Format: Live instructor-led online training via Zoom / Microsoft Teams

Advanced Data Science for Evaluators Training Course

Course Introduction

Advanced Data Science for Evaluators has become a critical discipline in modern monitoring, evaluation, research, and evidence-based decision-making. Governments, development agencies, humanitarian organizations, non-governmental organizations, research institutions, and private sector organizations increasingly rely on advanced data science methodologies to generate insights, evaluate program performance, predict outcomes, measure impact, and improve accountability. The integration of data science techniques such as machine learning, predictive analytics, artificial intelligence, big data analytics, geospatial analysis, and advanced statistical modeling has transformed how evaluators design studies, analyze complex datasets, and communicate findings for policy and program improvement.

This Advanced Data Science for Evaluators Training Course provides participants with comprehensive knowledge and practical skills in data science concepts, analytical frameworks, machine learning applications, predictive modeling, data visualization, and evidence generation methodologies. The course covers data management, programming for analytics, statistical computing, data mining, artificial intelligence applications, advanced evaluation designs, causal inference techniques, forecasting models, and data-driven decision support systems. Participants will gain practical competencies in transforming large and complex datasets into actionable evidence that supports strategic planning, organizational learning, and performance improvement.

The training emphasizes the application of advanced data science techniques in monitoring and evaluation systems, impact assessment, policy analysis, program performance monitoring, and development planning. Participants will learn how to integrate quantitative and qualitative data sources, build predictive models, apply advanced statistical methods, utilize machine learning algorithms, and create interactive dashboards and analytical reports that enhance organizational accountability and adaptive management.

Through practical exercises, simulations, hands-on projects, real-world case studies, and collaborative group assignments, participants will develop the technical and analytical capabilities necessary to implement advanced data science methodologies in evaluation practice. The course equips professionals with competencies to design robust analytical systems, perform sophisticated evaluations, generate predictive insights, and support evidence-based decision-making in complex organizational and development environments.

Course Objectives

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

1.     Understand advanced data science principles and applications in evaluation.

2.     Apply statistical computing and analytical methodologies to evaluation studies.

3.     Manage, clean, and integrate large and complex datasets.

4.     Utilize machine learning techniques for predictive evaluation and impact forecasting.

5.     Apply advanced statistical methods and causal inference techniques.

6.     Develop data visualization dashboards and analytical reporting systems.

7.     Conduct geospatial and big data analysis for evaluation purposes.

8.     Design predictive models for monitoring and evaluation systems.

9.     Apply artificial intelligence tools to evidence generation and decision support.

10.  Integrate data science solutions into evaluation frameworks and organizational learning systems.

Organizational Benefits

1.     Enhanced evidence-based decision-making capabilities.

2.     Improved evaluation quality and analytical rigor.

3.     Better forecasting and predictive performance measurement.

4.     Strengthened monitoring and evaluation systems.

5.     Enhanced organizational learning and adaptive management.

6.     Improved resource allocation and strategic planning.

7.     Increased capacity to manage and analyze large datasets.

8.     Improved policy formulation and program design.

9.     Enhanced accountability and impact assessment capabilities.

10.  Strengthened digital transformation and innovation initiatives.

Target Participants

This course is designed for Monitoring and Evaluation Specialists, Researchers, Data Analysts, Statisticians, Data Scientists, Program Managers, Project Managers, Policy Analysts, Government Officials, Development Practitioners, Information Technology Specialists, Management Information Systems Professionals, Public Health Analysts, Agricultural Researchers, Academicians, Business Intelligence Analysts, Monitoring Officers, Evaluation Consultants, Research Officers, and professionals involved in evidence generation, data analytics, impact assessment, and performance management.

Course Outline

Module 1: Introduction to Advanced Data Science for Evaluation

1.     Concepts and principles of data science

2.     Applications of data science in evaluation

3.     Data-driven decision-making frameworks

4.     Digital transformation in evaluation practice

5.     Emerging trends in evaluation analytics

6.     Case Study: Data science applications in development evaluations

Module 2: Data Management and Integration

1.     Data acquisition and management strategies

2.     Structured and unstructured data sources

3.     Data cleaning and preprocessing techniques

4.     Data transformation and integration methods

5.     Data quality assurance procedures

6.     Case Study: Integrating multiple datasets for evaluation studies

Module 3: Statistical Computing and Analytical Foundations

1.     Descriptive and inferential statistics

2.     Probability theory and statistical distributions

3.     Regression and correlation analysis

4.     Statistical inference methodologies

5.     Statistical programming concepts

6.     Case Study: Statistical analysis of evaluation datasets

Module 4: Programming for Data Science

1.     Introduction to analytical programming environments

2.     Data manipulation and processing techniques

3.     Exploratory data analysis methodologies

4.     Data visualization programming concepts

5.     Reproducible analytical workflows

6.     Case Study: Building analytical workflows for evaluations

Module 5: Machine Learning for Evaluation

1.     Fundamentals of machine learning

2.     Supervised learning methodologies

3.     Unsupervised learning techniques

4.     Predictive classification and regression models

5.     Model selection and optimization strategies

6.     Case Study: Machine learning applications in impact evaluations

Module 6: Predictive Analytics and Forecasting

1.     Predictive analytics frameworks

2.     Forecasting methodologies and models

3.     Time-series analysis techniques

4.     Risk prediction and scenario analysis

5.     Performance forecasting approaches

6.     Case Study: Forecasting program outcomes and impacts

Module 7: Big Data Analytics for Evaluators

1.     Fundamentals of big data analytics

2.     Large-scale data processing techniques

3.     Real-time data analytics systems

4.     Data mining methodologies

5.     Big data applications in evaluations

6.     Case Study: Big data analytics for development programs

Module 8: Geospatial Analytics and Spatial Evaluation

1.     Principles of geospatial analysis

2.     Geographic information systems for evaluation

3.     Spatial data collection methodologies

4.     Mapping and visualization techniques

5.     Spatial impact assessment methods

6.     Case Study: Geospatial analysis for project evaluations

Module 9: Advanced Evaluation Designs and Causal Inference

1.     Experimental and quasi-experimental designs

2.     Causal inference methodologies

3.     Counterfactual analysis techniques

4.     Propensity score matching methods

5.     Impact estimation and attribution analysis

6.     Case Study: Causal analysis in program evaluations

Module 10: Data Visualization and Communication

1.     Principles of analytical visualization

2.     Dashboard design and development

3.     Data storytelling techniques

4.     Interactive reporting methodologies

5.     Communication of analytical findings

6.     Case Study: Executive dashboards for evaluation reporting

Module 11: Artificial Intelligence in Evaluation

1.     Artificial intelligence concepts and applications

2.     Natural language processing methodologies

3.     Text analytics and sentiment analysis

4.     AI-powered decision support systems

5.     Ethical considerations in AI applications

6.     Case Study: Artificial intelligence in evidence generation

Module 12: Integrating Data Science into Monitoring and Evaluation Systems

1.     Data science strategy development

2.     Designing data-driven evaluation systems

3.     Performance measurement and learning frameworks

4.     Organizational readiness and capacity development

5.     Sustainability and continuous improvement strategies

6.     Case Study: Implementing data science solutions in evaluation systems

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.