With rapid technological advancement, Big Data and Artificial Intelligence (AI) have become key drivers of digital transformation across industries. The exponential growth of data makes skills in big data analysis a strategic necessity for effective decision-making.
AI contributes to developing tools and techniques that enable organizations to predict, understand, and respond efficiently, enhancing overall performance. This training program, Principles and Practices of Big Data and Artificial Intelligence (AI), combines technical concepts with practical strategies used by leading organizations.
Participants will gain in-depth knowledge of leveraging big data and AI applications to improve organizational performance, analyze value chains, apply machine learning, and design roadmaps that strengthen AI strategies in dynamic digital environments.
Participants will be able to:
- Understand big data and AI concepts and applications
- Design data models and implement machine learning solutions
- Apply big data techniques to improve operational performance
- Develop AI strategies for risk management and competitive advantage
- Integrate AI into organizational decision-making and digital transformation
- Measure and optimize AI project outcomes
Unit 1: Big Data Fundamentals and Analytics
- Definition and significance of big data in the digital era
- Applications in modern management
- Strategies for collecting structured and unstructured data
- Analysis using modern tools such as Hadoop and Spark
- Challenges of data volume and variety
- Leveraging big data to improve efficiency and decision-making
- Integrating big data with predictive analytics and continuous improvement
Unit 2: Artificial Intelligence and Machine Learning
- Definition, history, and types of AI
- Difference between AI, machine learning, and deep learning
- AI applications in business and management
- Techniques for data analysis using AI
- Role of AI in improving productivity and decision support
- Algorithms: classification, regression, supervised, unsupervised, and semi-supervised learning
- Future trends of AI in digital transformation
Unit 3: Building AI Projects
- Steps to design a successful AI project
- Project team formation and role assignment
- Problem definition and data exploration
- Prototype design and performance evaluation
- Implementation and monitoring in work environments
- Tools and platforms: Python, R, Jupyter, Azure, Watson
- Designing roadmaps and KPIs for AI projects
Unit 4: Big Data and AI in Management
- Big data use cases in organizations and strategy alignment
- AI applications in supply chain, marketing, services, and support functions
- Cloud computing integration with big data
- Data governance and security
- Decision support systems and enhancing customer experience
- Measuring AI impact on achieving business goals
Unit 5: Sustainability and Strategic Planning
- Integrating AI into strategic vision and short- and long-term data plans
- Developing future-ready data leaders and governance frameworks
- Predictive analytics for continuous improvement and decision-making
- Risk assessment in AI projects and enhancing competitive advantage
- Technical and leadership skills for successful AI project management
- Digital transformation managers and data leaders
- IT and software professionals
- Business and data analysts
- AI researchers and project developers
- Finance, HR, and strategic planning professionals