Research Design,ODK Mobile Data Collection,GIS Mapping,Data analysis using NVIVO and R course

INTRODUCTION

New developments in data science offer a tremendous opportunity to improve decision-making. In the development world, there has been an increase in the number of data gathering initiative such as baseline surveys, Socio-Economic Surveys, Demographic and Health Surveys, Nutrition Surveys, Food Security Surveys, Program Evaluation Surveys, Employees, customers and vendor satisfaction surveys, and opinion polls among others, all intended to provide data for decision making.

It is essential that these efforts go beyond merely generating new insights from data but also to systematically enhance individual human judgment in real development contexts. How can organizations better manage the process of converting the potential of data science to real development outcomes This ten days hands-on course is tailored to put all these important consideration into perspective. It is envisioned that upon completion, the participants will be empowered with the necessary skills to produce accurate and cost effective data and reports that are useful and friendly for decision making.

It will be conducted using ODK, GIS, NVIVO and R

DURATION

2 Weeks

LEARNING OBJECTIVES

•          Understand and appropriately use statistical terms and concepts
•          Design and Implement universally acceptable Surveys
•          Convert data into various formats using appropriate software
•          Use mobile data gathering tools such as Open Data Kit (ODK)
•          Use GIS software to plot and display data on basic maps
•          Qualitative data analysis using NVIVO
•          Analyze t data by applying appropriate statistical techniques using R
•          Interpret the  statistical analysis using R
•          Identify statistical techniques a best suited to data and questions
•          Strong foundation in fundamental statistical concepts
•          Implement different statistical analysis in R and interpret the results
•          Build intuitive data visualizations
•          Carry out formalized hypothesis testing
•          Implement linear modelling techniques such multiple regressions and GLMs
•          Implement advanced regression analysis and multivariate analysis
•          Write reports from survey data
•          Put strategies to improve data demand and use in decision making