Format: Live instructor-led online training via Zoom / Microsoft Teams

Quantitative and Qualitative Data Analysis using R, Python, NVIVO, ODK, and Power BI Course

Introduction

In the era of evidence-based decision-making, organizations require professionals who can effectively collect, analyze, visualize, and interpret data to generate actionable insights. The Quantitative and Qualitative Data Analysis using R, Python, NVIVO, ODK, and Power BI Course is designed to equip participants with comprehensive skills in statistical data analysis, qualitative research analysis, mobile data collection, business intelligence reporting, and data-driven decision-making. This program integrates advanced analytical tools widely used in research institutions, NGOs, government agencies, and corporate organizations.

The course focuses on ODK mobile data collection, data cleaning and management using R and Python, qualitative coding and thematic analysis using NVIVO, and interactive dashboard creation using Microsoft Power BI. Participants will learn to design digital questionnaires, collect real-time field data, perform descriptive and inferential statistics, analyze interview transcripts, and build professional dashboards for reporting and stakeholder communication.

Through real-world datasets and hands-on projects, learners will master survey data analysis, regression modeling, qualitative content analysis, sentiment analysis, GIS-enabled data visualization, monitoring and evaluation (M&E) analytics, and automated reporting workflows. This course bridges the gap between field data collection, statistical analysis, qualitative interpretation, and executive reporting.

By the end of this training, participants will be fully equipped to manage end-to-end data workflows—from digital data collection to advanced analytics and visualization—enabling organizations to improve program design, policy formulation, project monitoring, research quality, and strategic planning.

Course Objectives

1. Design digital surveys using ODK.
2. Collect and manage mobile field data.
3. Clean and prepare datasets using R and Python.
4. Perform descriptive and inferential statistical analysis.
5. Conduct qualitative coding and thematic analysis in NVIVO.
6. Apply regression and predictive modeling techniques.
7. Visualize data using Power BI dashboards.
8. Integrate qualitative and quantitative findings.
9. Automate data analysis and reporting workflows.
10. Produce professional analytical reports.

Organizational Benefits

1. Improved data-driven decision making.
2. Enhanced monitoring and evaluation quality.
3. Reduced data collection and reporting errors.
4. Faster reporting and visualization workflows.
5. Improved research and program accountability.
6. Better donor and stakeholder reporting.
7. Improved data management and archiving.
8. Enhanced staff analytical capacity.
9. Improved policy and project design quality.
10. Strengthened organizational performance management.

Target Participants

• Monitoring and evaluation officers
• Data analysts and statisticians
• Research officers and academics
• NGO and development practitioners
• Government planning officers
• Business intelligence analysts
• Public health and social science researchers
• Market research professionals
• Policy analysts
• Graduate students

Course Outline

Module 1: Introduction to Data Analytics and Research Design

1. Data types: quantitative and qualitative
2. Research design principles
3. Ethics in data collection
4. Data lifecycle management
5. Analytical frameworks
6. Case Study: NGO baseline survey design

Module 2: Digital Data Collection using ODK

1. Designing ODK forms
2. Mobile data collection workflows
3. Validation and skip logic
4. Data synchronization
5. GPS and multimedia data
6. Case Study: Household survey data collection

Module 3: Data Cleaning and Management with R and Python

1. Data import and preprocessing
2. Missing data handling
3. Data transformation
4. Data merging and reshaping
5. Data quality checks
6. Case Study: Cleaning a health survey dataset

Module 4: Descriptive and Inferential Statistics

1. Summary statistics
2. Data visualization in R and Python
3. Hypothesis testing
4. Correlation analysis
5. ANOVA and t-tests
6. Case Study: Comparing community health indicators

Module 5: Regression and Predictive Modeling

1. Linear and logistic regression
2. Model diagnostics
3. Feature selection
4. Predictive analytics
5. Model interpretation
6. Case Study: Predicting project outcomes

Module 6: Qualitative Data Analysis using NVIVO

1. Data import and transcription
2. Coding frameworks
3. Thematic analysis
4. Content analysis
5. Word frequency and queries
6. Case Study: Community feedback analysis

Module 7: Mixed Methods Data Integration

1. Triangulation techniques
2. Integrating qualitative and quantitative results
3. Joint displays
4. Interpretation strategies
5. Reporting mixed methods
6. Case Study: Program evaluation study

Module 8: Data Visualization with Power BI

1. Data modeling
2. DAX basics
3. Interactive dashboards
4. Drill-through reports
5. Publishing and sharing dashboards
6. Case Study: Management performance dashboard

Module 9: GIS and Spatial Data Analysis

1. GPS data integration
2. Mapping in Power BI
3. Spatial data cleaning
4. Hotspot analysis
5. Location-based dashboards
6. Case Study: Mapping service coverage

Module 10: Monitoring and Evaluation Analytics

1. Indicator tracking
2. Baseline and end-line analysis
3. Logical framework analytics
4. Performance scorecards
5. Data quality audits
6. Case Study: NGO M&E reporting

Module 11: Automation and Reporting

1. Automated scripts in Python
2. Scheduled Power BI refresh
3. Report templates
4. Version control
5. Data archiving
6. Case Study: Automated monthly reporting

Module 12: Advanced Analytics and Capstone Project

1. Advanced statistical models
2. Machine learning basics
3. Text mining
4. Capstone project design
5. Project presentation
6. Case Study: Integrated data analytics project

General Information

1. Customized Training: All our courses can be tailored to meet the specific needs of participants.
2. Language Proficiency: Participants should have a good command of the English language.
3. Comprehensive Learning: Our training includes well-structured presentations, practical exercises, web-based tutorials, and collaborative group work. Our facilitators are seasoned experts with over a decade of experience.
4. Certification: Upon successful completion of training, participants will receive a certificate from Foscore Development Center (FDC-K).
5. Training Locations: Training sessions are conducted at Foscore Development Center (FDC-K) centers. We also offer options for in-house and online training, customized to the client's schedule.
6. Flexible Duration: Course durations are adaptable, and content can be adjusted to fit the required number of days.
7. Onsite Training Inclusions: The course fee for onsite training covers facilitation, training materials, two coffee breaks, a buffet lunch, and a Certificate of Successful Completion. Participants are responsible for their travel expenses, airport transfers, visa applications, dinners, health/accident insurance, and personal expenses.
8. Additional Services: Accommodation, pickup services, freight booking, and visa processing arrangements are available upon request at discounted rates.
9. Equipment: Tablets and laptops can be provided to participants at an additional cost.
10. Post-Training Support: We offer one year of free consultation and coaching after the course.
11. Group Discounts: Register as a group of more than two and enjoy a discount ranging from 10% to 50%.
12. Payment Terms: Payment should be made before the commencement of the training or as mutually agreed upon, to the Foscore Development Center account. This ensures better preparation for your training.
13. Contact Us: For any inquiries, please reach out to us at training@fdc-k.org or call us at +254712260031.
14. Website: Visit our website at www.fdc-k.org for more information.