Clarity in Analytics

Struggling to make sense of your data or finish your dissertation? You’re not alone. Many postgraduate students hit a wall during the analysis stage, often for fixable reasons. This post unpacks the five most common causes behind stalled research and delayed submissions, and how to overcome them with clarity and confidence.

Explore the vital role of various types of data analysis in addressing drug-resistant tuberculosis in Southern Africa. This blog breaks down descriptive, diagnostic, predictive, prescriptive, and exploratory analyses through a real-world healthcare study, illustrating how data drives insights and solutions for complex public health challenges.

Learn why asking the right questions is the foundation of effective data analysis. Explore real-world case studies where wrong questions led to costly mistakes.

Defining the problem is the most important step in data analysis. A clear problem definition helps researchers, postgraduate students, and analysts avoid wasted effort, refine their focus, and ensure their findings address real issues. Learn how to distinguish perceived vs real problems, broad vs specific problems, and why revisiting the problem throughout your analysis leads to better results.