- TypeTraining or Development Class
- Location Nairobi, kenya,Nairobi,Kenya
- Date 19-12-2022 - 23-12-2022
Education/Teaching/Training/Development
Research/Science
Social Sciences
Course title: Training on Data Management Analysis and Visualization for Agriculture and Rural Development Programs
Training venue: Nairobi Kenya
Course dates:
15/08/2022 - 19/08/2022 : https://bit.ly/3NXyP4U
24/10/2022 - 28/10/2022 : https://bit.ly/3caE57T
19/12/2022 - 23/12/2022 : https://bit.ly/3O2TIM6
Contact us through: Email: [email protected]; Tel: +254732776700 / +254759285295
Introduction
Agriculture sector have impact on number of sectors in the economy including manufacturing, health, education, wholesale and retail, transport, financial services among others. As a result, the agriculture data can go along way beyond the data requirements of the immediate agriculture sector. For farmers, data such as weather data, market price data, and agricultural inputs data can improve farmer productivity by addressing key constraints, providing knowledge and access to appropriate inputs, extension advice, weather warnings and market prices. Data on quantity of produce, seasonal variation in production and varieties of crops grown can inform other actors in other sectors such as manufacturing in planning on expected quantity of produce supply. It is therefore important to collect accurate and timely data from agriculture sector which will inform decision making in interlinked sectors. This 5 days course aims at equipping participants in data collection, management, analysis and interpretation of data in agriculture for decision making.
Duration
5 days
Who can attend?
Course objectives
By the end of the training, the participants will be able to;
Course outline
Module 1: Understanding monitoring and Evaluation principles
Monitoring and evaluation strategy
· Linking M&E frameworks to indicators
Module 2: Performance monitoring
Measuring results in Agriculture and RD programmes
Module 3: Data collection, management and data quality
Module 4: Introduction to qualitative data Analysis
Module 5: Quantitative data Analysis using SPSS or Stata or R