Target Audience
Government, Academic, Researchers, and NGO
staff including data scientists, analysists, monitoring and evaluation experts,
Lecturers, project managers, Nutritionists, Social workers, health workers, Students
pursuing Certificates, Diploma, Bachelors, Masters, PhD, and Post Doc),
environmental scientists, public health officers and facilitators.
Why is this program
1. Lack of Practical Application: Although many training institution’s programmes include statistics courses, their emphasis is frequently on theoretical issues. This program prioritizes the pragmatic utilization of statistical techniques in real-life situations, a component that is frequently absent in traditional courses.
2. Limited Exposure to Tools: Students frequently lack exposure to a diverse array of data analysis tools. This program will introduce students with a diverse range of tools such as SPSS, STATA, SAS, R, Python, Epi INFO, NVIVO, CSPro, PowerBI, and Tableau, equipping them with an extensive set of tools for data analysis.
3. Need for Specialized Skills: Proficiency in data digitalization, modeling, econometrics, and data visualization is essential due to the growing complication of research. Standard statistics courses usually do not provide comprehensive coverage of these skills.
4. Data Reliability and Validation: Researchers must increasingly comprehend and utilize procedures for data reliability and validation, such as confirmatory data analysis, factor analysis, item response model, and classical theory testing. These methodologies guarantee the accuracy and reliability of study results and are an essential component of this program.
5. Challenges in Communicating Research Findings: A significant number of students face difficulties in properly conveying their study findings as a result of inadequate comprehension of statistical principles. The objective of this lessons is to enhance students’ proficiency in this field, empowering them to deliver their findings with confidence and precision.
6. Risk of Unqualified Assistance: Insufficient knowledge of statistics may lead students to seek assistance from unqualified sources, thereby jeopardizing the credibility of their research. This lessons provides students with the necessary knowledge and skills to conduct high-quality research autonomously.
7. Information Availability and Sharing: Finally, there is a need for a platform that facilitates the exchange of information among students and enables them to acquire knowledge from relevant matter experts. The objective of this program is to establish a platform that connects academic knowledge with practical skills.
Training Objectives
1. Understand the importance of data analysis in various disciplines and get an overview of the tools and techniques used.
2. To understand exploratory and confirmatory data analysis.
3. Gain hands-on experience in performing data analysis using various tools.
4. Understand the strengths and weaknesses of each tool.
5. Learn how to analyze qualitative data using Epi INFO and NVIVO software (your preferred software).
6. Understand the difference between qualitative and quantitative data analysis.
7. Understand the basics of modelling and econometrics and learn how to apply these concepts in real-world scenarios.
8. Learn how to visualize data using a tool of your preference; PowerBI and Tableau and other tools.
9. Understand the importance of data visualization in communicating results.
10. Learn about confirmatory data analysis through factor analysis, item response model, and classical theory tests.
11. Understand the importance of data reliability and validation in research.
12. Apply the skills learned in the course to a real-world data analysis project. This will help consolidate the learning and provide practical experience.
Training Methodology
The training will use exploratory learning, small group discussions, plenary brainstorming, facilitator’s input, case studies and video documentaries. The capacity building program will be tailored to the specific needs of the students, depending on their discipline of study and research interests. The training is available both remotely, hybrid and physical depending on trainees’
preferences.
Training Dates
This course is flexible
and available upon request. Get in touch with us today to share the next
scheduled training program for the actual dates.
Research & Data Analysis
Our research and data analysis capacity building services include:
- Concept and Protocol Development
- R Programming Training for Research
- Unlocking the Power of Bootstrap: Analyzing Small Sample Sizes with Confidence
- Scientific Writing and Publishing
- Designing Research for Impact: Aligning Statistical Methods and Research Design Approaches
- From Theory to Practice: Building Econometric Models with Classical Test Theory
- Demystifying Item Response Theory: Advanced Techniques for Insightful Analysis
- Thorough literature evaluation with meta-analysis
- Tool digitization using Survey CTO, ODK, Kobo Collect, CommCare, CSPro
- Data Collection
- Data Analysis with STATA, SAS, Python, SPSS, and interpretation
- Data Visualization with PowerBI, Tableau, R GGPlot
- Machine Learning
- Research, Monitoring, and Evaluation
- Advanced Referencing and Citation Techniques (EndNote, Zotero, Mendeley, RefWorks, Citavi, JabRef, Papers & EasyBib)