In an age of big data, projects generate vast streams of information that are impossible for humans to monitor manually. Subtle yet critical issues, from fraudulent transactions to malfunctioning sensors, can go unnoticed, leading to significant financial losses or project failures. This training course is designed to empower M&E professionals to master deep learning for anomaly detection, a cutting-edge field of artificial intelligence that automatically identifies unusual patterns in data. By moving beyond simple thresholds and rules, participants will learn how to build intelligent systems that can detect deviations in real-time, providing an early warning signal for potential problems before they escalate.

This comprehensive program provides a practical, hands-on roadmap for designing and implementing deep learning solutions for monitoring. Participants will gain skills in everything from preparing large datasets and training neural networks to interpreting model results and integrating these insights into a dashboard. The course is a strategic investment for organizations seeking to enhance the efficiency and foresight of their M&E systems, ensuring their work is a force for positive change in a world that needs it most.

Duration: 10 days

Target Audience:

• M&E Specialists
• Data Scientists
• Program and Project Managers
• Researchers
• Information Technology Officers
• Financial Analysts
• Strategic Planners
• Government Officials
• Civil Society Leaders
• Consultants

Objectives:

• Understand the core principles of deep learning for anomaly detection.
• Master the process of designing a deep learning M&E framework.
• Learn how to select and measure key indicators of anomalous behavior.
• Develop a data collection and analysis plan for deep learning.
• Integrate ethical considerations and safety protocols.
• Build a more participatory and inclusive approach to monitoring.
• Communicate findings to different audiences while managing risk.
• Ensure ethical considerations and safety protocols in data collection.
• Foster a culture of continuous learning and adaptive management.
• Apply M&E techniques to a wide range of sectors.

Course Modules:

Module 1: Introduction to Deep Learning for M&E

• The limitations of traditional M&E
• Defining deep learning and its purpose
• The business case for a deep learning approach
• An overview of the deep learning workflow
• The difference between an output and a deep learning outcome

Module 2: Foundational Concepts for Deep Learning

• The importance of a clear and focused research question
• Understanding the context and its impact on the project
• The role of a program's theory of change
• The importance of a clear and testable hypothesis
• An overview of the data-to-dashboard workflow

Module 3: Designing a Deep Learning Framework

• The importance of a clear and focused framework
• The role of a "digital" framework
• Integrating a gender and social inclusion analysis
• The importance of a "risk and mitigation" plan
• Case studies on effective framework design

Module 4: Key Performance Indicators (KPIs)

• The importance of a clear and compelling KPI
• The difference between an output, an outcome, and an impact
• The use of a simple scorecard and a dashboard
• Practical labs on a basic performance measurement tool
• The importance of a clear and consistent reporting style

Module 5: Data Collection and Security

• The importance of a clear and secure data collection protocol
• The use of a simple survey and an interview
• The role of a data management system (e.g., Salesforce, SAP)
• The importance of a clear and consistent reporting style
• Protocols for handling sensitive and confidential data

Module 6: Data Analysis and Interpretation

• The importance of a clear data analysis plan
• Using simple statistical analysis for quantitative data
• The role of qualitative data analysis methods
• Interpreting findings from a deep learning perspective
• The importance of data triangulation

Module 7: The Art of Storytelling with Data

• The difference between a simple visual and a data story
• The importance of a clear and compelling narrative
• Using dashboards and visualizations to communicate insights
• The role of a "data story map"
• Practical labs on building a data story

Module 8: Ethical Considerations and Safety

• The importance of a "do no harm" approach
• Ensuring the safety and privacy of participants
• The role of informed consent in a crisis
• The importance of a community-led ethical review process
• Protocols for handling sensitive and potentially harmful data

Module 9: M&E for a Diverse Audience

• The importance of knowing your audience
• The role of a "stakeholder analysis"
• Designing reports and dashboards for non-technical audiences
• The importance of accessibility and inclusivity
• Case studies on communicating with different audiences

Module 10: Mainstreaming M&E

• How to integrate M&E into the project cycle
• The importance of a phased implementation strategy
• The role of M&E in the project cycle
• Building a culture of adaptive management
• Case studies on successful integration

Module 11: M&E for a Diverse Sector

• M&E for a technology project
• M&E for a social enterprise
• M&E for a humanitarian project
• M&E for a climate action project
• M&E for a governance project

Module 12: The Role of the M&E Professional

• Shifting from a technician to a facilitator
• The skills required for a deep learning M&E professional
• Managing power dynamics and group conflicts
• The importance of a non-judgmental and empathetic approach
• The ethical responsibilities of the M&E professional

Module 13: CBM and Deep Learning

• The potential of community-based monitoring with a focus on deep learning
• Training community members as monitors
• The importance of a participatory approach
• The role of a feedback mechanism for continuous learning
• The long-term benefits of a community-led system

Module 14: Practical Application and Simulation

• A hands-on simulation of a real-world project
• Participants work in teams to design a deep learning framework
• Troubleshooting common challenges in data collection
• Analyzing and interpreting a set of data
• Peer review and feedback sessions on framework design

Module 15: The Future of M&E

• The role of AI and machine learning in automated analysis
• The potential of blockchain for data integrity
• The use of new data sources (e.g., satellite imagery)
• The rise of complexity-aware M&E
• The long-term implications for the sector

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 10 working days before commencement of the training.