build and deploy various ML models (regression, classification, clustering, neural networks)
• Operate and maintain ML models in production

Who Should Attend

This course is suitable for anyone looking to expand their knowledge of machine learning algorithms and how they can help create intelligent decision-making products that bring value to the business

Outlines

Day 1: Solving Business Problems Using AI and ML including Preparing Data, Training, Evaluating, and Tuning a Machine Learning Model

• Identify AI and ML Solutions for Business Problems
• Formulate a Machine Learning Problem
• Select Approaches to Machine Learning
• Collect DataTransform DataEngineer Features
• Work with Unstructured Data
• Train a Machine Learning Model
• Evaluate and Tune a Machine Learning Model

Day 2: Building Linear Regression Models including, Building Forecasting Models

• Build Regression Models Using Linear Algebra
• Build Regularized Linear Regression Models
• Build Iterative Linear Regression Models
• Build Univariate Time Series Models
• Build Multivariate Time Series Models

Day 3: Building Classification Models Using Logistic Regression and k-Nearest Neighbor, including Building Clustering Models

• Train Binary Classification Models Using Logistic Regression
• Train Binary Classification Models Using k-Nearest Neighbor
• Train Multi-Class Classification Models
• Evaluate Classification ModelsTune Classification Models
• Build k-Means Clustering Models
• Build Hierarchical Clustering Models

Day 4: Building Decision Trees and Random Forests including Building Support-Vector Machines

• Build Decision Tree ModelsBuild Random Forest Models
• Build SVM Models for ClassificationBuild SVM Models for Regression

Day 5: Building Artificial Neural Networks including Operationalizing Machine Learning Models, Maintaining Machine Learning Operations

• Build Multi-Layer Perceptrons (MLP)
• Build Convolutional Neural Networks (CNN)
• Build Recurrent Neural Networks (RNN)
• Deploy Machine Learning Models
• Automate the Machine Learning Process with MLOps
• Integrate Models into Machine Learning Systems
• Secure Machine Learning Pipelines
• Maintain Models in Production

Cost: 5995 USD

Duration: 5 Days

• Develop expertise in complex financial transactions and fraud resolution methods
• Gain a deep understanding of how and why fraud occurs
• Master skills in fraud prevention, detection, deterrence, and investigation
• Achieve the Certified Fraud Examiner (CFE) credential to enhance career growth
• Position yourself as a leader in the global anti-fraud community
• Increase earning potential and recognition by leading organizations worldwide

• Planning and conducting a fraud examination
• Collecting evidence
• Interview theory and application
• Interviewing suspects and signed statements
• Covert operations
• Sources of information
• Data analysis and reporting tools
• Digital forensics
• Tracing illicit transactions
• Report writing

• Overview of the legal system
• The law related to fraud
• Bankruptcy fraud
• Securities fraud
• Money laundering
• Tax fraud
• Individual rights during examinations
• The criminal justice system
• The civil justice system
• Basic principles of evidence
• Testifying

• Accounting concepts
• Financial statement fraud
• Asset misappropriation: cash receipts
• Asset misappropriation: fraudulent disbursements
• Asset misappropriation: inventory and other assets
• Corruption
• Theft of data and intellectual property

• Identity theft
• Financial institution fraud
• Payment fraud
• Insurance fraud
• Health care fraud
• Consumer fraud
• Cyber fraud
• Contract and procurement fraud

• Understanding criminal behavior
• White-collar crime
• Corporate governance
• Management’s fraud-related responsibilities
• Auditors’ fraud-related responsibilities
• Fraud prevention programs
• Fraud risk assessment
• Fraud risk management
• Ethics for fraud examiners

Cost: 4630 USD

Duration: 5 Days