Purpose
Research design, survey CTO, mobile data collection, GIS mapping, and analysis is a process that involves collecting, organizing, and analyzing data to extract insights and support decision-making.
Overall, research design, survey CTO, mobile data collection, GIS mapping, and analysis is a process that involves collecting, organizing, and analyzing data to support decision-making. This process typically includes steps such as research design, survey CTO, mobile data collection, GIS mapping, and data analysis, and is widely
 Course Objectives
- Providing participants with an understanding of the principles and practices of research design and survey methodology, including sampling techniques and data collection methods
- Introducing participants to the use of mobile technologies for data collection, including the use of smartphones and tablets for survey administration and data collection
- Teaching participants about CTO (computer-assisted telephone interviewing) and its application in survey research
- Familiarizing participants with the use of GIS (geographic information systems) for mapping and analyzing data
- Giving participants hands-on experience with various research tools and techniques, such as survey software, mobile data collection apps, and GIS software
- Preparing participants to design and conduct their own research projects using the knowledge and skills gained in the course.
Target Audience
- Market researchers: Professionals who use this process to collect data from consumers and analyze patterns and trends in consumer behavior.
- Social scientists: Researchers in fields such as sociology, anthropology, psychology, and political science who use this process to collect and analyze data from surveys and interviews.
- Urban planners: Professionals who use this process to collect and analyze data on land use, transportation, and population density to inform urban planning and development decisions.
- Environmental scientists: Professionals who use this process to collect and analyze data on environmental factors such as air and water quality, land use, and biodiversity to inform environmental management and conservation decisions.
- Public health researchers: Professionals who use this process to collect and analyze data on health outcomes and risk factors to inform public health policies and interventions.
- Business analysts: Professionals who use this process to collect and analyze data on sales, marketing, and customer behavior to inform business decisions.
- IT professionals: Professionals in IT departments who use this process to automate data management tasks, enforce data governance rules, and provide data access to different stakeholders.
Program Outline
Topic 1. Research design
This step involves planning and designing a research study, including identifying the research question, selecting the appropriate data collection methods, and determining the sample size and sampling methods.
Topic 2. Survey CTO (computer-assisted telephone interviewing)
This step involves using computer-assisted telephone interviewing (CATI) software to collect data from respondents over the phone. CATI software allows for more efficient data collection and can be used to administer complex surveys.
Topic 3. Mobile data collection
This step involves collecting data using mobile devices, such as smartphones and tablets, which allows for data collection in the field and in real-time.
Topic 4. GIS mapping
This step involves using geographic information systems (GIS) software to create maps and visualizations of the data. GIS software allows for the integration of spatial and non-spatial data and can be used to analyze patterns and relationships in the data.
Topic 5. Data cleaning and preparation
This step involves cleaning, transforming, and consolidating large and complex datasets. Tasks in this step might include removing missing or duplicate data, handling outliers, and transforming variables to make them suitable for analysis.
Topic 6. Data exploration and visualization
This step involves exploring the data to get a sense of its distribution, patterns, and relationships. Tasks in this step might include generating descriptive statistics, creating visualizations, and identifying outliers and anomalies.
Topic 7. Data analysis
This step involves conducting a wide range of data analysis to extract insights and test hypotheses. Tasks in this step might include conducting inferential statistics, building predictive models, and testing for relationships between variables.
Topic 8. Report generation
This step involves generating detailed reports and summaries of the analysis for stakeholders and decision-makers. Tasks in this step might include creating tables, figures, and summaries to present the key findings and conclusions.
Topic 9. Automation
This step involves automating repetitive tasks, such as data preparation and statistical analysis, to improve efficiency and reduce errors. Tasks in this step might include creating functions, scripts, or Jupyter Notebooks to automate processes.
Topic 10. Data Governance
This step involves enforcing data governance rules, such as data lineage and data quality, to ensure data integrity and compliance using libraries such as DVC or