Data Literacy for Non-Analysts in Research Teams Training Course

Data Literacy for Non-Analysts in Research Teams Training Course

Introduction

In today’s data-driven research environments, data literacy is no longer a skill reserved for analysts and data scientists. Non-analyst professionals—from project managers and administrators to subject-matter experts—must understand how to interpret, manage, and communicate data effectively. Data Literacy for Non-Analysts in Research Teams Training Course empowers participants with essential data skills, enabling them to contribute meaningfully to evidence-based decisions, data storytelling, and collaborative analysis within interdisciplinary research teams.

This hands-on, interactive training is tailored for professionals who work closely with data but lack formal training in data analysis. By leveraging real-world research case studies and user-friendly data tools, the course demystifies concepts like data quality, visualization, data ethics, and interpretation. Participants will gain confidence in understanding datasets, identifying patterns, questioning findings, and communicating insights with clarity and purpose.

Course Objectives

1. Understand foundational data literacy concepts in a research context.
2. Identify relevant datasets and assess data quality for non-technical users.
3. Develop skills to interpret basic data visualizations and statistical summaries.
4. Use data storytelling techniques to enhance communication in research teams.
5. Recognize common data pitfalls and misinterpretations.
6. Explore data ethics, privacy, and compliance in research projects.
7. Collaborate effectively with analysts and technical experts using data-informed dialogue.
8. Apply Excel and online tools for non-complex data analysis and reporting.
9. Understand the role of metadata and documentation in research datasets.
10. Evaluate the impact of data literacy on research outcomes and credibility.
11. Translate research findings into actionable recommendations using data.
12. Engage in critical thinking and questioning when presented with data.
13. Empower non-analysts to take a proactive role in data governance and usage.

Target Audience

1. Research project managers
2. Public health workers
3. Academic researchers without data backgrounds
4. Policy analysts
5. NGO staff involved in research programs
6. Program coordinators and administrators
7. Nonprofit communication officers
8. Undergraduate and graduate students in research roles

Course Duration: 5 days

Course Modules

Module 1: Introduction to Data Literacy in Research

• What is data literacy?
• Importance of data understanding for non-analysts
• Components of a data-literate research team
• Common myths about working with data
• Key terminology explained in simple terms
• Case Study: Miscommunication in a Public Health Study

Module 2: Understanding Research Data

• Types of data: qualitative vs. quantitative
• Identifying relevant and reliable datasets
• Reading basic dataset structures
• Introduction to metadata and documentation
• Recognizing data limitations
• Case Study: Educational Research Dataset Evaluation

Module 3: Basic Data Analysis for Non-Analysts

• Introduction to descriptive statistics (mean, median, mode)
• Exploring frequency tables and cross-tabulations
• Using Excel for simple calculations
• Recognizing outliers and anomalies
• Making sense of numerical findings
• Case Study: Survey Results in Community Research

Module 4: Visualizing Data for Insight

• Introduction to charts: bar, pie, line, scatter plots
• Choosing the right visualization
• Data dashboards for quick understanding
• Identifying misleading visuals
• Creating visuals using Excel and Canva
• Case Study: Presenting Research Findings to Stakeholders

Module 5: Data Ethics and Compliance

• Principles of ethical data usage
• Data privacy laws and research protocols
• Informed consent and data usage
• Understanding bias in data collection
• Responsible sharing and storage of data
• Case Study: Data Breach in a Clinical Research Project

Module 6: Collaborating with Analysts

• Understanding analyst roles and tools
• Framing research questions for data analysis
• How to read and comment on analysis reports
• Providing feedback without technical jargon
• Building mutual respect in cross-functional teams
• Case Study: Communication Gap in Environmental Study

Module 7: Communicating with Data

• Data storytelling frameworks (e.g., What-So What-Now What)
• Tailoring data stories to your audience
• Using analogies to explain data insights
• Avoiding technical overload in presentations
• Enhancing message retention with visuals and narratives
• Case Study: Convincing Funders with Data Stories

Module 8: Applying Data Literacy in Research Workflows

• Integrating data literacy into everyday tasks
• Building data review habits
• Asking the right questions during meetings
• Using checklists and templates for consistency
• Advocating for better data practices in teams
• Case Study: Embedding Data Review in Proposal Writing

Training Methodology

• Interactive workshops with practical exercises
• Hands-on tool demonstrations using Excel and Canva
• Real-world case studies and group discussions
• Downloadable templates and checklists
• Knowledge checks and feedback loops

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.