Project Management, Monitoring and Evaluation Course

After attending this Project Management, Monitoring and Evaluation Course, participants will:

• Understand key aspects of project design, planning, implementation, administration and Monitoring and Evaluation
• Understand ways to design, implement, monitor, and evaluate projects.
• Educate Learners on how to link a logical framework and translate it into a concise and transparent budget and a simple work plan.
• Use the logical framework to design an effective monitoring and evaluation system for tracking and reporting results for their individual organizations.
• Understand best practices techniques for project management, Monitoring and Evaluation

Project Management Course Content:

• What is Project Management?
• Overview of Project Initiation, Planning, Implementation, Control and Close out.
• Scheduling and planning to ensure targets are accomplished timely.
• Project Budgeting
• Work plan Development
• The logical framework approaches.
• Designing a Project Goal

Outcomes/Output/Activities

• Indicators
• Evidence and Risk analysis
• Assumption/Pre-conditions/Risks

Monitoring and Evaluation Course Content:

• Introduction to monitoring and evaluation terms and concepts
• Difference between monitoring and evaluation
• The need for monitoring and evaluation in development projects
• Monitoring in all process of the project cycle
• Types of indicators and Identifying outcome indicators for your project
• Monitoring and Evaluation Framework
• Types of Frameworks
• Role of in Monitoring and Evaluation planning
• Introduction to Monitoring and Evaluation Plan
• Functions of an M and E plan
• The main elements of an M and E plan
• Standards of an M and E plan
• Some of the complexities of M and E plan
• Overview of M and E plan implementation modalities
• Data Collection, Capture, and Data Quality Checks
• Identify types of data collection tools
• Identify type of tools at each M and E level
• Define data capture.
• Identify key steps in data capture.
• Define data quality.