Data Science and Predictive Analytics for Evaluation Professionals Training Course

Introduction

Evaluation professionals must harness the power of data science and predictive analytics. This course equips participants with the knowledge and tools to leverage advanced analytical techniques to improve decision-making, enhance program performance, and forecast future outcomes in monitoring and evaluation (M&E) systems.

The integration of data science into evaluation allows practitioners to extract deeper insights from large datasets, uncover hidden patterns, and identify factors that drive impact. Participants will explore how predictive analytics, machine learning, and data visualization can strengthen evaluation frameworks and inform strategic planning for development interventions.

Through this training, participants will gain practical experience in using data science tools such as Python, R, and Power BI to analyze structured and unstructured datasets. The course emphasizes the use of predictive models to assess program risks, anticipate results, and generate actionable insights that support adaptive management and evidence-based decision-making.

The course also explores how big data, satellite imagery, and artificial intelligence (AI) are transforming evaluation methodologies. Participants will learn how to integrate these technologies into M&E systems while ensuring ethical data use, privacy, and data quality standards are maintained.

A strong focus is placed on real-world applications and participants will engage in case studies where predictive analytics has been successfully used to optimize development outcomes in areas such as health, education, climate resilience, and economic inclusion.

By the end of the course, learners will have developed advanced analytical skills, enabling them to design, implement, and interpret data science-based evaluation systems that anticipate change, enhance accountability, and improve the effectiveness of development programs.

Who Should Attend

• Monitoring and Evaluation (M&E) professionals and specialists
• Data scientists and data analysts in development organizations
• Evaluation consultants and policy analysts
• Program and project managers using evidence-based decision-making
• ICT and innovation officers supporting M&E systems
• Development researchers and statisticians
• NGO and donor agency personnel managing results-based programs
• Public sector M&E and planning officers
• Academics and postgraduate students in data analytics and evaluation
• Impact measurement and research coordinators
• Social scientists integrating data science in evaluation
• Professionals transitioning into data-driven M&E roles

Duration

10 Days

Course Objectives

By the end of this course, participants will be able to:

• Understand the fundamentals of data science and predictive analytics in evaluation.
• Apply machine learning techniques to analyze and forecast development outcomes.
• Integrate big data and AI tools into monitoring and evaluation systems.
• Design predictive models for program performance and risk assessment.
• Use Python, R, and Power BI for data visualization and predictive modeling.
• Interpret data outputs to support evidence-based decision-making.
• Manage and clean large datasets for M&E analysis.
• Ensure ethical data collection, storage, and analysis practices.
• Combine qualitative and quantitative data for comprehensive evaluations.
• Communicate analytical findings through interactive dashboards and reports.
• Strengthen institutional capacity for data-driven evaluation systems.
• Develop adaptive learning frameworks based on predictive insights.

Comprehensive Course Outline

Module 1: Introduction to Data Science and Predictive Analytics

• Understanding the role of data science in evaluation
• Key concepts and terminologies in predictive analytics
• The evolution of data-driven M&E systems
• Applications of data science in development programs

Module 2: Fundamentals of Predictive Analytics for M&E

• Principles of predictive modeling and forecasting
• Data types and analytical frameworks
• Understanding algorithms for predictive evaluation
• Integrating prediction into performance monitoring

Module 3: Data Management and Preparation

• Data cleaning, transformation, and validation techniques
• Handling missing data and outliers
• Structuring large datasets for M&E analysis
• Ensuring data quality and consistency

Module 4: Tools for Data Science in Evaluation

• Overview of Python, R, and Power BI for analysis
• Setting up environments for predictive analytics
• Data visualization and reporting tools
• Practical exercises using sample evaluation datasets

Module 5: Statistical and Machine Learning Techniques

• Regression, classification, and clustering methods
• Predictive modeling for outcomes and impact assessment
• Using supervised and unsupervised learning in evaluations
• Model validation and accuracy testing

Module 6: Big Data and Evaluation Analytics

• Understanding big data in the development context
• Data sources: mobile data, sensors, and satellite imagery
• Integrating big data into M&E systems
• Challenges and opportunities in big data evaluation

Module 7: Designing Predictive M&E Frameworks

• Linking predictive analytics to theory of change
• Developing indicators aligned with predictive models
• Real-time data monitoring for adaptive management
• Case studies of predictive frameworks in development

Module 8: Ethical and Responsible Data Use

• Ethical principles in predictive analytics
• Data privacy, protection, and governance
• Bias and fairness in predictive modeling
• Responsible AI and inclusive evaluation practices

Module 9: Visualization and Storytelling with Data

• Visualizing trends, forecasts, and impact metrics
• Using dashboards to communicate insights
• Interactive reporting for decision-makers
• Data storytelling techniques for policy influence

Module 10: Predictive Models for Development Sectors

• Predicting program outcomes in health and education
• Forecasting agricultural productivity and food security
• Predictive modeling for climate and environmental programs
• Applications in economic inclusion and poverty reduction

Module 11: Advanced Analytics for Risk and Impact Assessment

• Predicting project risks and performance challenges
• Using time-series forecasting for evaluation planning
• Predictive impact evaluation methodologies
• Case examples from international development contexts

Module 12: Integrating AI and Automation in Evaluation

• Artificial intelligence applications in monitoring systems
• Machine learning automation for large-scale evaluations
• Natural language processing for qualitative data analysis
• Future trends in AI-enabled M&E

Module 13: Data Visualization for Strategic Decision-Making

• Building dashboards in Power BI and Tableau
• Visual analytics for high-level reporting
• Data-driven communication with stakeholders
• Translating complex data into actionable insights

Module 14: Emerging Issues in Data Science and Evaluation

• Ethical AI and responsible innovation
• Data feminism and inclusive analytics
• The rise of real-time and predictive evaluation systems
• Integrating climate and digital transformation data

Module 15: Institutionalizing Data-Driven M&E Systems

• Building data culture within evaluation organizations
• Strengthening M&E teams with data science skills
• Developing standard operating procedures for analytics
• Building partnerships with data and tech institutions

Module 16: Capstone Project and Hands-on Simulation

• Designing a predictive model for an M&E problem
• Group project presentation and peer feedback
• Reflection on lessons learned and future directions
• Certification and post-training mentorship plan

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: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue 

The training will be held at our Upskill 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 Upskill 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: training@upskilldevelopment.com, +254 721 331 808 

Terms of Payment

Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.