Research Design, Mobile Data Collection, Mapping & Data Analysis Using NVIVO & Python

Introduction

Research is the backbone of informed decision-making and strategic planning. This course offers a unique blend of qualitative and quantitative tools with NVIVO and Python. You'll explore how to design robust research, gather data via mobile platforms, map your findings, and perform detailed analysis to unlock valuable insights.

Target Audience

This course is ideal for:

• Researchers eager to modernize their data collection and analysis techniques.
• Students and academics looking to strengthen their research methodology.
• Data analysts interested in integrating qualitative and quantitative approaches.
• Professionals aiming to leverage modern tools for impactful insights.

Course Objective

By the end of this course, you will:

• Understand the principles of effective research design.
• Learn to conduct mobile data collection and mapping.
• Gain proficiency in NVIVO for qualitative analysis.
• Use Python for quantitative data analysis and visualization.
• Integrate insights to inform decisions and strategies.

Course Modules

Module 1: Fundamentals of Research Design

• Understanding research design principles.
• Differentiating between qualitative and quantitative research methods.
• Developing a solid framework for your research project.

Module 2: Mobile Data Collection Techniques 

• Introduction to mobile data collection platforms.
• Setting up surveys and forms for data gathering.
• Best practices for ensuring data quality and reliability.

Module 3: Mapping and Visualizing Data

• Using GIS tools for mapping research data.
• Techniques for visualizing spatial data effectively.
• Practical applications of mapping in research projects.

Module 4: Data Analysis Using NVIVO for Qualitative Insights 

• Introduction to NVIVO software and its capabilities.
• Coding and categorizing qualitative data in NVIVO.
• Analyzing themes and patterns to derive insights.

Module 5: Data Analysis and Visualization Using Python 

• Setting up Python for data analysis tasks.
• Using libraries like Pandas and Matplotlib for data manipulation.
• Creating compelling visualizations to present research findings.