Agriculture and Food Security Data Analytics Training

About This Course

Data is rapidly transforming how the world understands and responds to food security challenges. From satellite-derived crop monitoring to household consumption surveys, the volume and variety of agriculture and food security data available to practitioners has never been greater — yet the capacity to turn that data into reliable evidence remains a critical bottleneck across Africa and beyond.

This ten-day intensive programme from TrainingComplex is designed to close that gap. Participants will build a comprehensive skill set spanning the entire data value chain: survey design and data collection, database management, statistical analysis, geospatial mapping, machine-learning-assisted forecasting, and results communication tailored to decision-makers, donors, and policy audiences.

The curriculum draws on internationally recognised frameworks — including the Integrated Food Security Phase Classification (IPC), the FIES scale, and LSMS-ISA survey methodologies — and integrates real datasets from East and West Africa, enabling participants to practise with the kind of messy, real-world information they encounter on the job.

Delivered through a carefully sequenced blend of concept sessions, hands-on tool labs, case studies, and group analytical projects, the programme ensures that learning is immediately transferable to participants' organisations. By the end of the course, each participant will have produced a data analysis portfolio piece relevant to their own agricultural or food security context.

Who Should Attend

• Agricultural economists and food security analysts in government ministries and parastatals
• Data officers and M&E specialists working with NGOs, bilateral donors, and UN agencies
• Research officers at agricultural research institutions and universities
• Nutritional surveillance and early warning system officers
• Policy analysts and programme officers in rural development and humanitarian response
• GIS and remote sensing technicians working on crop and land-use monitoring
• Development consultants providing technical support on food security programmes
• Statisticians and data managers in national statistics offices covering agriculture

Learning Objectives

• Understand the global and regional food security data landscape, including key indicators, frameworks, and data sources
• Design robust agricultural surveys and household food security questionnaires aligned to IPC, HDDS, FIES, and LSMS methodologies
• Collect high-quality field data using mobile data collection tools such as KoBoToolbox and ODK
• Clean, validate, and manage large agricultural and food security datasets using R and Python
• Conduct descriptive and inferential statistical analyses of crop production, dietary diversity, and food access data
• Apply geospatial analysis and remote sensing techniques to map agro-ecological zones, crop conditions, and food insecurity hotspots
• Build and interpret predictive models for crop yield estimation and food security early warning
• Visualize complex agricultural data through dashboards, charts, and maps for technical and non-technical audiences
• Interpret and contextualise food security classification results using the IPC Acute Food Insecurity scale
• Produce high-quality analytical reports and policy briefs that translate data findings into actionable recommendations
• Present data-driven evidence effectively to government stakeholders, donors, and community audiences
• Apply ethical principles in agricultural data collection, management, and dissemination
• Design a data analytics plan for a real or simulated food security programme

Course Modules

Requirements & Prerequisites

Participants should have a working knowledge of food security, agriculture, or rural development concepts gained through professional experience or academic study. Basic familiarity with spreadsheet software such as Microsoft Excel is expected. No prior experience with statistical programming languages or GIS software is required, as these tools are introduced from the ground up during the course.

Training Methodology

• Hands-on software labs using R, Python (pandas/geopandas), QGIS, and KoBoToolbox on participants' own laptops with guided step-by-step exercises
• Analysis of real agricultural and food security datasets sourced from FAOSTAT, LSMS-ISA surveys, and WFP VAM databases
• Case study discussions based on food security crises and agricultural shocks in East Africa, the Sahel, and Southern Africa
• Group analytical projects in which teams investigate a food security question, produce visualisations, and present findings to the cohort
• IPC classification simulation exercises using structured scenario datasets and peer review of classification decisions
• Dashboard-building workshops where participants create interactive data visualisations in R Shiny or Power BI for programme reporting
• Policy brief writing practicum with structured peer and facilitator feedback on clarity, evidence use, and recommendations
• Reflective journalling prompts at the end of each day to consolidate learning and connect concepts to participants' home contexts
• Guest input sessions from practitioners in national statistics offices, FAO, and WFP sharing lived experience of food security data systems
• Individual portfolio project developed progressively throughout the ten days and presented on the final day

Organisational Benefits

• Strengthened internal capacity to generate timely, high-quality food security evidence without over-reliance on external consultants
• Improved quality and credibility of agricultural monitoring and evaluation reports submitted to donors and governments
• Faster and more accurate detection of food security deterioration through enhanced early warning data systems
• Better targeting of food assistance and agricultural inputs through geospatial analysis and vulnerability mapping
• Enhanced ability to leverage open data sources — satellite imagery, price monitoring, rainfall data — at no additional cost
• Increased competitiveness when responding to donor calls that require demonstrated data analytics capacity
• Stronger cross-departmental data culture and shared analytical language between programme, M&E, and policy teams
• Reduced costs associated with data quality errors, misclassification, and programme mis-targeting

Where Is Agriculture and Food Security Data Analytics Training Offered?

This course runs in-person across: Mombasa, Dar es Salaam, Riyadh, Lagos, Cape Town, Accra, Naivasha, Kampala, Johannesburg, Kigali, Nairobi, Pretoria, Arusha, Kisumu, Abuja, Nakuru, Zanzibar, Dubai, Addis Ababa, plus Live Virtual (online) cohorts. Browse live dates for your city below.

Upcoming Schedules

+228 more dates — view live schedule →

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