Chat with us

Generative AI for Research and Analytics Training Course

Online Training Download PDF
Upcoming Training Schedules 14 locations
Location Duration Next Start Date Dates Available Action
Nairobi, Kenya 10 days Jul 13, 2026 104 dates
Accra, Ghana 10 days Jul 13, 2026 31 dates
Addis Ababa, Ethiopia 10 days Aug 3, 2026 31 dates
Cape Town, South Africa 10 days Jul 20, 2026 52 dates
Dar es Salaam, Tanzania 10 days Jul 20, 2026 26 dates
Dubai, UAE 10 days Jul 13, 2026 52 dates
Istanbul, Turkey 10 days Jul 13, 2026 16 dates
Kampala, Uganda 10 days Jul 13, 2026 31 dates
Kigali, Rwanda 10 days Jul 27, 2026 52 dates
Kuala Lumpur, Malaysia 10 days Jul 13, 2026 31 dates
Mombasa, Kenya 10 days Jul 20, 2026 52 dates
Pretoria, South Africa 10 days Jul 27, 2026 52 dates
Singapore 10 days Jul 13, 2026 31 dates
Zanzibar, Tanzania 10 days Jul 20, 2026 16 dates

Generative AI for Research and Analytics Training Course

Course Overview

Generative Artificial Intelligence (Generative AI) is transforming research, analytics, knowledge management, and decision-making across governments, academia, humanitarian organizations, businesses, and development institutions. Advanced AI technologies such as large language models, intelligent assistants, machine learning algorithms, natural language processing, and automated content generation systems are revolutionizing how organizations collect, analyze, visualize, and communicate information. Generative AI enables researchers and analysts to accelerate data processing, automate repetitive tasks, generate insights from complex datasets, improve forecasting accuracy, and enhance evidence-based decision-making. As digital transformation continues to reshape industries, professionals increasingly require practical skills to harness Generative AI responsibly and effectively.

The Generative AI for Research and Analytics Training Course provides participants with comprehensive knowledge and practical competencies in applying Generative AI technologies throughout the research and analytics lifecycle. The course covers AI fundamentals, prompt engineering, AI-assisted literature reviews, automated data processing, qualitative and quantitative analytics, predictive modeling, data visualization, report generation, ethical AI implementation, and AI governance frameworks. Participants will learn how to utilize Generative AI tools to improve research efficiency, analytical accuracy, innovation, and organizational performance.

This highly practical course combines presentations, demonstrations, hands-on exercises, case studies, simulations, and collaborative projects to ensure participants gain real-world experience in deploying Generative AI tools for research and analytics applications. Participants will learn techniques for integrating AI solutions into research methodologies, business intelligence systems, monitoring and evaluation frameworks, strategic planning processes, and knowledge management systems while maintaining ethical standards and data integrity.

The course emphasizes responsible AI adoption, critical thinking, digital transformation, and innovation management. By developing competencies in Generative AI for Research and Analytics, participants will strengthen their ability to conduct high-quality research, improve analytical capabilities, automate workflows, generate actionable insights, enhance organizational decision-making, and prepare their institutions for the rapidly evolving future of artificial intelligence and data-driven transformation.

Course Objectives

Upon completion of this course, participants will be able to:

  1. Understand the concepts, principles, and applications of Generative AI in research and analytics.
  2. Apply prompt engineering techniques to improve AI outputs and research efficiency.
  3. Utilize AI tools for literature reviews, knowledge discovery, and information synthesis.
  4. Employ Generative AI for qualitative and quantitative data analysis.
  5. Develop AI-assisted predictive models and forecasting frameworks.
  6. Generate automated reports, summaries, and research outputs.
  7. Create effective data visualizations and interactive dashboards using AI tools.
  8. Integrate AI technologies into research, monitoring, and business intelligence systems.
  9. Address ethical, governance, and data privacy considerations in AI applications.
  10. Develop organizational strategies for sustainable AI adoption and digital transformation.

Organizational Benefits

Organizations participating in this training will benefit through:

  1. Increased efficiency in research and analytical processes.
  2. Faster generation of reports and evidence-based insights.
  3. Enhanced decision-making through AI-driven analytics.
  4. Improved data management and knowledge discovery capabilities.
  5. Reduced costs through automation of repetitive analytical tasks.
  6. Increased innovation and digital transformation readiness.
  7. Enhanced monitoring, evaluation, and reporting systems.
  8. Improved forecasting and predictive analytics capabilities.
  9. Strengthened research quality and organizational competitiveness.
  10. Enhanced capacity for responsible and sustainable AI adoption.

Target Participants

This course is suitable for:

  • Researchers and Research Managers
  • Monitoring and Evaluation Specialists
  • Data Analysts and Data Scientists
  • Business Intelligence Professionals
  • Policy Analysts and Development Practitioners
  • Academic Researchers and University Faculty
  • Government Officers and Statisticians
  • Project Managers and Program Managers
  • Information Technology Professionals
  • Knowledge Management Specialists
  • Consultants and Advisors
  • Professionals involved in research, analytics, innovation, and digital transformation initiatives

Course Outline

Module 1: Introduction to Generative AI and Analytics

  • Fundamentals of artificial intelligence and machine learning
  • Understanding Generative AI technologies and applications
  • Types of Generative AI models and capabilities
  • Evolution of AI in research and analytics
  • Opportunities and limitations of Generative AI
  • Emerging trends and future directions of AI

General Case Study: Exploring organizational opportunities for adopting Generative AI in research and analytical functions.

Module 2: Foundations of Prompt Engineering

  • Principles of effective prompt design
  • Structuring prompts for analytical tasks
  • Contextual prompting techniques
  • Iterative prompt refinement strategies
  • Developing research-oriented AI workflows
  • Improving accuracy and reliability of AI outputs

General Case Study: Designing prompts to automate analytical tasks and improve research productivity.

Module 3: AI-Assisted Literature Review and Knowledge Discovery

  • Conducting systematic literature reviews using AI
  • Information retrieval and evidence synthesis
  • Automated summarization techniques
  • Knowledge extraction from research databases
  • Identifying research gaps and trends
  • Managing references and citations using AI tools

General Case Study: Using Generative AI to accelerate literature reviews and identify emerging research areas.

Module 4: Generative AI for Data Collection and Preparation

  • AI-assisted survey design and questionnaire development
  • Automated data extraction and cleaning
  • Data preprocessing techniques
  • Handling structured and unstructured data
  • Data integration and transformation
  • Improving data quality and consistency

General Case Study: Applying AI tools to automate data preparation and improve research data management.

Module 5: Qualitative Data Analysis Using Generative AI

  • Text mining and natural language processing techniques
  • Automated coding and thematic analysis
  • Sentiment and discourse analysis
  • Interview and focus group analysis
  • Pattern identification in qualitative datasets
  • Integrating AI with qualitative research software

General Case Study: Using AI technologies to analyze interview transcripts and identify emerging themes.

Module 6: Quantitative Analytics and Statistical Applications

  • AI-assisted statistical analysis techniques
  • Descriptive and inferential analytics
  • Exploratory data analysis methods
  • Automated hypothesis testing
  • Statistical interpretation and reporting
  • AI integration with analytical software platforms

General Case Study: Utilizing Generative AI to improve quantitative analysis and interpretation.

Module 7: Predictive Analytics and Forecasting

  • Fundamentals of predictive analytics
  • Machine learning applications in forecasting
  • Predictive model development techniques
  • Trend analysis and scenario planning
  • Risk prediction and decision support systems
  • Evaluating predictive model performance

General Case Study: Developing predictive models for organizational planning and strategic decision-making.

Module 8: AI-Powered Data Visualization and Reporting

  • Principles of effective data visualization
  • AI-assisted chart and dashboard development
  • Storytelling with data and analytics
  • Automated report generation techniques
  • Interactive visualization tools and applications
  • Communicating insights to stakeholders

General Case Study: Designing AI-generated dashboards and reports that support executive decision-making.

Module 9: Generative AI for Monitoring, Evaluation, and Learning

  • AI applications in monitoring and evaluation systems
  • Automated indicator tracking and reporting
  • Learning analytics and performance measurement
  • Real-time monitoring and adaptive management
  • Impact evaluation using AI technologies
  • Evidence generation and knowledge management

General Case Study: Integrating AI into monitoring and evaluation systems to improve organizational learning.

Module 10: Ethics, Governance, and Responsible AI

  • Principles of responsible AI implementation
  • AI ethics and accountability frameworks
  • Data privacy and confidentiality considerations
  • Bias detection and mitigation strategies
  • Governance and regulatory frameworks
  • Risk management and ethical decision-making

General Case Study: Developing organizational policies for responsible and ethical use of Generative AI technologies.

Module 11: AI Strategy and Digital Transformation

  • Developing organizational AI strategies
  • Building AI readiness and digital capabilities
  • Change management for AI adoption
  • Workforce transformation and skills development
  • Integrating AI into business processes
  • Measuring AI maturity and organizational impact

General Case Study: Designing digital transformation strategies that leverage Generative AI for organizational growth.

Module 12: Future Trends and Emerging Applications of Generative AI

  • Large language models and advanced AI systems
  • Multi-modal AI applications in research and analytics
  • Intelligent agents and autonomous analytics
  • AI for innovation and knowledge management
  • Future workforce implications and digital leadership
  • Developing organizational AI roadmaps

General Case Study: Building integrated Generative AI frameworks that enhance research quality, analytical capabilities, innovation, organizational performance, and evidence-based decision-making.

General Information

  1. Customized Training: All our courses can be tailored to meet the specific needs of participants.
  2. Language Proficiency: Participants should have a good command of the English language.
  3. Comprehensive Learning: Our training includes well-structured presentations, practical exercises, web-based tutorials, and collaborative group work. Our facilitators are seasoned experts with over a decade of experience.
  4. Certification: Upon successful completion of training, participants will receive a certificate from Foscore Development Center (FDC-K).
  5. Training Locations: Training sessions are conducted at Foscore Development Center (FDC-K) centers. We also offer options for in-house and online training, customized to the client's schedule.
  6. Flexible Duration: Course durations are adaptable, and content can be adjusted to fit the required number of days.
  7. Onsite Training Inclusions: The course fee for onsite training covers facilitation, training materials, two coffee breaks, a buffet lunch, and a Certificate of Successful Completion. Participants are responsible for their travel expenses, airport transfers, visa applications, dinners, health/accident insurance, and personal expenses.
  8. Additional Services: Accommodation, pickup services, freight booking, and visa processing arrangements are available upon request at discounted rates.
  9. Equipment: Tablets and laptops can be provided to participants at an additional cost.
  10. Post-Training Support: We offer one year of free consultation and coaching after the course.
  11. Group Discounts: Register as a group of more than two and enjoy a discount ranging from 10% to 50%.
  12. Payment Terms: Payment should be made before the commencement of the training or as mutually agreed upon, to the Foscore Development Center account. This ensures better preparation for your training.
  13. Contact Us: For any inquiries, please reach out to us at training@fdc-k.org or call us at +254712260031.
  14. Website: Visit our website at www.fdc-k.org for more information.

 

 

Explore:

M&E Research & Data Analysis GIS & Remote Sensing ICT Skills Humanitarian Agriculture Business & Governance All Courses Download Training Calendar (PDF) Virtual / Online Training Courses Contact Us Subscribe Group Rates Gallery Trainer Consultancy
14 locations — Jump to booking

Enquire

Captcha code Click image to refresh

training@fdc-k.org • +254 712 260 031 • Nairobi, Kenya