Course Introduction
Evaluation Research, Evidence Synthesis and Decision Support Training Course provides a comprehensive framework for designing, conducting, interpreting, and communicating rigorous evaluations that generate credible evidence for organizational, programme, policy, and investment decisions. The course brings together evaluation research, research methodology, evidence synthesis, critical appraisal, data analysis, knowledge translation, and decision-support approaches to help participants determine what works, for whom, under what conditions, and with what level of confidence. It emphasizes practical methods for converting research findings and evaluation evidence into useful recommendations for decision-makers.
The programme develops participants’ understanding of evaluation theory and practice, including needs assessment, formative evaluation, process evaluation, outcome evaluation, impact evaluation, economic evaluation, implementation evaluation, and real-world programme assessment. Participants learn how to formulate evaluation questions, develop theories of change and logic models, identify appropriate indicators, establish evaluation criteria, select study designs, and develop evidence collection strategies. Particular attention is given to aligning evaluation methods with the purpose, context, resources, stakeholders, and decisions that the evaluation is intended to support.
A central component of the training focuses on evidence synthesis and the systematic integration of findings from multiple studies, evaluations, datasets, and other credible evidence sources. Participants explore systematic reviews, rapid reviews, scoping reviews, narrative synthesis, meta-analysis, realist approaches, evidence mapping, and structured literature review methods. The course addresses searching, screening, data extraction, quality appraisal, risk of bias assessment, heterogeneity, synthesis strategies, and transparent reporting so that participants can assess whether a body of evidence provides sufficiently reliable support for a particular conclusion or decision.
The course also examines quantitative and qualitative approaches for analyzing evaluation evidence and understanding programme performance. Participants explore descriptive and inferential statistics, effect measurement, comparative analysis, qualitative coding, thematic synthesis, mixed-methods integration, and interpretation of uncertainty. Emphasis is placed on selecting methods that appropriately address evaluation questions rather than applying analytical techniques without considering study design, data quality, assumptions, context, and limitations. Participants learn how to interpret findings cautiously and distinguish statistical results from practical, organizational, and policy implications.
Modern evidence-informed decision-making increasingly requires rapid access to high-quality evidence and effective systems for translating complex findings into actionable recommendations. The programme therefore addresses decision-support frameworks, evidence-to-decision processes, stakeholder engagement, knowledge translation, evidence communication, dashboards, evidence briefs, and decision matrices. Emerging issues such as artificial intelligence-assisted evidence synthesis, automation of literature screening, machine-assisted data extraction, living evidence reviews, real-time evaluation, open science, reproducibility, and responsible use of AI are also examined alongside their methodological and governance implications.
By the end of the programme, participants will be able to design appropriate evaluations, critically appraise research evidence, synthesize findings across diverse sources, analyze evaluation data, assess uncertainty, and communicate evidence in forms that support practical decisions. The training strengthens the connection between research quality and decision usefulness by helping participants consider not only whether evidence is methodologically credible, but also whether it is relevant, timely, applicable, equitable, and understandable to decision-makers. Participants will leave with a structured approach to producing and using evidence for programme improvement, organizational learning, policy development, resource allocation, and strategic decision-making.
Duration
10 days
Who Should Attend
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Monitoring and evaluation professionals responsible for designing, managing, or interpreting programme and project evaluations.
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Researchers and research managers conducting quantitative, qualitative, mixed-methods, or applied evaluation studies.
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Policy analysts and public-sector professionals involved in evidence-informed policy development and programme improvement.
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Programme managers seeking rigorous approaches for assessing effectiveness, implementation, outcomes, and impact.
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Development practitioners working with donor-funded, community, health, education, social, or economic development programmes.
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Data analysts and statisticians supporting evaluation studies, evidence synthesis, and decision-support activities.
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Academics, lecturers, and postgraduate researchers undertaking evaluation research or evidence synthesis projects.
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Health professionals and health-system managers involved in evidence-based programme evaluation and service improvement.
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Consultants providing research, evaluation, advisory, performance improvement, and evidence-based decision-support services.
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Knowledge management and knowledge translation professionals responsible for converting research findings into usable evidence.
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Government and institutional decision-makers who need to interpret research evidence for planning, investment, and policy decisions.
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Non-governmental organization professionals responsible for demonstrating programme effectiveness, accountability, and learning.
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Quality improvement and performance management professionals using evaluation evidence to improve organizational processes and outcomes.
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Research librarians, information specialists, and evidence synthesis professionals supporting systematic searches and evidence reviews.
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Professionals transitioning into monitoring, evaluation, research, evidence synthesis, policy analysis, or decision-support roles.
Course Objectives
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Develop participants’ understanding of evaluation research principles and enable them to select appropriate evaluation approaches according to programme objectives, context, stakeholders, and intended decisions.
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Enable participants to formulate clear evaluation questions, criteria, indicators, outcomes, assumptions, and measures that provide a strong foundation for rigorous and useful evaluation studies.
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Equip learners with practical skills for developing theories of change, logic models, results frameworks, and evaluation frameworks that clearly connect interventions, activities, outputs, outcomes, and impacts.
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Strengthen participants’ ability to select appropriate quantitative, qualitative, and mixed-methods evaluation designs while considering validity, feasibility, ethics, resources, and contextual complexity.
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Develop practical competence in systematic literature searching, evidence identification, screening, extraction, critical appraisal, and synthesis across diverse research and evaluation sources.
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Enable participants to assess methodological quality, risk of bias, consistency, relevance, applicability, and certainty when interpreting findings from individual studies and bodies of evidence.
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Introduce participants to quantitative evaluation techniques for measuring outcomes, comparing groups, assessing changes over time, estimating effects, and interpreting uncertainty in evaluation findings.
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Build competency in qualitative evaluation methods, including interview analysis, focus-group analysis, thematic coding, framework analysis, and interpretation of stakeholder and contextual perspectives.
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Develop the ability to integrate quantitative and qualitative findings through mixed-methods approaches to produce comprehensive explanations of programme performance and observed outcomes.
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Enable participants to apply evidence synthesis methods such as systematic reviews, rapid reviews, scoping reviews, narrative synthesis, realist synthesis, and meta-analysis appropriately.
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Strengthen participants’ ability to translate complex evaluation and research findings into evidence briefs, recommendations, dashboards, decision matrices, and other practical decision-support products.
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Prepare participants to critically assess emerging technologies such as artificial intelligence-assisted evidence synthesis while addressing transparency, reproducibility, bias, governance, human oversight, and responsible use.
Comprehensive Course Outline
Module 1: Foundations of Evaluation Research and Evidence-Informed Decision-Making
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Principles, purposes, types, and applications of evaluation research across programmes, projects, policies, services, organizations, and interventions.
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Relationship between evaluation, research, monitoring, performance measurement, quality improvement, learning, accountability, and evidence-informed decision-making.
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Evaluation utilization, stakeholder needs, decision contexts, evaluation users, and the importance of designing evidence around clearly defined decisions.
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Challenges in generating and using evaluation evidence, including uncertainty, incomplete information, contextual variation, competing interests, and resource limitations.
Module 2: Evaluation Planning and Evaluation Questions
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Developing evaluation purposes, objectives, key evaluation questions, evaluation criteria, indicators, outcomes, assumptions, and measurable performance standards.
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Stakeholder identification and engagement approaches for understanding information needs and ensuring evaluation findings are relevant to intended users.
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Evaluation planning processes covering scope, timelines, resources, responsibilities, deliverables, data requirements, quality assurance, and reporting arrangements.
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Developing evaluation matrices that connect evaluation questions with indicators, data sources, collection methods, analytical approaches, and intended uses of findings.
Module 3: Theories of Change, Logic Models and Evaluation Frameworks
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Principles of theories of change and their application in explaining how interventions are expected to produce outputs, outcomes, and longer-term impacts.
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Development and interpretation of logic models linking inputs, activities, outputs, intermediate outcomes, final outcomes, impacts, assumptions, and external factors.
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Identifying causal pathways, implementation assumptions, contextual influences, unintended effects, and potential mechanisms within complex programmes.
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Using programme theories and evaluation frameworks to guide evaluation design, data collection, interpretation, learning, adaptation, and decision-making.
Module 4: Evaluation Designs and Methodological Approaches
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Experimental, quasi-experimental, observational, longitudinal, cross-sectional, case-study, and comparative evaluation designs and their appropriate applications.
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Formative, process, outcome, impact, implementation, developmental, economic, and summative evaluation approaches across different programme environments.
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Selecting evaluation designs according to research questions, intervention characteristics, available data, ethical requirements, feasibility, and stakeholder expectations.
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Internal validity, external validity, reliability, generalizability, contextual validity, and methodological limitations affecting interpretation of evaluation findings.
Module 5: Quantitative Evaluation Methods and Data Analysis
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Descriptive analysis techniques for summarizing evaluation populations, programme participation, outcomes, performance indicators, and changes over time.
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Inferential methods for estimating differences, associations, effects, confidence intervals, statistical significance, and uncertainty in evaluation datasets.
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Comparative evaluation approaches for intervention and comparison groups, pre-and-post assessments, longitudinal measurements, and observational programme data.
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Interpretation of quantitative results with attention to assumptions, missing data, measurement quality, effect magnitude, uncertainty, and practical significance.
Module 6: Qualitative Evaluation Research and Analysis
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Qualitative research designs and data collection methods including interviews, focus groups, observations, document analysis, case studies, and participatory approaches.
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Coding and thematic analysis techniques for identifying patterns, explanations, experiences, perceptions, barriers, facilitators, and contextual influences.
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Framework analysis, content analysis, narrative approaches, and other qualitative synthesis techniques for organizing complex evaluation evidence systematically.
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Ensuring credibility, reflexivity, transparency, triangulation, dependability, and appropriate interpretation of qualitative evaluation findings.
Module 7: Mixed-Methods Evaluation and Evidence Integration
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Principles of mixed-methods evaluation and approaches for integrating quantitative measurements with qualitative explanations and contextual evidence.
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Sequential, convergent, embedded, and multiphase mixed-methods designs for addressing complex evaluation questions from complementary perspectives.
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Techniques for connecting datasets, comparing findings, explaining contradictions, and developing integrated interpretations from multiple evidence streams.
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Practical approaches for presenting integrated evidence using joint displays, evidence matrices, narratives, dashboards, and decision-oriented reporting formats.
Module 8: Systematic Reviews and Evidence Synthesis
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Foundations of systematic reviewing, including review questions, eligibility criteria, search strategies, screening procedures, extraction frameworks, and transparent documentation.
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Systematic review workflows for identifying, selecting, assessing, and synthesizing evidence from peer-reviewed research and relevant evaluation literature.
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Rapid review and evidence assessment approaches for decision environments requiring credible findings within shorter timeframes and constrained resources.
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Common evidence synthesis challenges including publication bias, heterogeneous studies, incomplete reporting, duplicate evidence, and differences in populations or interventions.
Module 9: Scoping, Narrative and Realist Evidence Synthesis
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Scoping review methods for mapping concepts, evidence types, research gaps, emerging themes, and broad bodies of literature within complex fields.
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Narrative synthesis approaches for integrating findings where studies differ substantially in design, measures, interventions, populations, or analytical methods.
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Realist evaluation and realist synthesis concepts for examining how interventions work, for whom, under which circumstances, and through what mechanisms.
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Evidence mapping and knowledge mapping techniques for organizing large evidence bases and identifying priorities for further research or decision-making.
Module 10: Meta-Analysis, Critical Appraisal and Certainty of Evidence
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Principles of meta-analysis, effect measures, statistical heterogeneity, fixed-effect and random-effects approaches, and interpretation of pooled findings.
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Critical appraisal frameworks for evaluating research design, methodological quality, relevance, risk of bias, consistency, and limitations of individual studies.
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Assessment of publication bias, selective reporting, small-study effects, heterogeneity, indirectness, imprecision, and other factors influencing confidence in synthesized evidence.
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Approaches for communicating certainty, strength, limitations, and applicability of evidence without overstating what research findings can demonstrate.
Module 11: Evaluation Data Quality, Validity and Research Integrity
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Data quality dimensions including accuracy, completeness, consistency, timeliness, reliability, validity, traceability, and fitness for evaluation purposes.
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Approaches for identifying measurement error, missing information, inconsistent records, data anomalies, reporting problems, and potential sources of analytical bias.
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Research integrity, ethical evaluation practice, responsible data management, confidentiality, informed participation, transparency, and appropriate documentation of analytical decisions.
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Reproducibility, audit trails, version control, transparent workflows, preregistration, open materials, and other practices supporting trustworthy evaluation research.
Module 12: Decision Analysis and Evidence-to-Decision Frameworks
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Decision-support frameworks for connecting research evidence with stakeholder values, implementation considerations, resource requirements, risks, benefits, and contextual factors.
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Evidence-to-decision processes for structuring complex choices and making the assumptions, trade-offs, uncertainties, and evidence supporting recommendations explicit.
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Decision matrices, multi-criteria decision analysis, prioritization frameworks, scenario assessment, and structured comparison of alternative interventions or strategies.
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Incorporating feasibility, acceptability, equity, sustainability, cost, implementation requirements, and stakeholder preferences into evidence-informed decisions.
Module 13: Economic Evaluation and Resource Allocation Evidence
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Foundations of cost analysis, cost-effectiveness analysis, cost-benefit analysis, cost-utility analysis, and other economic evaluation approaches.
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Identification and measurement of intervention costs, resource utilization, outcomes, benefits, opportunity costs, and relevant economic assumptions.
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Interpreting incremental costs, incremental outcomes, uncertainty, sensitivity analysis, and alternative scenarios when comparing programmes or interventions.
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Using economic evidence alongside effectiveness, equity, feasibility, acceptability, and contextual considerations to support resource allocation decisions.
Module 14: Knowledge Translation, Evidence Communication and Decision Support
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Principles of knowledge translation and approaches for moving research and evaluation findings into practice, policy, organizational learning, and strategic decisions.
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Development of evidence briefs, executive summaries, policy briefs, technical reports, presentations, dashboards, and other decision-oriented communication products.
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Data visualization and evidence storytelling techniques for presenting complex findings clearly while preserving methodological accuracy and appropriate uncertainty.
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Stakeholder communication strategies for adapting evidence messages to executives, technical teams, practitioners, policymakers, communities, and other evidence users.
Module 15: Artificial Intelligence and Emerging Evidence Synthesis Technologies
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Applications of artificial intelligence and machine learning in literature searching, study screening, evidence classification, data extraction, synthesis support, and research workflow automation.
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Large language models and AI-assisted research workflows, including opportunities, limitations, verification requirements, reproducibility considerations, and appropriate human oversight.
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Living systematic reviews, automated evidence surveillance, real-time evidence platforms, and emerging technologies for continuously updating evidence bases.
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Risks and governance challenges involving hallucination, algorithmic bias, source verification, confidentiality, transparency, intellectual property, and responsible AI-supported research.
Module 16: Advanced Evaluation Practice, Reporting and Decision Implementation
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Designing complete evaluation protocols that connect research questions, methodologies, evidence requirements, analysis plans, quality assurance, ethics, and reporting strategies.
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Developing actionable recommendations that distinguish clearly between findings, interpretations, assumptions, limitations, implications, and proposed implementation actions.
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Translating evaluation evidence into organizational improvement, policy decisions, programme adaptation, investment priorities, strategic planning, and resource allocation processes.
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Establishing evidence-use systems that support continuous learning, adaptive management, monitoring of implemented decisions, and ongoing evaluation of decision outcomes.
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 a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, 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.
